1996 temporal and spatial study of thunderstorm rainfall
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University of WollongongResearch Online
University of Wollongong Thesis Collection University of Wollongong Thesis Collections
1996
Temporal and spatial study of thunderstorm rainfallin the Greater Sydney regionAli Akbar RasulyUniversity of Wollongong
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Recommended CitationRasuly, Ali Akbar, Temporal and spatial study of thunderstorm rainfall in the Greater Sydney region, Doctor of Philosophy thesis,School of Geosciences, University of Wollongong, 1996. http://ro.uow.edu.au/theses/1986
TEMPORAL AND SPATIAL STUDY OF
THUNDERSTORM RAINFALL
IN THE GREATER SYDNEY REGION
A thesis submitted in fulfilment of the requirements
for the award of the degree
UNIVERSITY O*
DOCTOR OF PHILOSOPHY
from
UNIVERSITY OF WOLLONGONG
by
ALIAKBAR RASULY
B.Sc. & M.Sc. (IRAN, TABRIZ University)
SCHOOL OF GEOSCIENCES
1996
CERTIFICATION
The work presented herein has not been submitted to any
other university or institution for a higher degree and,
unless acknowledged, is m y own original work.
A. A. Rasuly
February 1996
i
ABSTRACT
Thunderstorm rainfall is considered as a very vital climatic factor because of its significant effects and often disastrous consequences upon people and the natural environment in the Greater Sydney Region. Thus, this study investigates the following aspects of thunderstorm rainfall climatology of the region between 1960 to 1993.
In detail, it was found that thunderstorm rainfalls in Sydney have marked diurnal and seasonal variations. They are most frequent in the spring and summer and during the late afternoon and early evening. Thunderstorms occur primarily over the coastal areas and mountains, and less frequently over the lowland interior of the Sydney basin. Environmental factors, such as the local climatic factors and physiographic parameters may control thunderstorm occurrence and its associated rainfall distribution. More detailed associations, possible causal relationships, using stepwise regression indicate that thunderstorm rainfall frequency could partially be affected by air and sea temperatures, and air humidity.
Accordingly, specific attention was paid to the patterns of the spatial variation of thunderstorm rainfall during the warm months (October to March) over a long time-span (34 years), using data from 191 rainfall stations. Mathematically, the g a m m a functions (beta and alpha values) describe and summarise the probability distribution of daily thunderstorm rainfall across the Sydney region. The findings reveal the interplay of topographic, coastal and urban effects in controlling the amount of thunderstorm rainfall in both spring and summer.
A "climatologically oriented GIS" (including a Digital Elevation Model (DEM), a proximity map, and a landuse model) together with regression procedures were used to assess the relative importance of physiographic and environmental variables for six of the largest thunderstorm rainfall events. Three patterns emerged. The first is an increase in thunderstorm rainfall occurring toward the coast. The second is an increase in thunderstorm rainfall as elevation increases. Finally, the more compact the urban residential and commercialised areas the greater the amount of thunderstorm rainfall. These variables account for 70 per cent of thunderstorm rainfall variations throughout the Sydney region.
ii
ACKNOWLEDGMENTS
I would like express my very special gratitude to Associate Professor Edward Bryant, m y supervisor, who gave m e encouragement and support throughout the study with ideas, literature, computer programs, proof reading and much more assistance. I am as well grateful to staff and academic members of the School of Geosciences, University of Wollongong for their suggestions and support throughout m y period of study. Very special thanks should be given to Professor A. Chivas, Professor M . Wilson, Associate Professor B. Young, Associate Professor G. Nanson, Associate Professor C. Woodroffe, Dr. A. Young, Dr. A. O'Neill, Dr. L. Brown, Dr. L. Head, Dr. J. Formby, Dr. G. Waitt, Dr. R. Wray, Mr. D. Price, Mr. G. Black and Ms. J. Shaw, very kind people, who gave m e so much encouragement and provided much more valuable materials during m y study. I also wish to thank Mr. J. Marthick for the use of his computing skills, particularly in GIS and Mr. R. Miller and Mr. D. Martin are thanked for advice on cartography. M y fellow postgrads were all very helpful and understanding. Thanks must be given to all these friendly people.
Grateful acknowledgment is made also to the people - at the Bureau of Meteorology Sydney Regional Office; the National Climate Centre in Melbourne; Sydney Water; the Australian Oceanographic Data Centre; the Australian Surveying and Land Information Group; and Infomaster Australia (SPANS GIS) - who kindly provided sources of data for this research.
I am also grateful to my parents, relatives, friends and academic members of the Department of Geography in Tabriz University, who consistently encouraged m e to finish this study in many letters. Without doubt, I am indebted to m y wife and children's infinite understanding, my supporters who shared probably the entire range of emotional states with me in producing this thesis. Appreciation is offered to all.
Finally, I would like to appreciate The Ministry of Culture and Higher Education of Islamic Republic of IRAN, for awarding m e a scholarship and providing financial support to make this thesis possible. Yet, I deeply believe that the greatest acknowledgment is for God's support and guidance.
iii
TABLE OF CONTENTS
PAGE
A B S T R A C T i A C K N O W L E D G M E N T S ii T A B L E O F C O N T E N T S iii LIST O F T A B L E S vii LIST O F FIGURES « LIST O F PLATES xi
CHAPTER 1 INTRODUCTION
1.1 Thunderstorm Rainfall in the Sydney Region 1 1.2 Topo-Climatic Characteristics of the Sydney Region 3
1.2.1 Topography of the Region 3 1.2.2 Climate of the Study Area 3
1.2.2.1 Rainfall Characteristics 5 1.2.2.2 Temperature Patterns 8
1.3 Objectives of This Study 9 1.4 Research Significance 10 1.5 Data Management and Modelling Techniques Applied 12 1.6 Thesis Outline by Chapters 15
CHAPTER 2 LITERATURE REVIEW ON T H U N D E R S T O R M RAINFALL
2.1 Introduction 17 2.2 Thunderstorm Characteristics 17
2.2.1 Life-Cycle of a Single Thunderstorm 18 2.2.2 Complex Thunderstorm Systems 19
2.3 Synoptic Weather Patterns Creating Thunderstorms 22 2.4 Climatic Variables and Thunderstorms 24
2.4.1 Air Temperature 24 2.4.2 Sea-Surface Temperature 25 2.4.3 El Nino / Southern Oscillation 28
2.5 Physiographic Parameters and Thunderstorm Rainfall 28 2.5.1 Topography and Thunderstorm Rainfall 29 2.5.2 Effects of Proximity to the Sea upon Thunderstorm Rainfall 33 2.5.3 Impacts of Urban Areas on Thunderstorm Rainfall Distribution 35
2.6 Distribution of Thunderstorms in Australia 38 2.7 Synoptic Patterns Associated with Thunderstorm Activity in Australia 40
2.7.1 Weather Systems and Thunderstorm Activity in N S W 44
iv
2.7.2 Thunderstorm Development in the Sydney Region 48 2.8 Sydney's Physiographic Parameters and Thunderstorm Rainfall 57 2.9 Conclusions 64
CHAPTER 3 TEMPORAL DISTRIBUTION OF THUNDERSTORM RAINFALL IN T H E S Y D N E Y REGION
3.1 Introduction 67 3.2 Data Used 67 3.3 Methods Applied 68 3.4 Yearly Distribution of Thunderstorm Rainfall 71 3.5 Seasonal and Monthly Distributions 76 3.6 Diurnal Variation 79 3.7 Discussion 81
3.7.1 The Role of Synoptic Weather Patterns 82 3.7.2 The Effect of Climatic Factors 83 3.7.3 The Impact of Physiographic Parameters 84
3.8 Summary and Conclusion 85
CHAPTER 4 THUNDERSTORM RAINFALL A N D CLIMATIC VARIABLES
4.1 Introduction 86 4.2 Data Sources and Analysis Techniques 86 4.3 Description of Variables 87
4.3.1 Air Temperature 88 4.3.2 Sea Surface Temperature 89 4.3.3 Air Humidity 91
4.4 Correlations Matrices of Variables 92 4.5 Multiple Associations Between Variables 95 4.6 Discussion 97
4.6.1 Effects of Sea-surface Temperature 97 4.6.2 Associations Between Air Temperature and Thunderstorms 98 4.6.3 The Role of Air Humidity 100
4.7 Summary and Conclusion 102
CHAPTER 5 A REVIEW ON GIS TECHNIQUES
5.1 Introduction 103 5.2 What is a GIS? 103 5.3 Purpose of GIS 104 5.4 H o w GIS Operates 106
5.4.1 Data Structures in GIS 107 5.4.2 Functionality of Data in GIS 108
5.5 Implications of GIS Techniques in Climatology 110
v
5.6 Application of the GIS in Resolving Problems in Rainfall Analysis 112 5.7 Data Sources on GIS System 115
5.8 Methods Used in a SPANS GIS n 5
5.8.1 Data Input 116
5.8.2 Model Building I17
5.8.3 Model Analysing I18
5.9 GIS Potential Errors n 8
5.10 Summary and Conclusion 119
CHAPTER 6 THE SPATIAL VARIATION AND DISTRIBUTION OF THUNDERSTORM RAINFALL
6.1 Introduction 121
6.2 Data Selection 121
6.3 Techniques Used 126
6.4 Thunderstorm Rainfall Selection Criteria I28
6.5 Spatial Variability of Thunderstorm Rainfall 131 6.6 Spatial Distribution of Thunderstorm Rainfall I35
6.6.1 Average Event Values I3" 6.6.2 The Biggest Events I39
6.7 Discussion *49 6.8 Summary and Conclusion I52
CHAPTER 7 RELATIONSHIPS BETWEEN THUNDERSTORM RAINFALL A N D PHYSIOGRAPHIC PARAMETERS
7.1 Introduction I54
7.2 Data Used 154
7.3 Techniques Employed I57
7.3.1 GIS Techniques Applied I57
7.3.1.1 Landuse Map of the Sydney Region 161 7.3.1.2 Advanced SPANS GIS Functions Used 168
7.3.2 Statistical Techniques Used 172 7.4 Topography and Rainfall from Thunderstorms 172
7.4.1 Description of Major Topographic Units 172 7.4.2 Association Between Elevation and Thunderstorm Rainfall 174 7.4.3 Association Between Aspect Classes and Rainfall 176
7.5 Proximity to the Sea and Thunderstorm Rainfall Distribution 178 7.6 Landuse Patterns and Thunderstorm Rainfall 180 7.7 Overlay Modelling / Multiple Relations 183
7.7.1 GIS Overlay Modelling 184 7.7.2 Multiple Relations Among Variables 185
7.7.2.1 Stepwise Multi-Regression Technique 185 7.7.2.2 Spatial Distribution of Z Scores Over Sydney 188
7.8 Discussion 191
7.8.1 The Role of Coastal Area 7.8.2 Impact of Topographic Factors 192 7.8.3 Effect of Landuse on Rainfall Distribution 194
7.9 Summary and Conclusion 196
CHAPTER 8 CONCLUSIONS
8.1 Introduction 198 8.2 Major Conclusions of the Thesis 198 8.3 Limitations of the Study 200
8.3.1 Limitations of Data Used 200 8.3.2 Limitations of Techniques Applied 201
8.4 Advantages and Implications of the Study 201 8.4.1 Advantages of the Study 202 8.4.2 Implications of the Study 203
8.5 Suggestions for Future Studies 204 8.6 Concluding Remarks 204
REFERENCES 206
APPENDIX A LIST OF COMPUTER PROGRAMS
A. 1 Computer Program Number 1 230 A.2 Computer Program Number 2 233 A. 3 Computer Program Number 3 234 A. 4 Computer Program Number 4 236 A. 5 Computer Program Number 5 238
APPENDIX B THUNDERSTORM RAINFALL DATA
B. 1 Common Thunderstorm-days in Sydney Region 240 B.2 Monthly Thunderstorm Rainfall Data at Richmond 249 B.3 Monthly Thunderstorm Rainfall Data at Sydney R.O. 250 B.4 Monthly Thunderstorm Rainfall Data at Sydney Airport 251 B.5 List of Rainfall Stations 252
APPENDIX C SYNOPTIC WEATHER CHARTS
Synoptic charts 6.1 from 23th to 25th October, 1987 256 Synoptic charts 6.2 5th to 12th November, 1984 257 Synoptic charts 6.3 9th to 11th December, 1988 258 Synoptic charts 6.4 19th to 22th January, 1991 259 Synoptic charts 6.5 7th to 11th February, 1990 260 Synoptic charts 6.6 10th to 11th March, 1975 261
vii
APPENDIX D DATA USED FOR GIS A N D STATISTICAL MODELS
Geographical Location of Rainfall Stations and their Attributes
APPENDIX E EQUATIONS Equations Used in SPANS GIS
LIST OF TABLES
TABLE PAGE
See
1.1 Examples of thunderstorm rainfall events causing flash floods
1.2 Gives examples in using thunderstorm data in the region
3.1 Represents a detailed description of the codes of present and past
weather used in thunderstorm observations
3.2 C o m m o n thunderstorm-days in the Sydney region
3.3 General geographic characteristics of the seven selected stations
3.4 Locality of the seven selected stations
3.5 Yearly variation of thunder-days frequency and thunderstorm rainfall
amounts at 7 thunder-recording stations in the Sydney region
3.6 Summary descriptive statistics for yearly thunderstorm rainfall frequency,
in the Sydney region
3.7 Summary descriptive statistics for yearly thunderstorm rainfall amounts,
in the Sydney region
3.8 Average seasonal thunderstorm rainfall for selected stations
3.9 The percentage of average thunderstorm rainfall to mean monthly
rainfall in different stations, in the Sydney region
4.1 Monthly thunderstorm rainfall frequency at Richmond
4.2 Monthly thunderstorm rainfall amount at Richmond
4.3 Monthly thunderstorm frequency at Sydney R. 0.
4.4 Monthly thunderstorm rainfall amount at Sydney R. 0.
4.5 Monthly thunderstorm rainfall frequency at Sydney A.
4.6 Monthly thunderstorm rainfall amount at Sydney A.
4.7 Description of thunderstorm data
4.8 Means and extremes of temperature at three selected stations
4.9 Monthly and yearly sea-surface temperature data at Port Hacking
4.10 Simple statistics of the relative humidity in the Sydney region
4.11 The correlation matrix between dependent variables
4.12 Correlation matrix for independent variables
4.13 Linear regression coefficients of dependent variables by independent
variables
4.14(a) Results of stepwise multiple regression analysis of thunderstorm
rainfall frequency at the Sydney Airport station
4.14(b) Results of stepwise multiple regression analysis of thunderstorm rainfall
frequency at Richmond station
6.1 Difference between two sets of stations (the Sydney Water and the Bureau
of Meteorology) according to their rainfall means
6.2 List of stations and the periods from which data were used See
6.3 Thunderstorm rainfall values extracted from the intersection of two
See
See
See
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10
14
68
Appendices
69
71
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Appendices
Appendices
Appendices
Appendices
Appendices
Appendices
88
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123
Appendices
ix
populations using probability excellence graphs
6.4 General descriptions for the 6 biggest thunderstorm rainfall events
in the region
7.1 Origin of the data that used in Chapter 7
7.2 Geographical location of rainfall stations and their attributes
7.3 Limits of the study area / database
7.4 Aspect classes derived from the D E M model, using S P A N S GIS
7.5 Description of landuse types in the Sydney region.
7.6 Equations which were written in S P A N S environment
7.7 Area cross tabulation results between the topography map of the region
and thunderstorm rainfall map.
7.8 The areal distribution of thunderstorm rainfall by topographic classes
7.9 A linear regression analysis between thunderstorm rainfall amount and
elevation of rainfall stations located in the region
7.10 Area cross tabulation results between the aspect map of the region and
thunderstorm rainfall map
7.11 A multiple regression analysis between aspect classes and thunderstorm
rainfall amount
7.12 Area cross tabulation results between the proximity to sea m a p of the
region and thunderstorm rainfall map
7.13 The areal distribution of thunderstorm rainfall by proximity classes
7.14 Correlation coefficients between the proximity to the sea and
thunderstorm rainfall
7.15 Area cross tabulation results between the landuse map of the region and
thunderstorm rainfall map
7.16 The areal distribution of thunderstorm rainfall by landuse classes
7.17 The result of a t-test for rainfall distribution in different landuse classes
7.18 A multiple regression analysis between landuse classes and
thunderstorm rainfall
7.19 Interrelations matrix among physiographic parameters and
thunderstorm rainfall
7.20 Presents the result of stepwise multiple regression analysis for the
average of the biggest thunderstorm rainfall amounts
130
139
157
See Appendices
158
161
162
See Appendices
174
175
176
177
178
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186
187
LIST OF FIGURES
x
FIGURE PAGE
1.1 Study area - the Sydney region, NSW, Australia 2
1.2 Topographic and Location M a p of Sydney region 4
1.3 Illustrates inter-annual precipitation variation at Observatory Hill, Sydney 5
1.4 Median annual rainfall of T H E Sydney region 6
1.5 Average monthly rainfall at three stations in the Sydney region 7
1.6 Average monthly maximum and minimum temperature at Sydney 8
1.7 Percentiles of maximum temperature at Observatory Hill 9
2.1 Schematic representation of a thunderstorm cell 18
2.2 Three stages in the development of a thunderstorm 19
2.3 Schematic visual appearance of a supercell thunderstorm 21
2.4 Average annual thunder-days in Australia 39
2.5 Basic elements in (a) the pattern of pressure distribution and of
associated (b) airmasses over Australia in summer 40
2.6 Represent a pre-frontal trough (a), and a line storm associated with an
eastward moving trough (b) over south-eastern Australia 41
2.7 Shows a sample of cut-off low in the region 45
2.8 Schematic of the life cycle of the precipitation area of a M C C s 46
2.9 Presentation of the anomaly maps using the Terminal Area Severe
Turbulence (TAST) radar data in the Greater Sydney region 50
2.10 Diurnal distribution of thunderstorm occurrence for the different time
periods (local time) in the Sydney region 51
2.11 Presents examples of six meso-scale synoptic weather systems causing
thunderstorm activity in the Sydney region 53
2.12 Selected M S L P charts illustrating the six synoptic classes over Sydney 54
2.13 Thunderstorm density model based on new radar echoes in Sydney 55
2.14 Lightning density for single thunderstorm events based on data from
the N S W lightning detection network 56
2.15 The intensity of annual energy use in the Sydney region 61
2.16 Spatial distribution of nitrogen oxides emissions from all sources
in the Sydney 62
3.1 A dendrogram shows the result of the N N A technique 71
3.2 Yearly variation of thunder-days frequency and thunderstorm rainfall
at Sydney Regional Office 74
3.3 Yearly variation of thunder-days frequency and thunderstorm rainfall
at Richmond 75
3.4 Normalised Residual Mass curves of annual thunderstorm rainfall
in the Sydney region 75
3.5 Seasonal distribution of thunderstorm rainfall in different stations 76
XI
3.6 Monthly distribution of thunderstorm rainfall in the Sydney region for
different stations 78
3.7 Diurnal variation of thunderstorm rainfall frequency for three thunder
seasons at Katoomba 80
3.8 Diurnal variation of thunderstorm rainfall frequency for three thunder
seasons at Richmond 80
3.9 Diurnal variation of thunderstorm rainfall frequency for three thunder
seasons at Sydney Regional Office 81
4.1 Average monthly variation of the sea surface temperature 90
4.2 Monthly distribution of the mean relative humidity at three stations
in the Sydney region 92
5.1 Schematically represents different data structures used in a GIS 108
6.1 Relation between correlation coefficient (r) and interstation-distance 124
6.2 Sydney region - rainfall stations networks 125
6.3 The g a m m a density function for a and /? values 127
6.4 Probability of exceedence diagrams for 7 selected thunder-recording stations 129
6.5 Geographical distribution of alpha value, Spring (Oct to Dec) 132
6.6 Geographical distribution of beta value, Spring (Oct to Dec) 133
6.7 Geographical distribution of alpha value, Summer (Jan to Mar) 134
6.8 Geographical distribution of beta value, Summer (Jan to Mar) 135
6.9 Average thunderstorm rainfall per event (Oct to Dec) 137
6.10 Average thunderstorm rainfall per event (Jan to Mar) 138
6.11 Thunderstorm rain - Sydney region (23-25 October 1987) 140
6.12 Thunderstorm rain - Sydney region (5-12 November 1984) 141
6.13 Thunderstorm rain - Sydney region (9-11 December 1988) 143
6.14 Thunderstorm rain - Sydney region (19-22 January 1991) 144
6.15 Thunderstorm rain - Sydney region (7-11 February 1990) 147
6.16 Thunderstorm rain - Sydney region (10 and 11 March 1975) 148
7.1 Sydney region - thunderstorm rainfall, the average of the 6 biggest
daily thunderstorm rainfall events 156
7.2 Proximity map from average coast-line 159
7.3 Aspect map of the Sydney region 160
7.4 Landuse map of the Sydney region 163
7.5(a-d) Physiographic parameters of the Sydney region subject to the highest
daily thunderstorm rainfall amounts 171
7.6 Major topographic units of the Sydney region 173
7.7 The distribution of thunderstorm rainfall in the Sydney region based
upon aspect classes 177
7.8 Distribution of thunderstorm rainfall in the Sydney region based upon
landuse classes 182
7.9 Spatial Distribution of Z scores over Sydney region 190
xii
LIST OF PLATES
PLATE PAGE
1.1 Gives examples of some extensive and serious damage caused by
thunderstorm rainfall in the Sydney region 11
2.1 Shows a cold front off the South Coast of N e w South Wales 42
2.2 Displays thunderstorm development over the Sydney region 49
2.3 Shows smog over central Sydney 63
7.1 Closeup view of heavy commercial landuse showing the part of C B D 164
7.2 Closeup view of heavy industrial landuse 165
7.3 View of compact residential landuse in the Sydney region 165
7.4 View of light-moderate residential landuse 166
7.5 View of normal rural /semi-urban area 167
7.6 Shows example of rural / open areas 167
7.7 Closeup view of compact vegetated land cover in the Sydney region 168
CHAPTER ONE Introduction 1
CHAPTER 1
INTRODUCTION
1.1 Thunderstorm Rainfall in the Sydney Region
Sydney, with its sprawling suburban area, and a population of approximately 3.5 million, is
Australia's largest city. It is located between the South Pacific Ocean and the mountain
ranges in the west. During the last three decades, meteorologists (Williams, 1984;
Colquhoun and Shepherd, 1985) and climatologists (Hobbs, 1972; Sumner, 1983b and
Linacre, 1992) have indicated that thunderstorm activity is a characteristic feature of the
warm summer months in this region and that rainfall from thunderstorms is a major source
of moisture for most parts of the study area. Flash floods are usually associated with the
type of thunderstorm that produces localised, but very intense rainfall, which damages
property and even results in a loss of life (Shanahan, 1968; Riley, 1980; Riley, et al., 1985;
Speer and Geerts, 1994). It seems that thunderstorm rainfalls, and occasionally their
associated flash floods, are a natural part of Sydney's climatic environment (Bryant, 1991;
Johnson et al., 1995).
Information on the variation and distribution of thunderstorm rainfall over time and within
the region is, therefore, crucially important in a variety of applications. However, in the
Sydney region, the knowledge of the temporal and spatial distribution of thunderstorm
rainfall is limited to some case studies of the specific thunderstorm rainfall events which
have been considered over a short period of time (Armstrong and Colquhoun, 1976;
Morgan, 1979a; Nanson and Hean, 1985; Shepherd and Colquhoun, 1985). Consequently,
this study's investigation will involve defining some important aspects of the thunderstorm
rainfall climatology of the region, which has not received a similar level of attention. This
will be done by analysing the available data over a longer period - from 1960 to 1993 -
using appropriate sets of techniques.
There are two main aims of this thesis. The first is to focus attention on the patterns of the
temporal and spatial variation and distribution of thunderstorm rainfalls during the warm
months (October to March) over the time-span of 34 years. The second is to examine the
thunderstorm rainfall patterns in relation to some of the primary climatic variables (air and
sea temperatures, for instance) as well as physiographic parameters such as topography,
proximity to the ocean, and the landuse of the Sydney region.
CHAPTER ONE Introduction 2
In the following section, the study area and the important topo-climatic characteristics of
the Sydney region are described. Section 3 outlines the aims of this study. Section 4 offers
some important reasons indicating that the anticipated information from the current
research will be of value for future investigations in the fields of meteorology and
climatology. Section 5 gives a framework for all available data and the techniques which
will be used in this study. Finally, in section 6, the whole thesis is outlined.
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CHAPTER ONE Introduction 3
1.2 Topo-CIimatic Characteristics of the Sydney Region
The Sydney region located on the south-east coast of Australia, in New South Wales
( N S W ) includes the Sydney Metropolitan area, which is expanding rapidly inland and
contains highly industrialised pockets. The study area, as part of the Greater Sydney
Region, is bounded in the north by 33° 30* latitude, extending to 150° 15* longitude in the
west, and to the south-east of Wollongong as far as Bowral at 34° 30 latitude south.
Figure 1.1 indicates the geographical location of the study area within Australia as well as
an enlargement of the Sydney region, including the location of all selected thunder-
recording stations (used in Chapter 3) in the area.
1.2.1 Topography of the Region
The Sydney region is bowl-shaped with a low plain in the middle of which is effectively
walled in on three sides by bills and mountains. In the centre of the region there is the
Cumberland Plain opening to the Pacific Ocean from the east. To the north of the plain, the
rise is about 450 m to the top of a ridge lying eastward from the Great Dividing Range
towards the coast. To the south, the rise in elevation on average is over 350 m. In the
south-east of the study area the coastal range rises from 150 m to 500 m, just in the North
west of Wollongong. To the north of Sydney the land rises from about 150 m near Broken
Bay to 450 m on the northern boundary. However, westward, the region rises sharply to
over 1200 m at the top of the Blue Mountains, the eastern section of the Central
Tablelands. The elevation map of the Sydney region (Figure 1.2) illustrates the topographic
features of the region, including the location of the main suburbs. For a more detailed
topographic map of the region, refer to Figure 7.6.
1.2.2 Climate of the Study Area
The Sydney region enjoys a temperate climate and generally the broad-scale wind pattern is
westerly in the winter, and easterly in the summer. This climate can be classified as being
temperate with cool to cold winters and warm to hot summers (Bureau of Meteorology,
1991a). Generally the climate of this region arises from a complex interaction of broad
scale, regional and local controls. O n the broad scale the region is under the influence of
mainly drier westerly airstreams in the winter, and predominantly moist, easterly air
streams in the summer months (Linacre and Hobbs, 1977).
On the regional scale, the major influences are physiographic features (for example,
topography) in and around the region, sea surface temperature off the coast and the
orientation of the coastline (Bureau of Meteorology, 1979). In a such region, local
variation in climate may be caused mainly by the topography (exposure to wind direction,
elevation), proximity to the sea and other local factors (Cox, 1983). Within this region,
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CHAPTER ONE Introduction 5
which extends to about 100 k m inland with some parts reaching elevations of over 1200 m,
most climatic elements vary significantly. Therefore, it is not surprising that Sydney's
climate has been considered as a complex of many local climates (Paine et al., 1988).
1.2.2.1 Rainfall Characteristics
In Sydney's climate, the primary rain-producing mechanisms are: major storms, cold fronts
and thunderstorms. Major storms, which are mainly dependent on the deep low pressure
systems in the Tasman Sea, can produce strong winds and heavy rainfall along the N S W
coast (Bureau of Meteorology, 1991a). These systems can be classified by their origin into
several types which occur at different times of the year. In contrast, cold fronts produce
comparatively little rain in the Sydney region, especially in summer, when the flow behind a
cold front is most often from the south (Colquhoun et al., 1985). Little moisture is
provided unless a following upper-level low pressure trough provides additional instability.
Generally, Wilson and Ryan (1987) found that Sydney appears to be a location where only
a small amount of the total precipitation is due to mechanisms associated with fronts.
Thunderstorm activity is also acknowledged as an important rain-producing system in the
region, particularly in late spring and the summer months. Thunderstorms can produce
heavy rainfalls resulting in a considerable contribution to the annual precipitation over the
Sydney region.
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2000
- 1500
-r- -+- -+- -+- -h -h
• 1Q00
- 500
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990
YEAR
Figure 1.3 Illustrates inter-annual precipitation variation at Observatory Hill, Sydney.
Although Sydney's annual average precipitation is estimated to be about 1200 millimetres,
it tends to be erratic and unreliable. This means that annual rainfall over the Sydney region
is extremely variable spatially and temporally, and this may reflect the occurrence of the
CHAPTER ONE Introduction 6
large thunderstorm events in the warmer seasons. To gain an appreciation of longer term
variability of rainfall, examination of the historical record is useful. The station with the
longest record of rainfall in the region was the Observatory Hill station (it has been closed).
Figure 1.3 shows the annual totals from 1900 to 1990. Considerable variability is evident,
the highest fall recorded being 2194 m m (1950) and the lowest recorded, 625 m m (1976).
The totals during the period from about 1990 to 1950 exhibit less inter-annual fluctuation
than after 1950 and more recent times.
It is likely that the greatest spatial variation in rainfall can also be associated with changes
in topography and distance from the sea, both of which can cause considerable variations in
the annual precipitation in the Sydney region (Figure 1.4).
THE SYDNEY REGION
Figure 1.4 Median annual rainfall (in m m ) of the Sydney region. The isohyets are derived from all available data from rainfall stations with at least 20 (1832-1986) years of record. The number of years of record varies for different stations in the region (Bureau of Meteorology).
It is evident from the annual rainfall distribution map, for instance, that an area which is
located west of Wollongong, over the Illawarra Plateau, receives more than 1600 m m rain
annually. While the highest rainfall in Sydney of more than 1300 m m occurs over the more
elevated parts of the northern suburbs that form the Hornsby Plateau. The lowest rainfall
occurs in low-lying pockets of the western Cumberland Plain. For example, both Windsor
and Campbelltown, which are located in lowland areas, receive less than 750 millimetres
N
S
TASMAN
SEA
CHAPTER ONE Introduction 7
per annum. The Blue Mountains, located in the west of the study area, in contrast to the
low relief of the coastal plain, receives much more rainfall (more than 1300 m m ) . This
major topographic unit, with more than 1200 m elevation, receives some of its intence
rainfalls in the summer months. In addition, in the south-east of the Sydney region, the
Ulawarra Escarpment appears to be partly responsible for inducing a minor rain shadow
effect over the central part of the Sydney region, as well as increasing local rainfall quite
significantly (Bryant, 1982).
It has been suggested that rainfall in the Sydney region falls uniformly throughout the year
when compared to other parts of Australia (Bureau of Meteorology, 1991a). As it shown
in Figure 1.5, there are, however, considerable variations in monthly average rainfall
amounts over the year for the three selected stations located in different parts of the
region. These stations are: the Sydney Regional Office located in the east of the region
near the coast; Richmond station located in the North-west of the study area (inland); and
Katoomba which is located in the Blue Mountains (see Figure 1.2).
Figure 1.5 Average monthly rainfall at three stations in the Sydney region (1960-93).
Figure 1.5 gives the average monthly rainfall amounts during January to December at these
three stations from 1960 to 1993. The wettest month, the month in which the average
rainfall is highest, is different for the three stations. Generally, during the warm months
(November to March) when thunderstorms and easterly airstreams prevail, the monthly
rainfall is high. This was noted by Fitzpatrick and Armstrong (1973) w h o found that in the
Sydney region there are clearly steeper gradients of rainfall in summer, a reflection of the
influence of prevalent thunderstorm rainfall. Also, there is a secondary maximum in June
affecting the region in the cooler months, which may be attributed, in part, to the frequency
of east coast lows (Holland et al., 1987). As stated in 1968 by Gentilli, the rainfall of
CHAPTER ONE Introduction 8
Sydney can be seen as a complicated regime, with two or three major peaks during the
year, and with the driest period in late winter or early spring.
1.2.2.2 Temperature Patterns
Temperatures in the Sydney region vary widely from place to place. As the Sydney region's
location is adjacent to a water mass in the east and relatively high ranges in the west, it
escapes extremes of temperature. However, within the region the range of temperatures is
relatively high. It has been shown by Fitzpatrick and Armstrong (1973) that the variation in
temperature can be caused by differences in elevation and distance from the coast, as well
as by other factors, such as aspect and slope of a particular site and the surrounding
terrain. In Sydney, temperatures are mildest near the coast with a few extremely hot or
cold spells during the year. Inland on the plains, temperatures in excess of 35 degrees (°C)
occur regularly during the summer. Generally, the highest average temperatures occur in
the low-lying central parts of the region, while the lowest occur over the Blue Mountains
in the winter. The range of average maximum temperatures across the region is about 7 °C.
Figure 1.6 shows the variation of the average monthly temperatures (maximum and
minimum) at the three selected stations in the Sydney region throughout the year. More
details about temperature patterns of the Sydney region are given in Chapter 4, where a
close relationship amongst thunderstorm rainfall and temperature data will be considered.
35
30 f •
9
ft. 15
8
io --
5 --
\ N A.
* . -A
•Sydnry R. 0 Max
H 1 1 1- 4 \ h
Sydnry R. 0 Min
• Richmond Max
Richmond Min
A Katoomba Max
Katoomba Min
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure 1.6 Average monthly maximum and minimum temperatures (°C) at Sydney.
The variability of maximum temperatures from day to day is also greatest in the summer
and increases with distance from the coast. During the summer the difference between the
90th and 10th percentiles (which is a measure of variability) ranges from about 8 °C at
CHAPTER ONE Introduction 9
Observatory Hill, Sydney (Figure 1.7) near the coast, to 14 °C at Richmond, about 55 k m
inland on the plain (elevation = 19 m ) and at Katoomba (not shown), about 90 k m inland
on the Central Tablelands, with 1030 m elevation. Distance from the sea is particularly
prominent in respect of maximum temperatures in summer.
During the winter the variability of maximum temperatures is around 6 °C and is fairly
uniform across the region, but the difference between the maxima at the three locations
varies throughout the year. While in winter the maximum diurnal temperature is quite
definite and occurs around 3 pm, in summer it occurs between 11 a m to 2 pm, with a slight
maximum at 2 pm..
35
5 -:
-•—Percentile 10
- 0 — Percentile 50
-A—Percentile 90
Jan Feb Mar Apr May Jun Jul
Months
Aug Sep Oct Nov Dec
Figure 1.7 Percentiles of maximum temperature at Observatory Hill, Sydney (1900-1990).
1.3 Objectives of This Study
The main aim of this study is the organisation of thunderstorm rainfall data in time and
space within the Sydney region. The second aim is to find any relationships among
thunderstorm rainfall and several environmental factors (climatic and physiographic
parameters of the region). In particular this study addresses several questions:
1) Are there temporal distribution patterns for yearly, seasonal, monthly and diurnal
thunderstorm rainfall in the Sydney region?
2) Are there some possible causal relationships amongst climatic factors of the
region and thunderstorm rainfall?
3) Are there recognisable spatial variations in thunderstorm rainfall?
CHAPTER ONE Introduction 10
4) What are the average daily thunderstorm rainfall distribution patterns for spring
and summer?
5) D o the physiographic parameters of the Sydney region significantly control the
spatial distribution of the largest thunderstorm rainfall events?
1.4 Research Significance
The study of thunderstorm rainfall in the Sydney region is important for many reasons.
Although the climatology of severe thunderstorms activity in the region has been studied
by Griffiths et al. (1993), several features of thunderstorm rainfall climatology in the
Sydney region have not yet received a similar level of attention.
Firstly, the temporal distribution of thunderstorm rainfall in the Sydney region, using daily
thunderstorm rainfall data, has not been, in detail, studied over long time periods by
previous investigators. Secondly, the spatial variation and distribution of thunderstorm
rainfall, throughout the Sydney region, has not been analysed completely, for example on a
monthly or seasonal bases.
Table 1.1 Examples of thunderstorm rainfall events causing flash floods in the region.
Location Subject of Floods and Estimated
Date Damage by Thunderstorms Damage, m$
18 Jul 1965 North and South of Sydney unknown
10-11 Mar 1975 Eastern Parts of Sydney and Illawarra District >20
10 Nov 1976 Metropolitan Area* >40
29 Dec 1980 Sydney and Suburbs >50
5-9 Nov 1984 Most Parts of Sydney Region > 100
18 -19 Feb 1984 Illawarra District (Dapto) >6
25 Oct 1987 Illawarra and Metropolitan Area unknown
3 Feb 1990 Metropolitan Area >30
18 Mar 1990 Liverpool, Ryde and City areas 313
21 Jan 1991 Northern Suburbs of Sydney 560
* The Metropolitan area refers to the entire contiguous built up area in the Sydney region,
including suburbs such as Campbelltown, but not separate urban areas such as Wollongong.
** The 'City' is a term often used loosely. Some writers use it in the same sense as 'Metropolitan
area', but here it refers to the CBD (Central Business District) and older suburbs.
In addition, previous studies have not emphasised the importance of the effects of some
climatic factors and the physiographic parameters upon thunderstorm rainfalls. Some
researchers, for example Williams (1991), view an understanding of the physical
environment as essential for an understanding of the weather. Therefore, a good
CHAPTER ONE Introduction 11
knowledge of the locations of towns, mountains and details of landscape features is very
useful when dealing with rainfall modelling. Advantages can be obtained by the studying
thunderstorm rainfalls in relation to these parameters.
(a) Roadway at Point Piper
(Eastern Suburbs, Rose Bay)
(b) Flood and erosion
at Scots College
(Eastern Suburbs, Rose Bay)
i _<y v
Plate 1.1 (a and b) Gives examples of some extensive and serious damage caused by thunderstorm rainfall in the Sydney region.
Finally, it is well known that the greatest proportion of the summer rainfall of the Sydney
area comes from thunderstorm activities, occasionally causing flash floods in the region
(Colls, 1991; Egger, 1991). Flash flooding occurs when the intense sudden rainfall from
thunderstorms cannot be absorbed or drained away quickly enough. City areas can also
3 0009 03201123 6
CHAPTER ONE Introduction 12
experience flash flooding when the rainfall is too intense for the thunderstorm water
drainage systems to cope with it (Bufill, 1989; Weeks, 1992). Such thunderstorms are
most c o m m o n in the warm months, but little is known about the relative contribution and
importance of temporal and the spatial variability and distribution of these thunderstorm
rainfalls over a long time-span. Table 1.1 summarises the examples of thunderstorm rainfall
events causing flash floods in the Sydney region (Bureau of Meteorology, 1965 to 1991).
Some researchers, such as Colquhoun and Shepherd (1985); Eagle and Geary (1985);
White (1985) and Bryant (1991) described the Sydney thunderstorms of November 1984 in
which insurance losses from flash flooding exceeded $100m. The damage cost in Sydney
from flooding events is particularly high when urban areas are involved. For example, as a
result of the flood that affected the Sydney area in August 1986, damage totalling
approximately $ 100 million occurred, and six lives were lost. In 1990 and 1991 (Spark
and Casinader, 1995), two other thunderstorms caused damage to properties totalling $
599 million. Rural flooding can result in significant crop and stock losses and increased
erosion (see Plate 1.1).
Joy (1991b) has estimated that the total annual cost of flooding to Australia is $ 380
million, and thunderstorms, it is estimated, cause more than 1 6 % of the annual average
costs of natural disasters. More recently, Ryan (1993) calculated that since 1967-91
insurance payouts were more than $ 1808 million for severe thunderstorms in Australia.
Surprisingly, existing records show that in the Sydney region insurance costs have been
more than $ 1100 million in the same period. Most of the damage has been caused by the
direct or indirect effects of flash floods, with considerable loss of lives. Urban areas,
particularly residential and commercial properties, were the most affected by these severe
storm events (detailed inBlong, 1991; Smith, 1993; and Joy, 1993).
Therefore, it is important to obtain temporal and spatial distribution models of this
thunderstorm rainfall. Such information is especially relevant, for instance, in city designing
programs, channel network, the location of rain-gauge networks (Davidson, 1981), for
disaster management, and to the insurance industry (Hobbs and Littlejohns, 1991). As a
result, this research may be considered to lie within the bounds of applied climatology
1.5 Data Management and Modelling Techniques Applied
A variety of data with different scales and origins were used to assess the distribution of
thunderstorm rainfall distribution in time and space for the Sydney region. These data
originated from the National Climate Centres; the Sydney Water Board, and the Sydney
Regional Office of the Bureau of Meteorology. Rainfall data, used to investigate rainfall
variability, either spatially or temporally, should, as far as possible, be homogenous. Like
CHAPTER ONE Introduction 13
most rainfall data collected over time in Australia, that for the Sydney region suffers from
inconsistencies and errors in measurement that weaken the absolute confidence that can be
placed in the observed data (Lavery et al., 1992; Griffiths et al., 1993). Specifically,
inhomogeneities in rainfall records include:
Changes in observing practices;
Changes in exposure of rain-gauge;
Changes in station location (both in altitude and position);
Changes in the type of gauge used; and
Missing data (Nicholls, 1995; Karl, 1993).
In addition, data used in the present study were collected by different agencies. Some
investigators note that thunderstorm reports are probably the most 'noisy' and biased of all
meteorological data (Batt et al., 1995), and that for many types of extreme events, such as
thunderstorms, the maintenance of long-term homogeneity in observations is most difficult
(Nicholls, 1995).
Few of these limitations could be assessed in detail in the present study because such
factors are poorly documented for Australian rainfall stations. As the present study is event-
based, some steps could be taken to ensure spatial data continuity for the larger
thunderstorm events. First, the raw data on thunderstorm days and associated rainfall
amounts were extracted from data tapes provided by the National Climate Centre. This
source contained all thunderstorm days from 1960 to 1993 for 30 thunderstorm recording
stations. The timing of these events were corroborated using other data sets provided by
the Sydney Regional Office of the Bureau of Meteorology. Next, all stations with less than
a seven year record and having more than three years of missing data were excluded. This
left 15 stations that record thunderstorm events.
Then, rainfall data was extracted from the more that 400 meteorological stations recording
daily rainfall in the Sydney region. This data was provided by the Bureau of Meteorology
(288) and the Sydney Water Board (112). These stations covered the period from 1960 to
1993 inclusive. To restrict these data, stations with less than a 10 year record were
excluded. In addition, stations which had not contributed sufficient thunderstorm rainfall
observations, according a criterion (at least 100 observations), were also excluded (see
Chapter 6). This left 191 rainfall stations in the data set, 134 from the Bureau of
Meteorology and 57 from the Sydney Water Board.
Similar data sets have been used successfully by numerous researchers to provide
significant insights into rainfall in the Sydney region. These studies are summarised in Table
Introduction
1.2. Further criteria for limiting the data are described in Chapter 3 for thunderstorm
observations, and in Chapter 6 for the amount of rain falling at stations during
thunderstorm events. These criteria have ensured that only the largest of thunderstorms
(1584 events for the study of temporal variation, and 347 events for spatial analysis) were
considered in this study.
Table 1.2 Examples of studies that have used thunderstorm data in the region.
The Main Aim of the Study Author(s) Period of Data Used
Spatial and temporal distribution of Williams, A. 5-9 Nov. 1985
thunderstorm rainfall
Climatology of flash-floods in the Sydney Speer, M., Geerts, B. 1957-1990
Metropolitan area
Thunderstorm distribution in the Sydney Matthews, C., Geerts, B. 1965-1989
area
Climatology of severe local storms in N S W Batt, K., Hobbs, J. 1991-1995
Following the considerations described above, data were summarised and reduced to
managable proportions by writing several specific computer programs (presented in
Appendix A ) . The process of data reduction also involved a number of other steps, some
of which, such as data transformation, editing, coding and the generation of new variables,
have been done by using a variety of commonly used computer programs. For example, the
Microsoft Excel program (Apple Macintosh and P C computers) was widely used for
manipulating, summarising and analysing data as well as for graphing purposes. Other
computer application programs such as Ms-Dos (for editing data) Clarisworks (as a data
base) were likewise used extensively in this study.
In the second stage, a major objective was to reduce the complexity of the subject to
clearly define the climatological relationships. Because of the data complexity, a set of
tools, including statistical, mathematical, and the Geographic Information Systems (GIS)
techniques were applied to the available data.
Accordingly, two statistical computer programs, the JMP (SAS Institute Inc, 1989) and
SPSS (Norusis, 1994) have been used for statistical analysis. These statistical techniques
consist of descriptive and inferential statistics (according to the nature of the variables) are
applied to find possible associations between those variables. Both the descriptive statistics
and inferential statistics are frequently used in this thesis. Detailed descriptions of these
analytical methods are ? in numerous text books.
Using the above-mentioned statistical programs, a wide range of descriptive statistics were
applied to the data sets. In most types of climatic analyses these statistics are commonly
CHAPTER ONE Introduction 15
used, firstly, to organise large data sets, and secondly to summarise such data either by
measuring the central tendency or data dispersion, by using inferential statistical techniques
to find bi-variate relationships. For example, the relationship between thunderstorm rainfall
and elevation of the study area was considered using a simple regression technique. Other
inferential statistical techniques which were used in this study are: the chi-square method,
t-tests, and some simple to multiple regression methods.
Each chapter details the data-analysis procedures used. For example, in Chapter 3 the
Nearest Neighbour Analysis ( N N A ) technique was used to establish some possible
relationships amongst thunder-recording stations in the Sydney region. The aim was to
select the best possible thunder-recording stations as they could represent thunderstorm
activity in the region. This technique was again used, in Chapter 6, to determine whether
the two networks of rainfall stations are compatible in the Sydney region.
The gamma functions (beta and alpha values) are mathematically used to find the
probability distribution of thunderstorm rainfall amounts at each rainfall station in the
Sydney region. The purpose of using g a m m a distribution is to analyse the spatial variation
of thunderstorm rainfall on a seasonal basis. Because of its importance, this technique is
described in detail in Chapter 6.
Also, in Chapter 6, GIS techniques are used to model the spatial distribution of
thunderstorm rainfall over the study area. They are then utilised to find some expected and
initial associations among rainfall patterns and physiographic parameters of the Sydney
region (see Chapter 7).
1.6 Thesis Outline by Chapters
All relevant material which has been collected using a set of appropriate methods will be
presented in seven chapters. Chapter 2 will bring together a general relevant literature
review on thunderstorm rainfall. This literature review will consider research questions
which can be related to those thunderstorm activities which cause rainfall. The strategy of
this literature review is to categorise material which will lead to the development of each
question to be tested using various methods.
In this thesis, a methodology chapter was not included because each of the chapters where
results are presented (3, 4, 6 and 7), have their o w n method section, describing a variety of
techniques which were applied for different sets of data. Since the use of GIS in rainfall
studies, at least in Australia, is a relatively new notion, sources of data and techniques in
GIS, particularly its purposes and applications in climatology, are explained in Chapter 5.
The relevance of this chapter to the overall thesis can be found specifically in chapters 6
CHAPTER ONE Introduction 16
and 7, where a set of GIS methods were applied to illustrate thunderstorm rainfall data in
space in relation to the Sydney's physiographic parameters.
Chapters 3, 4, 6 and 7 will present the various results of this research. The main aim of
chapter 3 is to characterise the average temporal distribution of thunderstorm rainfall in the
Sydney region. In this chapter the temporal distribution of thunderstorm rainfall, for
different time-scales, will be described, while in Chapter 4 the possible causal relationships
between climatic variables and thunderstorm rainfall data will be examined on a monthly-
basis. The overall goal of this chapter is, therefore, to determine the significant levels of
associations among the variables.
In Chapter 6, the spatial variation and distribution of thunderstorm rainfall patterns will be
analysed using the g a m m a distribution technique at each rainfall station. T o compare
g a m m a values with actual rainfalls, a GIS method will also be used to visualise the spatial
distribution of thunderstorm rainfall amounts over the Sydney region. Then, it will be
argued in Chapter 7 that the spatial distribution of thunderstorm rainfall over the Sydney
region is largely a function of the interplay and interaction between different physiographic
factors such as elevation, proximity to sea, and urban landuse. In order to examine the
possible associations between these parameters and rainfalls, a GIS technique and some
statistical procedures will be employed to assess the strength and significance of the
relationships between variables.
The final chapter, Chapter 8, will, discuss the results obtained and their relationship to
those research questions regarding thunderstorm rainfall amounts and distribution in the
study area. This will help in returning to the original research questions and unifying the
aims of this thesis. This concluding chapter re-states and aggregates all the information
from the preceding chapters in terms of the aims of this thesis. Then, based on the results
obtained, advantages and disadvantages of all techniques used will be outlined. The last
part of the chapter will offer some suggestions for future research.
All computer programs produced in this study to extract data or for other purposes, will be
located in the Appendix A. Data that are referenced to different parts of the thesis or
presented in summarised form in the text, will also be shown in the associated appendices
B to E, in a complete form.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 17
CHAPTER 2
LITERATURE REVIEW ON THUNDERSTORM RAINFALL
2.1 Introduction
Chapter 2 brings together a general relevant literature review on thunderstorms. It considers
those assumptions which can be related to the thunderstorm activities causing rainfall. The
strategy of this literature review is, therefore, to assemble and categorise material which will
lead to the development of each question to be tested using various applicable methods. All
of the material introduced in the literature review has the purpose, either to develop
arguments for use in the analysis to be described later in the following result chapters or to
unify these arguments. This chapter is divided into the following sections, beginning with the
more general concepts of thunderstorm activities and leading on to the special goals of the
thesis.
2) Thunderstorm Characteristics
3) Synoptic Weather Patterns Creating Thunderstorms
4) Climatic Variables and Thunderstorms
5) Physiographic Parameters and Thunderstorm Rainfall
6) Distribution of Thunderstorms in Australia
7) Synoptic Patterns Associated with Thunderstorm Activity in Australia
8) Sydney's Physiographic Parameters and Thunderstorm Rainfall
2.2 Thunderstorm Characteristics
A thunderstorm is defined as a convective cloud or a collection of clouds in which electrical
discharges, visible as lighting or heard as thunder, is observed by a person on the ground
(Houghton, 1985). Convection systems, which may frequently develop through a
considerable depth within the troposphere, are characterised by cumulonimbus clouds and
considerable moisture (Lutgens and Tarbuck, 1982; Moran and Morgan, 1991). These are
all products of a huge convection system in the atmosphere which can be identified by the
towering cumulonimbus cloud (Figure 2.1). Although a thunderstorm cell is defined as a unit
of convection circulation, thunderstorms may be composed of single or multiple cells (Oliver
and Fairbridge, 1987).
CHAPTER TWO Literature Review on Thunderstorm Rainfall 18
Basically, vertical motion in the atmosphere is the key to many of the characteristics of a
convection system. Upward motion results in expansion, cooling, and eventual condensation
of the water vapour in a stream of air (Wood, 1985). The release of latent heat is often an
important factor in accelerating the convection by increasing the buoyancy (instability) of the
air (Wallace and Hobbs, 1977). Therefore, the prime prerequisites leading to the formation
of thunderstorms are high humidity, high temperatures, an unstable atmosphere, suitable
upper wind structure and a lifting mechanism to initiate convective activity.
Figure 2.1 Schematic representation of a thunderstorm cell (Based upon Bryant, 1991).
2.2.1 Life-Cycle of a Single Thunderstorm
Fairbridge (1967) suggested that often a thunderstorm is a small-scale system which affects a
relatively small area and is short-lived. The life-cycle of such a thunderstorm cell was
summarised by following the three stages that a typical thunderstorm undergoes in its life-
cycle.
In the first stage - the developing stage or growing stage - the rising air may cause small
cumulus clouds to appear, under daytime conditions of unequal heating, particularly during
summer, when convection currents can develop fast. During this stage, strong vertical
updrafts (upward moving air) occur throughout the cloud and consequently no precipitation
reaches the ground (Tapper and Hurry, 1993).
In the second stage or mature stage, the most important changes take place inside the
convection system. Some water droplets begin to freeze, which sets off important drop-
growing processes. In this stage, both upward and downward motions occur in the
CHAPTER TWO Literature Review on Thunderstorm Rainfall 19
convection cell which reaches its maximum development. The mature stage of the
thunderstorm cell may, therefore, be accompanied by violent effects near the earth's surface.
These include squalls, often hail and torrential rainfall (Neiburger et al., 1982). Precipitation
from the mature thunderstorm is intense and composed of large raindrops, literally a
cloudburst (Critchfield, 1983).
In such a situation, the updrafts of a mature thunderstorm produces rain drops through the
condensation of moist air which cools as it rises. W h e n rain drops become too large to be
supported, they fall, however intense updrafts of a severe storm can suspend huge amounts
of rain before releasing a deluge onto the ground. Such rain can reach an intensity of more
than 200 millimetres per hour, provided the environment is humid enough to feed sufficient
moisture to the storm. Occasionally, these thunderstorms become storehouses for
precipitation leading to flash floods (Lilly, 1986, 1990).
(a) Cumulus stage (b) Mature stage (c) Dissipating stage
Figure 2.2 Three stages in the development of a thunderstorm (After Lutgens and Tarbuck, 1982).
In the final stage - the decaying or dissipating stage - the thunderstorm enters the dissipating
stage and the updraught currents disappear entirely and the air motion in the convective
cloud becomes mainly downwards. During the dissipating stage the thunderstorm cell loses
its supply of moisture and energy and disintegrates, but the thunderstorm will continue to
exist if new cells are added at its margins (Bradshaw and Weaver, 1993). W h e n the
downdraft weakens, the intensity of the rainfall decreases and finally stops. Surface weather
conditions soon revert to their pre-thunderstorm stage. Figure 2. 2 illustrates the three stages
in the life-cycle of a thunderstorm.
2.2.2 Complex Thunderstorm Systems
According to the above suggested mechanisms, the life history of a thunderstorm cell used to
be classified into the three stages, however, within the atmosphere, in the three-dimensional
flow fields associated with a thunderstorm, it is not always possible to distinguish one stage
CHAPTER TWO Literature Review on Thunderstorm Rainfall 20
from the other. The complexity occurs when all three stages of development take place in
close proximity and nearly simultaneously. In such circumstances, thunderstorms organise
themselves into a group of cells where each one is at a different development stage at a
specific instant. This multicell stage of a thunderstorm can become severe when it causes
surface damages (Oliver and Fairbridge, 1987).
Supercell thunderstorms are also large thunderstorm systems comprising a number of cells
probably each at different stages of development (Doswell and Brooks, 1993) which can
produce violent and severe weather conditions. The term 'supercell' was first used for such
thunderstorms by Browning (1962). Later, the use of radar, scanning the thunderstorms in
both the horizontal and vertical planes, has allowed a greater understanding of their structure
both in terms of two-dimensional cross-sections and more recently of three-dimensional
models. Generally, the supercell thunderstorm is defined as a large and violent storm
dominated by one huge cell or supercell in a mature stage of development, which may persist
in a steady state for hours, emphasising the fact that such thunderstorms are frequently
asymmetric both in shape and in the distribution of their weather elements. These supercell
thunderstorms are more highly organised, larger, more persistent and more severe than all
other types of thunderstorms (Musk, 1988). The structure of a supercell thunderstorm (a)
and its an idealized plan view (b) is shown by Figure 2.3.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 21
-OVERSHOOTING TOP
BACK-SHEARED, AHV1V
FLANKING LINE
1
MAMMATUS \' '
STORM MOTION
(a) Schematic visual appearance of a supercell thunderstorm
Anvil edge
Light rain Hill Moderate - heavy rain
| H I Small hail | Large hail
T Tornado
Flanking line
Overshooting top
Ob) Idealized plan view of a supercell thunderstorm
Figure 2.3 Schematic visual appearance and an idealized plan view of a supercell thunderstorm (Based on the Australian Bureau of Meteorology and The U.S. National Severe Storms Laboratory publications).
In Austraia, the supercell thunderstorm - another basic type of thunderstorm is far rarer and
much more violent. Recently, much research has been undertaken in understanding and
modelling much more complex thunderstorms, so called 'supercell thunderstorms' (Bureau
of Meteorology, 1995). They could be the subject of much study in the future because of
their severe weather characteristics, which are notorious for producing damaging hail and
tornadoes (Mitchell and Griffiths, 1993). In case of the Sydney region, occasionally the
supercell and multi-cell thunderstorms can be introduced by some of the synoptic weather
patterns, generally advancing from the south-east and north-east (Armstrong and
Colquhoun, 1976). While these kinds of thunderstorms are rare in the region, they tend to be
CHAPTER TWO Literature Review on Thunderstorm Rainfall 22
more severe than air-mass thunderstorms, and they can persist for a longer time, up to
several days (Bureau of Meteorology, 1995).
These widespread thunderstorms, introduced by supercell systems, are almost always
associated with unstable weather systems (for example, lows and troughs) where they may
cause rainfall to develop along the region. Perhaps, the severe thunderstorms over Sydney
Metropolitan area on 21st August 1971 (Bahr et al, 1973) or thunderstorm cells which
advanced to Dapto in February 1985 (Shepherd and Colquhoun, 1985) are specific examples
of such thunderstorms causing damaging flash floods. T w o similar episodes took place at
18th March 1990 and on 21 January 1991 producing considerable intense rainfall from
thunderstorms (Armstrong and Colquhoun, 1976; Mitchell and Griffiths, 1993). There were
some evidence that these storms were supercell thunderstorms. Radar and other
meteorological data were supportive of the conclusion that the damages were caused by high
precipitation supercell thunderstorms (Spark and Casinader, 1995). It appears certain that in
warm seasons (spring and summer) the increased influence of such thunderstorm activity is
responsible for some of the greatest and widespread severe events by producing intense and
high rainfall amounts.
Severe thunderstorms also impose dramatic environment impacts (Dargie, 1994). These
thunderstorms produce hailstones with a diameter of 2 cm or more, wind gusts of 90 km/h
or greater, tornadoes, or any combination of the above (Bureau of Meteorology, 1993b and
Johnson et al., 1995). Severe thunderstorms are also able to produce very high intensity
rainfall causing flash-floods (Elliott, 1994). Such floods from thunderstorms, are exacerbated
when the storm moves slowly, so that one small area receives most of the rain. However, the
largest amount of rain occurs when organised lines of thunderstorms form and move in such
a way that several mature thunderstorms pass over the same location within a short period of
time. In such instances, record rainfalls and, thus floods are the result (Bureau of
Meteorology, 1993 a).
2.3 Synoptic Weather Patterns Creating Thunderstorms
A great deal of knowledge has been added in the past 40 years to our understanding of the
initiation of convective systems and their association with different synoptic-scale
circulations (Campbell 1906 and Kessler, 1983). It was found that the distribution of
thunderstorms world-wide varies from year to year and reflects the overall synoptic patterns
and other affecting factors such as: moist warm waters over oceans, and mountains barriers
during the main thunder-producing months. Further studies in thunderstorm distribution
have emphasised that the synoptic patterns provide suitable conditions in which
thunderstorms can develop easily (Atkinson, 1981 and Mortimore, 1990).
CHAPTER TWO Literature Review on Thunderstorm Rainfall 23
Barnes and Newton (1986) suggest that large circulation systems, for example migratory
cyclones and anticyclones in temperate latitudes, provide the general conditions necessary
for thunderstorm occurrence. Such synoptic-scale systems, whose lives range from days to a
week, are very important because their winds transport moist air into the continental regions,
where the main thunderstorm activity takes place. In addition to the role played by these
circulations in carrying heat and moisture horizontally over long distances, their associated
regions of organised ascending and descending motions contribute to thunderstorm
development. These motions also affect the vertical stratification of temperature and water
vapour in ways that lead to selective occurrence of convective storms in restricted regions.
Therefore, most thunderstorms are controlled by the broad-scale circulation systems
(anticyclones and cyclones) and their systematic rising motions, particularly over land areas
adjacent to the western sides of the oceans in subtropical and temperate latitudes.
In the USA, early investigators (for example, Carpenter, 1913) found that there is an
association between thunderstorms and synoptic weather patterns. Later, Blake (1933) noted
that two synoptic patterns are associated with thunderstorms. One pattern consists of air
approaching from the south and east and it is traditionally called the 'Sonara storm'. The
other pattern brings in tropical air from the south and west and is due to a dissipating
'Chubasco' that has penetrated far enough northward to affect southern California.
Regardless of large-scale synoptic weather patterns, the resulting thunderstorms are also
associated with air-mass systems.
Over the European continent, the origin of sever thunderstorms is correlated to the slow
moving low pressure systems and troughs. For example, in the relatively warm summer of
1992 there were several occasions of heavy convective systems with hazardous weather in
central Europe (Kurz, 1993). O n many of these occasions, synoptic weather patterns such
as: the upper troughs moving slowly eastwards, and shallow depressions corresponding to
the troughs and associated fronts were responsible for the development of many
thunderstorm activities (Andersson et al., 1989 and Prezerakos, 1989). In England, Prichard
(1990) found that thunderstorms which occur during summer nights, after a hot day, may be
triggered by cold moving fronts driven by fairly sharp upper troughs. These troughs draw
hot continental air into a zone where there is sufficient moisture to fuel thunderstorms.
However, in Spain Liasat and Ramis (1989) indicated that most convectional generated
rainfalls can be controlled by upper cut-off lows. Over this region, thunderstorms can also be
encouraged by warm air advection from the south or south-east at low levels.
These studies are only a few examples from different parts of the world, suggesting that
within the broad-scale weather systems, thunderstorm activity can take place. Other synoptic
weather patterns on a regional-scale such as fronts, lows, troughs and extreme instability in
the free upper atmosphere are also favoured systems for the introduction and creation of
CHAPTER TWO Literature Review on Thunderstorm Rainfall
many thunderstorms. However, they are probably not necessary nor a sufficient reason for
the occurrence of thunderstorms. Other trigger mechanisms for thunderstorm initiation are
required.
2.4 Climatic Variables and Thunderstorms
It has been suggested that some climatic variables (for example, both air and sea
temperatures) are important in creating or affecting a convection system and, as a result,
explain thunderstorm variation, in time and space (Willet and Sanders, 1959; Golde, 1977).
For example Lutgens and Tarbuck (1982 p:237) suggest:
'All thunderstorms require warm, moist air, which, when lifted, will release sufficient latent heat to provide the buoyancy to maintain its upward flight. Although this instability and associated buoyancy are triggered by a number of different processes, all thunderstorms need an unstable atmospheric environment in which the instability can be enhanced by high surface temperatures'.
2.4.1 Air Temperature
Generally, the air temperature which was primarily assumed to be a function of the amount
of solar radiation received on the ground, has also been known to be one of the main
climatic variables causing convection activity (Critchfield, 1983). It has been already shown
that thunderstorms generally occur within moist, warm air-masses that have become unstable
through surface heating. Because instability is enhanced by high surface temperature,
thunderstorms are most common in the afternoon and early evening, particularly in summer
months when uneven heating generates vigorous convection which can lead to the growth of
storms in a matter of hours.
More recently, Laudet et al. (1994) used lightning data from the Lightning Position Tracking
System (LPATS) to derive a preliminary climatology of lightning in N S W . In contrast with
the traditional thunderstorm observation (lightning seen and thunder heard by observers), the
L P A T S system gives real-time lightning and it is becoming an important tool for
thunderstorm observation and forecasting. Using this system, it was found that the spatial
distribution of lightning (thunderstorms), is seen to be a temperature-related variable more
dominant during summer than during the rest of the year. Over the land in summer the
diurnal variation in thunderstorm occurrence closely follows the diurnal temperature
variation. Therefore, most thunderstorms develop around midday in the spring and summer
months when the potential for convection is usually the greatest and adequate water vapour
is available.
Areas of high elevation facing the sun - which obtain much more solar radiation (Benjamin,
1983) and, as a result, have high surface temperatures, have been suggested, affect
CHAPTER TWO Literature Review on Thunderstorm Rainfall 25
thunderstorm activity, particularly in summer months (see section 2.6.1). However, surface
heating is generally not sufficient in itself to cause thunderstorm activity and, therefore, any
another climatic or non climatic factor that can destabilise the air, aids in generating a
thunderstorm. This simply means that the air temperature should be considered as one of the
factors which is able to create or enhance thunderstorm activity.
2.4.2 Sea-Surface Temperature
Surface heating is another climatic factore that may enhance thunderstorm activity by
supplying moisture to feed convection (Ramage, 1972). Studies by Rodewald (1963) and
Bjerknes (1963) indicate the importance of variations in sea-surface temperature and its
associations with other climatic variables over certain regions, particularly in the North
Atlantic. Bartzokas and Metaxas (1994) suggest sea-surface temperature is a fundamental
parameter in meteorology and climatology of the Mediterranean region. Research has also
been focused on sea-surface temperature patterns and their relationship to rainfall in the
tropics by Ichiye and Paterson (1963). Their results indicated positive relationships between
sea-surface temperature and rainfall amount. Hastenrath (1984) compared selected dry and
wet years for the Sahel in Africa, revealing that wet years are associated with warmer than
normal surface waters and indicated that sea-surface temperatures can modify rainfall
distribution in time and space considerably. In many circumstances a certain relationship
between sea-surface temperature - as a fundamental factor in climatology - and rainfall was
therefore settled.
In Australia, the possible association between sea-surface temperature and rainfall, but not
necessarily thunderstorms, has been well established. For example, Streten (1981 and 1983)
demonstrated that wet years over the Australian continent are associated with warmer than
normal sea-surface temperature. Whetton (1990), by correlating the Victorian rainfall and
patterns of sea-surface temperature anomalies concluded that increased rainfall in Victoria
can be related to warm sea-surface temperature off the north-west coast of the continent.
Also Nicholls (1984) documented the exitence of a relationship amongst the SST anomalies,
the Southern Oscillation, and interannual fluctuations in the Austalian Tropical Cyclone, in a
broad band from south-east Australia through the centre of the continent to the north-west
coast (Nicholls and Kariko, 1993).
It was also found that the sea surface temperature can affect the rainfall distribution in time
and space. For example, in the Sydney region, O'Mahoney (1961) noted evidence of a
correlation between Sydney monthly rainfall and sea temperature at Port Hacking. Later,
Priestley (1964) showed that there is a positive association amongst monthly anomalies in
rainfall, air and sea temperatures along the N S W coast. Subsequent publications by Priestley
and Troup (1966) and Priestley (1970) stressed that some of Sydney's rain comes from
CHAPTER TWO Literature Review on Thunderstorm Rainfall 26
onshore winds, the moisture content and instability of which is increased by the warm
Tasman Sea. However, they indicated that the correlation coefficient was not high (r = 0.2).
Hirst and Linacre (1978) examined connections amongst sea-surface temperature, rainfall
and prevailing winds. Their evidence indicated that rainfall and sea-surface temperature are
positively connected and also that the incidence of onshore winds can increase rainfall
amounts. Correlation coefficients were however low (less than 0.24). Hirst and Linacre
(1978) concluded that sea-surface temperature and onshore winds individually can control
the rainfall distribution in the region. They also suggested that a warmer sea surface and
onshore winds may cause more instability and, as a result, enhance orographic rainfall in the
coastal hills or convective rainfall by bringing more moist and warm air to the region.
Generally, these studies found that the association between sea-surface temperature and
rainfall is small for places which are far from the coast.
Later, Fandry and Leslie (1984) found that easterly flows which are located just off the coast
and parallel to the coast can move west over the Sydney area and appear as meso-scale
phenomena. Leslie et al. (1987) show that these systems can be enhanced by the topography
of the region and by the meridional gradient of sea-surface temperature toward the coast.
More recently, Hopkins and Holland (1994) found that the combination of the Great
Dividing Range, the cooler coastal land mass, and sea surface temperature gradient provides
high zonal baroclinicity favourable for formation and intensification of the Australian east-
coast cyclones. For example, a 24-hour rainfall event on the 1 st August 1990 was simulated
by Golding and Leslie (1993). They showed that falls of over 100 m m were confined to
coast facing slopes of the Great Dividing Ranges from Newcastle in the north to Jervis Bay,
south of the Sydney region. The output of the model simulations indicated that the
precipitation was enhanced about fourfold, with maxima of 90 m m over the sea, and 120 m m
over the mountains. It was also found that over half of the precipitation was of a convective
nature. Sea-surface temperature data in the region, were already correlated to the rainfall by
Bryant (1983a, 1985a, 1988) who suggested significant relationships between monthly sea
level, sea-surface temperature and rainfall at Stanwell Park beach just south of Sydney.
In addition, the effect of the difference between sea and air temperatures on the instability of
the atmosphere is a very important factor. Such instability, particularly over the coastal
areas, can cause thunderstorm activity. Linacre and Hobbs (1977) supposed that in those
conditions, when a warm sea makes the air less stable, a free convection in low winds can be
expected. This mechanism, which is a result of the instability in the atmosphere, can lead to
the growth of tall clouds which are conditionally unstable. Sometimes convection on a vast
scale leads to strong updraughts and turbulence within the cloud, which can cause rain,
lightning and thunderstorms.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 27
The difference between land and sea temperatures may lead to instability and hence influence
the growth of thunderstorms in various ways. For instance, when the daytime heating of the
ground along the coastal areas causes high temperatures, especially in summer, cold air-mass
flows from a cold to warmer surface, from sea to the land (Gentilli, 1971). This mechanism
may enhance the afternoon convection activity. In contrast, when the cold air-mass comes
from the cold land and the sea is warm, nocturnal thunderstorms may occur over the sea
because of oceanic warmth and thus the presence of moisture in the lower layers of the air.
Occasionally, thunderstorms can also be expected, because of the passing of cold air over
warm sea or warm air. This is relatively c o m m o n off the coast of N S W in autumn (Linacre
and Hobbs, 1977).
All of these studies have generally shown an association between rainfall and sea-surface
temperature or air temperature. Although none have established an association directly
between thunderstorm rainfall and sea-surface temperature data, Hirst and Linacre (1978
p. 327) announced, 'a high incidence of onshore winds would enhance convective rainfall, by
bringing the moist air together with a warmer sea-surface temperature'. Both moist air and a
warmer sea can cause greater instability in the coastal atmosphere and, as a result, increase
the tendency for convective rainfall. According to this idea Linacre (1992 p:262) suggests,
'It seems plausible that surface conditions which influence evaporation thereby affect the subsequent rainfall, especially in the case of meso-scale convective precipitation'.
He also assumed that in some circumstances, where high temperatures occur with little wind
there is more possibility of convective rainfall.
On the other hand, there are some arguments (Linacre, 1992) that surface conditions have
little effect on rainfall, except in special circumstances, for at least two reasons. First,
variations in surface conditions are usually not felt beyond a few hundred metres from the
ground, which is much below the level at which rain is formed. Secondly, the sequence of
evaporation, advection, condensation and precipitation normally takes several days. B y that
time the airflow has separated rainfall from its source by some hundreds or thousands of
kilometres. However, it must be noted that in case of convective activity, the precipitation
procedure takes place in a matter of hours or within a day.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 28
2.4.3 El Nino / Southern Oscillation
In the past, the El Nino / Southern Oscillation (ENSO) phenomenon was correlated to the
rainfall variability. The influnce of E N S O on rainfall has been a matter of investigations by
many meteorologiests and climatologists in Australia (Allan, 1985). Recent studies
questioned the stability of relationships between the Southern Oscilation Index (SOI) and the
summer rainfall during the last century (Allan, 1988, 1989; Suppiah, 1992). The instability in
correlation patterns is not only apparent in the Australian region, but it is a c o m m o n feature
in the global scale. Certainly, over the Australian continent, the rainfall data indicate long-
term variations. For example, Pittock (1975) demonstrated a dry phase between 1913 and
1945 and a wet phase from 1946 to 1978 over the Australian region. In another example, the
summer monsoon circulation features were closely linked to various cycles that include the
30-50 day oscillation and E N S O phenomenon over the region. Suppiah (1992) suggested
that the SOI could have a strong influnce on local rainfall. A n inspection of Bureau of
Meteorology (1988a) data - the average number of annual thunder-days in northern
Australia - reveals that the areas having large numbers of thunderstorms, show significant
correlation between summer rainfall and SOI. Interestingly, these areas indicate a greater
number of thunderstorms during the summer season. In the Sydney metropolitan area,
Griffiths et al. (1993) correlated the number of severe thunderstorms with SOI. However,
coefficients calculated were mostly very small (0.214). This has a probability of occurrence
by chance of 0.007, which indicates that the relationship has litle predictive value. Therefore,
in the future, studies on the influnces of phenomenon such E N S O on various thunderstorm
rainfall systems would be useful for providing further information.
Despite these arguments, it can be concluded that climatic variables on a regional scale have
a major influence on rainfall distribution. Ocean waters adjacent to the coast can provide
atmospheric moisture and, as a result, affect the rainfall distribution patterns. Thunderstorms
can also be enhanced by warm and moist air from the ocean and high temperature of the
earth's surface, largely as a result of differential heating.
2.5 Physiographic Parameters and Thunderstorm Rainfall
Many climatologists (Browning and Hill, 1981; Atkinson, 1983) proposed that distribution
of precipitation - over a region in a specific time-scale - is largely a function of the interplay
and interaction between synoptic air patterns (at several scales), cloud physics and
physiographic factors. The influence of each of these factors upon the distribution, amount
and variation of rainfall has been explored both temporally and spatially throughout this
century (Fogel and Hyun, 1990; Bonell and Sumner 1992). These studies have generally
suggested that topography, proximity to sea, and more recently, urban areas are the most
important controlling factors in rainfall distribution and its characteristics.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 29
However, it has been suggested that the effect of these parameters in a wide variety of
latitudes, climates and weather conditions is not the same (Smith, 1982, 1989). Moreover, in
contrast with normal precipitation, which is suggested to be of more modest intensity but of
longer duration and covering a large area (Oladipo and Mornu, 1985), convectional
precipitation has extremely high intensity, a short-duration nature and affects a
comparatively small area with considerable spatial variation at the ground surface.
Therefore, it is logical in a new region to correlate each physiographic parameter with
thunderstorm rainfall distribution rather than rely upon previously defined relationships.
2.5.1 Topography and Thunderstorm Rainfall
Clear relationships between precipitation amount and elevation are now generally established
(Bader and Roach, 1977; Browning, 1980; Hill et al., 1981). The effects of topography on
annual and seasonal rainfall distribution have long been recognised by Salter and Cale (1921)
and Bergeron (1965). Several objective attempts have been conducted to assess statistically
the influence of altitude and other topographic parameters on the distribution of
precipitation. In the most general terms, the orographic impact on cloud and precipitation
enhancement or inhibition is well known (Pedgley, 1970 and Wheeler, 1990).
Orographic precipitation enhancement occurs in a wide variety of latitudes, climates and
weather conditions near terrains of differing size and shape (Schermenhom, 1967; Griffiths
and Saveney, 1983; Storr and Ferguson, 1983). Over the long term, areas of high relief
experience generally greater precipitation amounts and intensities on their windward sides
and near the summits, but often produce a rain shadow on their lee side (Craig, 1980;
Atkinson, 1983). Topographic features such as spot altitude, rise, orientation and exposure
on rain bringing wind, have been suggested to be important topographic factors influencing
rainfall amounts and distribution (Balchin and Pye, 1948). Various authors (for example,
Browning et al., 1974; Atkinson and Smithson, 1974) have discussed the nature of
orographic rainfall in different geographical areas. All these researchers looked at the effects
of topographic features upon precipitation distribution. They found that, generally
orographical precipitation occurs over, and occasionally immediately downwind of the relief,
in a close association with the origin of weather systems.
There is, however, little discussion in the literature dealing with thunderstorm rainfall related
to topographic factors. In contrast with normal precipitation, thunderstorm rainfall was
suggested to be spatially heterogeneous and highly time dependent (Sumner, 1988). O n a
global scale, it was reported that the overall distribution pattern of thunderstorms is
influenced by three primary elements; the intertropical convergence zone, the solar heating
of land masses and finally warm ocean currents (Brooks, 1925). But, on a regional basis, it
CHAPTER TWO Literature Review on Thunderstorm Rainfall 30
has been suggested that the geographical features which encourage convective cloud
formation, and as a result thunderstorm development, are: the land-water boundary, land
heating, particularly on summer days, and mountainous terrain particularly north-east facing
slopes in the Southern Hemispheric (Fuquay, 1962).
Mountains act as a high level heat and moisture source and as a barrier to prevailing air flow
that can enhance convective cloud formation (Chacon and Fernandez, 1985). Because the air
near the mountain slope is heated more intensely than air at the same elevation over the
adjacent lowlands, this may cause a general upward movement during the daytime and the
development of thunderstorm cells. For example, mountainous regions such as the Rockies
and Appalachians in the U S A , experience a greater number of air-mass thunderstorms than
the Plains States. (Lutgens and Tarbuck, 1982). Generally, precipitation systems develop
easily in mountainous areas which can also be subject to local thunderstorm development
during heatwaves. The forced uplift mechanism can provide the final push which causes
atmospheric instability. This itself may release massive potential energy. Such a condition
can trigger-off damaging thunderstorms, whilst most parts of the region may remain sunny
and cloudless (Mortimore, 1990).
In the past, mountain-generated thunderstorms have been studied in several locations. For
example: K u o and Orville (1973) in the Black Hills; Holroyd (1982) in the northern Great
Plains; Klitch et al. (1985) in Colorado; and Banta (1984) in northern N e w Mexico. All these
studies have illustrated that convective activity usually occurs first in mountainous areas. For
example, in a study of a mountain-generated precipitation system in Northern Taiwan, a
radar system was used to investigate the effect of terrain on precipitation systems (Chen et
al., 1991). They found that mountains can obtain high heat and keep moisture which both are
important climatic factors in the producing of thunderstorm activity. The influence of
topography and exposure to moisture sources emerges as the major controlling factor of the
thunderstorm rainfall amount and its distribution.
In addition to the complex interaction between local winds, topography was hypothesised
through the use of many case studies, to be important for generating localised precipitation.
For example, in the U S A the important influence of topographic features on the distribution
of convective rain has long been recognised (Tubbs, 1972). It has been suggested that
orographic features such as hills and aspects to the wind direction, contribute to the
development of convective clouds for one or more of the following reasons: (1) topographic
features may encourage the instability of a conditionally unstable air-mass; (2) the roughness
of the terrain results in a series of vertical perturbations, some of which may trigger the
formation of cumulus clouds in a conditionally unstable air-mass; (3) the hills and mountains
can act as high-level heat sources due to the differential heating of their tops and of the free
air at the same altitudes (Byers and Braham, 1949).
CHAPTER TWO Literature Review on Thunderstorm Rainfall Jl
Again, in the United States, the effect of mountainous barriers on the distribution and diurnal
variations of thunderstorm rainfall was well stressed by Schermenhom (1967), Wallace
(1975) and Mass (1982). These studies found that some thunderstorm cells tend to originate
in the same place because of topographic effects, and then may follow lower surrounding
topography. For example, Astling (1984) studied a relationship between diurnal mesoscale
circulations and precipitation in a mountain valley (Utah in U S A ) . H e found that the
mountainous terrain of Utah is a region where diurnal signatures are present in precipitation
occurrences and in local wind fields. This study indicated that summer diurnal precipitation
modulations are dependent on elevation, with maximum frequencies of measurable events
peaking in the early afternoon at high elevations above 2100 m and nearly three hours later
in mountain valleys below 1500 m.
The summer thunderstorms over southern California, which were studied by Tubbs (1972),
primarily occur over the mountains. It was also evident that the mountains to the south and
east receive many more thunderstorms than those ranges to the north and west. Over the
years, summer thunderstorms have also hit parts of the Rocky Mountains harder than any
other areas of the United States. A study by Easterling and Robinson (1988) and Easterling
(1991) showed that parts of the Rocky Mountains have had the highest number of summer
thunderstorms. Furthermore, thunderstorm tracks can be frequently guided by topographic
features which may locally enhance precipitation when larger weather systems dominate
(Berndtsson, 1989).
In other parts of world, for example, over Nigeria, Balogun (1981) found that the
orientation of the maximum thunderstorm activity lines along the south-eastern part of
Nigeria follows the orientation of mountain ranges. Therefore, he concluded, that even on a
localised scale, the degree of instability, and as a result, the intensity of thunderstorm
activity, can be largely dependant on the topographic features.
In the United Kingdom, a considerable amount of summer precipitation has been attributed
to localised convectional thunderstorms (Mortimore, 1990). In 1962, Shaw indicated that at
some rainfall stations more than 90 per cent of summer rainfall comes under the category of
'thunderstorm'. In this region, topographical features play an important part in the more
local nature of thunderstorm development. It was suggested that mountain ranges can set-off
thunderstorms in potentially unstable airflows and this development can, in some situations,
drift away and further develop and affect large areas of lowland Britain. Fogel and Hyun
(1990 p:126) have generally suggested that for summer thunderstorms,
'The elevation effect may be caused by: (1) an increase in the rate of arrival of events or equivalent decrease of expected inter-arrival time; (2) an increase in the mean rainfall per event which in turn
TWO Literature Review on Thunderstorm Rainfall
may originate from a change in event structure such as changes in event duration or the dependence between duration and rainfall amount. (3) and a combination of (1) and (2)'.
The incidence of thunderstorms in Australia is also higher in the higher parts of the country.
Over much of the Australian continent relatively low relief dictates that weather systems are
comparactively unaffected by local topographic factors, but where considerable relief is
present, such as along the Great Dividing Range and the tablelands of N S W and Queensland,
there can be considerable modifications (Sumner, 1983a). As it was indicated by Linacre and
Hobbs (1977), in N S W there is a clear correlation between the frequency of thunder-days
and elevation. In such area, the occurrence of thunderstorms and rainfall is very much
affected by topographic factors (Batt, et al., 1995). This is because the effect of local
topography is a major factor in the triggering action. The spatial distributions of lightning in
N S W also support the concept that topography is very significant factor in controlling
thunderstorms. This effect is evident in areas surronding the Great Dividing Range and over
the nearby ranges (Laudet et al., 1994).
On the other hand, there is some argument in the literature illustrating the fact that the
enhancement of thunderstorm rainfall is not exactly associated with areas of pronounced or
extreme relief. For example, Castro et al., (1992) have attempted to determine whether the
topography of the area where storm formation takes place has an effect on the behaviour of
storms. The results obtained from an area located in the north east of the Iberian Peninsula,
namely Middle Ebro Valley (in Spain), showed h o w different storms with different internal
structure (unicellular or multicellular) and behaviours were differentially affected by
topography.
In the USA, (in the Santa Catalina Mountains near Tucson, Arizona) Duckstein et al. (1973)
compared precipitation amounts deduced from winter frontal systems with summertime air-
mass thunderstorm rainfalls. They noted that winter precipitation was increased more than
four-fold at 2100 metres as compared to that found at 1200 metres. For the same elevation
(2100 metres), summer rainfall was not quite doubled.
Also, along the Appalachian region, Easterling (1989 and 1990), showed orography plays a
substantial role in determining the thunderstorm rainfall regime at a station. This effect was
acknowledged by the U.S. Weather Bureau (1947), where a decrease was noted in daily
precipitation intensities for the summer months. Also, Easterling and Robinson (1988) have
suggested that the mountain areas of the Rockies is a region with a relatively high probability
of receiving small rainfall amounts from thunderstorms. In addition, it has been found that in
mountainous areas, because of the complex topography, the spatial distribution of
thunderstorm rainfall varies greatly (Fuquay, 1962). Therefore, Smith (1989), suggested that
CHAPTER TWO Literature Review on Thunderstorm Rainfall 33
the thunderstorm activity in a mountainous area varies not only with elevation, but also with
slope angle, orientation and micro topography.
Some researchers, for example Osborn (1982), think that the importance of topography in
enhancing the variations in thunderstorm rainfall distribution for each of the weather types
such as, frontal systems or troughs, is not the same. Generally, the distribution of
thunderstorm rain in mountainous regions reflects variations in wind direction as different
slopes and land surfaces induce thunderstorm activity (Smith, 1975). The final role of
topography, as a generator of thunderstorms in mountainous regions, is as an initiator of
different convective activity between opposing wind streams in different directions. The
relative importance of each of these topographic factors in generating thunderstorms may
clearly change from day to day, as weather conditions change. For instance, in a study in the
Greater Athens area, Amanatidis et al. (1991) found that during summer months the
thunderstorm activity is influenced less by the local topographic features.
Worldwide, the positive correlation of increased thunderstorm activity with altitude is well
documented, especially on the windward side of mountains (Spreen, 1947; Reid, 1973). In
many locations, the effect of mountains on thunderstorm activity is clearly seen on maps
showing the correspondence between patterns of thunder-days and terrain height. The
results from many parts of the world indicate that thunderstorm rainfall-relief relationships
are also positive because topography plays a large part in the formation of heavy showers
and thunderstorms in association with advancing airmasses. Therefore, over long-time
periods 'classic orographic' enhancement of thunderstorm activity should provide the most
suitable explanation for permanent spatial variations in thunderstorm rainfall amounts.
On the other hand, some investigators have pointed out that because of the localised nature
of thunderstorms, topography does not always appear to be an important factor affecting
thunderstorm rainfall amounts. Thus, attempts should be made by climatologists to look at
the thunderstorm rainfall-elevation relationships over both short and long-term periods, for
each individual geographic location.
2.5.2 Effects of Proximity to the Sea upon Thunderstorm Rainfall
Proximity to the sea is known to be a very important factor in producing rainfall as well as
influencing rainfall patterns (Merva et al., 1976; Berndtsson and Niemczynowicz, 1988). In
the literature there have been only few studies that have concentrated on the details of
thunderstorm rainfall mechanisms along the coastal areas. However, theoretical studies have
generally verified that, in many places, wind circulation may enhance the effects of surface
heating and, as a result, initiate a convection system over land near the sea.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 34
Estoque (1962) and Findlater (1963), for example, suggested that mechanisms such as
surface, upper winds and surface heating together, may produce convection activities
adjacent to water bodies, leading to thunderstorms and perhaps rains. Sumner (1983b) found
that these meso-scale circulations in the lower troposphere may develop in response to
differential surface heating in particular, between the land and the adjacent sea. These
mechanisms may cause convectional activity in response to differential solar radiation during
the day, depending on the geographic characteristics and weather conditions of each place.
Such conditions may produce thunderstorm activity and showers. Simplified and idealised
models have been successfully constructed by, for example, Simpson (1964) and Simpson et
al. (1977). Further detailed information on the dynamics and theory can be found in
Atkinson (1981).
The effect of the distance from the sea on rainfall patterns can be seen on Florida's coastal
areas. Perhaps this region provides an ideal example, in which the thunderstorm / local wind
systems develop parallel to coast-lines (Byers and Rodehurst, 1948). The close association
between the time of occurrence of rainfall and thunderstorms over the region was studied by
Gentry and Moore (1954) and L'hermitte (1974). All these studies have emphasised the
importance of coastal areas in which the thunderstorm activity can easily be developed.
In Tanzania, in Dar es Salaam, coastal influence on rainfall generation has been illustrated by
Sumner (1984) using the spatial correlation of daily data. H e found that, because of the
development of rainstorms along the coast, rainfall distribution patterns paralleled the coastal
area. In other places, locally intense thunderstorm activity, reflecting coastal and orographic
influences, have also been highlighted. For example, in Catalonia (in Spain), it was suggested
that complex relief and morphology can create unique precipitation areas. One such area is
characterised by its vicinity to the sea (Periago et al., 1991). Also, in Israel, a relatively high
incidence of convectional rainfall was recognised in the coastal areas (Sharon and Kutiel,
1986). In the western Mediterranean basin, Sumner et al. (1993) found that the heaviest
rainfall that had contributed to severe localised flooding, was convectionally generated by
upper cut-off lows which were often controlled by proximity to warm Mediterranean waters.
More recently, in a study in South Carolina U S A , Changnon (1994) indicated that the sea-
breeze circulation, which influences convection near the coast, exhibits its strongest
influence on heavy rainfalls during the summer months.
On the other hand, it appears almost certain that enhancement mechanisms in coastal areas
vary considerably and they may change from winter to summer (Smith, 1985). The effect
also depends on the direction of the prevailing wind. In particular, heavy rainfall can occur
on steep windward slopes facing the sea, as the hills may trigger thunderstorms and anchor
them in the lowlands (Tubbs, 1972). In the U S A , Easterbrook and Rogers (1974)
concentrated on sea-breeze front thunderstorms along the Georgia coast. They found that
CHAPTER TWO Literature Review on Thunderstorm Rainfall 35
generally, thunderstorms occurred in a preferred zone within 50 km of the coast and not
exactly near the coastal areas. In this region it was also found that winds blowing parallel to
the coast, were seen as important in the generation of thunderstorms.
The proximity factor seems to influence the thunderstorm rainfall pattern, particularly in
coastal areas. In these areas, thunderstorms occur in a preferred zone, because in many cases
the local winds were thought to be able to increase the instability of the atmosphere. Also
the existence of moist winds blowing parallel to the coast had been supposed as important in
the enhancing or generation of thunderstorms. In many places decreases may be seen in
precipitation as the distance from the coast increases.
By contrast, along the coastal areas, on a smaller scale, the general trend may be spatially
reversed particularly during the summer months in which the thunderstorm rainfall patterns
display larger variability. Therefore, along the coastal area because of the complex
topography and the juxtaposition of land and water over short distances, different
thunderstorms rainfall patterns may be experienced in the various seasons.
2.5.3 Impacts of Urban Areas on Thunderstorm Rainfall Distribution
Several climatic studies during the past 100 years have shown that cities develop their own
special internal climate, being warmer and less windy than rural areas (Chandler, 1965; Oke,
1977). It has also been established in numerous empirical studies, for example Landsberg
(1962) and Oke (1979), that city meso-climates are markedly different from those over
surrounding, more natural areas. Moreover, a few key climatic studies in the past 30 years
such as Smith (1975), Huff and Vogel (1978) and Lee (1984) found that cities may also
produce effects on clouds and precipitation that extend several kilometres out from the city.
For several years, the reality of urban effects on precipitation was the subject of considerable
debate (Changnon, 1969; Atkinson, 1968, 1971 and Landsberg, 1981). The possible effects
of urban areas on precipitation have recently received more attention, particularly in regard
to the incidence of short-duration heavy rainstorms of convective origin (Bradshaw and
Weaver, 1993). A great deal has been written about the influence of urban areas on summer
rainfall distribution and thunderstorm activity in the last decades. For convenience, this
section will review the findings of some investigators throughout the world.
A review of research in the USA concerning the modification of rainfall by urban areas
reveals that research was quite poor before Changnon (1968). The 1968 reporting of the
LaPorte precipitation anomaly by Changnon (1968) drew a great deal of attention and
focused national interest in the U S A on the subject of urban effects on precipitation. Results
of this study showed about 31 per cent increases in warm season rainfall, more days with
moderate to heavy rainfall, 38 per cent more thunderstorms, and even 246 per cent more hail
days on downwind of Chicago compared to the surrounding areas.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 36
The interest initiated by this study led to intensive climatic studies of other American cities
by Changnon (1969), Huff and Changnon (1972 and 1973). In general, relatively strong
evidence of urban effects was found in the precipitation distributions for St. Louis, Chicago,
Detroit, and Washington (Harnack and Landsberg, 1975). Although in some cities (for
example, Indianapolis and Tulsa) evidence was weak, the urban effect appeared to be more
pronounced in summer than in winter and usually maximised 50 to 35 miles downwind of the
city centre. However, effects were identified within the city also, at Chicago, Detroit,
Washington, and N e w Orleans (Changnon, 1978).
In the USA, concern over the complex problem of urban effects on clouds, precipitation, and
related severe weather phenomenon finally led to a major investigation of the prior research
of the Metropolitan Meteorological Experiment ( M E T R O M E X ) at St. Louis (Changnon et
al., 1971). This project was the world's first major field-research program with a 5-year field
experiment, and it was sponsored by several government agencies, universities and institutes.
This study attempted to clearly establish how, when, and where an urban area affects
atmospheric behaviour, especially convective rainfall. As a result, summer thunderstorms and
associated rainfall have been found to be 25 per cent higher in urban areas.
Other studies such as Huff (1975); Huff and Vogel (1978); and Changnon (1973 and 1978)
embraced a variety of cities in different climates of the U S A . They mostly reported 5 to 30
per cent local increases in rainfall amounts. These findings were supported by Landsberg
(1981) and Changnon and Huff (1986). These studies generally found the urban effects on
summer weather conditions included greater convective activity, more thunderstorms, 10 to
30 percent more precipitation, and a greater incidence of hail along storm paths to the lee of
the urban area, than over adjacent rural land. According to Huff and Changnon (1986) in
urban areas, not only the probability of stormy rainfall is high, but also very heavy rainfall
can occur in the late afternoons or nights because of additional urban heating of the lower
troposphere.
In the United Kingdom, the possible effects of urban areas on precipitation have been
attracting increasing attention in the past years, especially with regards to the incidence of
short-duration heavy thunderstorms. Parry (1956) and Barnes (1960) attempted to study
single storms over Reading and the Midlands areas, respectively. These studies were the first
two examples of the climatological approach to the urban rainfall problems in U K . Although,
there was a lack of sufficient data because of less dense rain gauge network, their results
indicated the effect of urban areas on the distribution of thunder rain.
Later, Atkinson (1968 and 1969) showed the maximum in thunder rainfall over the central
part of London in summer in a feature associated with warm frontal storms. H e strongly
CHAPTER TWO Literature Review on Thunderstorm Rainfall 37
attributed this pattern to higher daytime temperatures and increased air turbulence over the
city centre. In 1971, Atkinson studied a storm formed about 20 miles west of London. H e
found that when this storm passed over the city, there was rapid cloud growth which
resulted in a maximum of precipitation over the local urban area. This case study indicated
that moving storms can be influenced by the warm moist air of urban areas.
Atkinson (1977) further demonstrated that convective rainfall over London was enhanced by
the presence of the urban heat island. H e found that urban-producing heat can increase the
incidence of thunder rainfall and thunder itself, especially in the hilly areas of the city. This
study suggested that cities which develop an urban heat island also attract thunderstorms,
because the heat island tends to initiate updrafts over the city, which then draws in any
thunderstorms developing in the area.
In the other parts of the world, the study of urban effects upon rainfall distribution have been
followed by several researchers, using available climatological data. For instance, in India,
Khemani and Murty (1973) used the rainfall data of three stations in the region downwind of
the urban industrial complex at Bombay, and of two stations in the nearby non-urban region.
They found that, with respect to the non-urban region, the region downwind of the urban
industrial complex recorded an increase of rainfall by about 15 per cent. They attributed this
increase in rainfall amount to the high level of industrialisation.
On the other hand, the findings of a number of studies - which were about the relationship
between precipitation and urban-dome - have indicated divergent viewpoints. For example,
in a study of three Japanese cities (Tokyo, Osaka, and Nagoya), Sekiguti and Tamiya (1970)
have noted that it often happens that no rain has been observed in the big cities, but in their
outskirts, fairly good amounts of rain (have been) measured. Also, Tabony (1980) in a study
of rainfall trends over London, indicated that any feature of rainfall patterns could not be
attributed to urbanisation. However, he has not rejected the effect of urban areas upon the
frequency of high-intensity, short-duration rainstorms during summer. O n the basis of the
above-mentioned evidence, the following points appear worthy of emphasis as concluding
remarks:
1) Generally, it was found that urban areas can affect incoming solar radiation changing
albedo rates and heating processes, so that in the day, there is a greater take-up of solar
radiation in the city than in its surroundings (Auer, 1978). Some climatologists such as
Vogel and Huff (1978) think that cities decrease wind speeds and, humidity rates but
increase cloudiness and precipitation amounts.
2) The materials used in buildings, paved surfaces and the multi-faced nature of the rough
urban surface, not only make for increased opportunity for absorption of heat, but also
CHAPTER TWO Literature Review on Thunderstorm Rainfall 38
increase heat storage (Henry et al., 1985). The result is that urban areas are appreciably
warmer that their surrounding rural areas during the day. This produces the so-called 'heat
island' phenomenon which leads to rising vertical motion over the cities and subsequent
convectional activities (Hane, 1978). Therefore, cities as warmer locations are often also
areas of enhanced thermal process. The magnitude of the urban 'heat island' has been shown
to be proportional to city population size for European and North American cities (Oke,
1979).
3) Atmospheric pollution may also increase rainfall (Landsberg, 1962). As a result, it was
also suggested that thunderstorm activity may increase, if it is reinforced by increases in the
amount of atmospheric aerosols such as smoke from bush fires, pollution or thermonuclear
devices (Changnon and Huff, 1973).
4) Finally, there is considerable evidence that the thermodynamic effects of urban
environment upon precipitation processes is a very important factor in the initiation of
convection cells, and over large cities, aerodynamic roughness of urban structure may
enhance the development of severe weather systems such as thunderstorm activity
(Landsberg, 1981).
Briefly, the findings of the above-mentioned researchers established the reality of urban
impacts upon anomalies of most climatic elements such as temperature, humidity, and
precipitation amount. In urban areas, air temperatures are generally warmer than the
surrounding areas, so rural cooler air may be drawn inwards to feed the enhanced convection
near the urban centre. Although the relative humidity in the city may be lower than that of
surrounding areas, but the absolute humidity, which shows the actual moisture in the
atmosphere is often higher. This would lead to higher moisture availability for thunderstorms
(Lee, et al., 1991). Interactions between thermal conditions and the availability of
condensation nuclei sometimes can also trigger convection and the development of
thunderstorm clouds. In this situation, there is a slight increase of cloudiness observed and,
consequently, more rainfall over or downwind of a city.
2.6 Distribution of Thunderstorms in Australia
In Australia, the distribution of thunderstorms is shown in Figure 2.4 (Bryant, 1991). The
greatest intensity of thunderstorms occurs in the tropics and along the Eastern Divide. The
highest incidence of thunderstorms occurs in the north, and, not surprisingly, the tropical
north of Australia experiences the highest number of thunderstorms. The number of average
annual thunder-days increase to above 80 thunderstorm days per year near Darwin (Barkley,
1934 and Crowder, 1995). A similar situation exists over south-east Queensland, where a
combination of summer tropical air and the proximity of the Great Dividing Range provide
ideal conditions for the breeding of thunderstorms (Colls and Whitaker, 1990). In the eastern
CHAPTER TWO Literature Review on Thunderstorm Rainfall 39
part of Australia, along and over the Great Divide the number of thunderstorm-days is more
than 40 days per year (Oliver, 1986). A generally low incidence of thunder days, between 10
and 15 thunderstorm per year, can be expected over the southern parts of Australia (Bureau
of Meteorology, 1989). It must be noted that 'as currently understood, there are still
considerable gaps in our knowledge of the occurrence of such events.
D UNOEH «0
OATS
• 0-^9
OATS
£ OATS
60-69
OATS
70-79
OATS
MORE THAN
60 DATS
Figure 2.4 Average annual thunder-days in Australia (After Bryant, 1991).
In Australia, as thunderstorms represent localised areas of instability, their distribution and
intensity are dependent upon factors which increase this instability and, as a result,
thunderstorm activity. Globally, the overall distribution pattern is influenced by three primary
elements: (1) the intertropical convergence zone; (2) solar heating of land masses and (3)
warm ocean currents. These conditions provide a favourable environment for thunderstorm
development during the entire year (Oliver and Fairbridge, 1987). O n a large scale, the
common processes that take place from the large scale synoptic weather patterns, for
example, a trough line, a low pressure area, or the passage of a cold front, can initiate
convection activity and thus thunderstorm rainfall (Kessler, 1986). O n a localised scale, the
initiation of thunderstorms may be caused by the local physical environment. For example,
conditions of instability may be reached when air is forced over a mountain, or the unequal
heating of the earth's surface which can appear as a 'trigger action'. In the following
sections the role of each the synoptic weather systems, and physiographic parameters in the
initiation of thunderstorms will be examined. Thus, the main aim is the understanding of
situations that create thunderstorms.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 40
2.7 Synoptic Patterns Associated with Thunderstorm Activity in Australia
This section concerns the large circulation systems that bring about the general conditions
necessary for thunderstorm occurrence in Australia. Over the Australian continent the
anticyclones which move eastwards across the continent and dominate the weather pattern
of the whole of country, are a predominating influence (Linacre and Hobbs, 1977).
Generally, the seasonal movement of pressure cells - anticyclonic highs and cyclonic lows
and associated troughs and fronts - determines the types of air that are drawn towards the
region (Tapper and Hurry, 1993). However, this movement is also related to rain-bearing
tropical or sub-tropical maritime air-masses and polar maritime airs which dominate over the
continent, in summer and winter respectively. Basic elements in the pattern of pressure
distribution and associated airmasses over Australia in summer are shown in Figure 2.5.
Figure 2.5 Basic elements in (a) the pattern of pressure distribution and of associated (b) airmasses over Australia in summer (Tapper and Hurry, 1993).
Thunderstorm formation which has been associated with instability of air-masses - in relation
to various physical environment (for example, topography) impacts - can also be related to
the atmospheric triggering mechanisms (Ludlam, 1962, 1963). Investigation of the
respective roles that may have been played by each atmospheric process in the initiation of
thunderstorm activity is a very complex task. Meanwhile, in Australia, literature relating the
meteorological situation (synoptic patterns) directly to the occurrence of thunderstorm
activity may be categorised into four main classes: 1) Tropical cyclones and monsoon
depressions of northern Australia, 2) Eastward moving troughs and lows, 3) Frontal activity,
4) Upper atmospheric activities.
1) Tropical cyclones and synoptic-scale depressions are known as major weather-related
causes of thunderstorms along the northern coast of Australia. They are generally small
intense low pressure cells, often less than 100 k m in diameter, associated with stormy
CHAPTER TWO Literature Review on Thunderstorm Rainfall 4J_
weather conditions, extremely strong winds and heavy rainfall (Tapper and Hurry, 1993).
These tropical cyclones are defined as intense cyclonic storms that originate over warm
tropical seas and develop from December to M a y in this region. They bring intense rainfall to
much of northern Australia during this time.
Barkley (1934) mentioned that a majority of storms in the tropics occur just in those
portions of the north-east and north-west coasts that are subject to the incidence of tropical
cyclones. For example, Willis Island off the North Queensland coast, records many
thunderstorms per annum. Also, between Cooktown and Mackay in Queensland, and from
Condon to Onslow in Western Australia severe thunderstorms are recorded each year.
Occasionally these cyclones can move to the south or south-east of Australia and cause
severe widespread rainfall (Whetton, 1988). In western Australia, the cyclones which reach
these coasts are usually almost circular storms with well marked discontinuities of pressure
in the air streams such as would produce thunderstorms.
(a) (b)
Figure 2.6 Represents a pre-frontal trough (a), and a line storm associated with an eastward moving trough (b) over south-eastern Australia (Tapper and Hurry, 1993).
It was also suggested that some of the thunderstorms over northern Australia may be
associated with the monsoon depressions which are responsible for a significant proportion
of the seasonal rainfall (Tapper and Hurry, 1993). These systems are normally located over
land, but may be intensified when they move over warm ocean waters. Although monsoon
depressions are less organised than tropical cyclones, they can often be strong enough to
dominate in the region. According to Barkley (1934) along the north and north-west coasts,
where monsoons dominate, the maximum number of thunderstorms can be expected.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 42
2) Eastward moving troughs are depressions which form in a low pressure trough. They are
not a type of thunderstorm, but the most frequent type of thunderstorms which experienced
over Australia (Bureau of Meteorology, 1989) may occur in them. Occasionally, ahead of a
trough line or east of a low pressure area, there are areas of uplift. This uplift in itself may be
sufficient to trigger a thunderstorm. A trough line moving eastward divides the cooler, drier
southern maritime air from the warm, moist tropical maritime air-masses (see Figure 2.6).
This trough line can enhance the vertical motions just ahead of the trough which leads to
thunderstorm development.
Plate 2.1 Shows a cold front off the South Coast of New South Wales.
In summer time, when the troughs exist between high and low pressures, the moist onshore
trade winds, orographically uplifted, bring substantial cloud and rainfall to the coastal
regions. For example, on Sunday, 11 January 1981, several population centres in Western
Australia experienced destructive squalls associated with one or more severe thunderstorms.
According to press reports, thunderstorms were associated with an eastward moving trough.
Such a trough is a familiar feature of Australian summers (Kingwell, 1982). Intense
downpours were experienced with 24-hour total falls of 34 m m reported during
thunderstorm activity (Bureau of Meteorology, Perth, 1981). Also, analysis of thunderstorm
activity over Tasmania on Monday, 9th, December, 1985, showed that a trough line over
CHAPTER TWO Literature Review on Thunderstorm Rainfall 43
Tasmania was responsible for widespread thunderstorm activity with high rainfall rates of
greater than 120 m m per hour (Jessup and Hughes, 1991).
3) Cold fronts are found at the boundary between adjacent air-masses. The advancing edge
of cold fronts sharply undercuts warmer air, forcing it to rise. This lifting mechanism can
cause rapid instability resulting in thunderstorm activity with considerable rainfall along the
cold front (Hutchinson, 1970; Linacre and Hobbs, 1977). For example, on 22nd December
1990, the study of synoptic patterns showed that thunderstorms which affected Melbourne,
were formed in warm moist air, ahead of the approaching cold front moving eastward
(Treloar, 1991). Thunderstorms may develop if the air ahead of the front is conditionally
unstable and sufficiently moist (Plate 2.1). These thunderstorms tend to occur in long lines
parallel to the front. Sometimes, a continuous wall of thunderstorms may stretch over
hundreds of kilometres (Tapper and Hurry, 1993). Generally, fronts can introduce
thunderstorm activity, particularly over the south-west of Australia where they are c o m m o n
synoptic weather systems. However, it was suggested by Bryant (1991) that the passage of
cold fronts plays a minor role in Australia, compared to the United States, in forcing
thunderstorm development.
4) Upper atmospheric activity has also been associated with thunderstorm development over
Australia. In the upper atmosphere moving air may somehow affect the lower air currents
because of complex dynamic process or in response to the thermodynamic activities into the
atmosphere. For example, when an area of upper air divergence forms, surface air may be
drawn upwards to replace the diverging upper air. Mclnnes et al. (1992) found that upper-
level cold pool systems are usually associated with intense thunderstorm activity. For
instance, a widespread thunderstorm activity over the south-east of Australia on the 1st
March, 1967 and a localised thunderstorm with more than 50 m m rainfall along the east
coast, on 9 February, 1990 were both related to upper shear line and cut-off low
development. In these situations, warm air advection, in conjunction with the splitting of the
jet stream, were also found to be the most significant factors in thunderstorm development
(Mottram, 1967). Occasionally, thunderstorm activity can also be initiated by divergence in
the upper troposphere causing local convection and representing a localised area of
instability (Bryant, 1991).
In short, during the past decades, it has been stressed that, in Australia, thunderstorms can
be introduced by a number of above-mentioned synoptic weather systems which are known
to be primarily responsible for many thunderstorm developments. These synoptic weather
systems also occasionally provide favourable conditions for the widespread development of
severe thunderstorms and associated rainfalls. Essentially, the initiation of thunderstorms in
conditionally unstable air requires some initial uplift - referred to as 'trigger action' - which
CHAPTER TWO Literature Review on Thunderstorm Rainfall 44
may be caused by one or by the accompanying of these synoptic weather systems,
synchronously.
2.7.1 Weather Systems and Thunderstorm Activity in NSW
Weather conditions over south-eastern Australia are usually dominated by the generally
eastward movement of successive high pressure systems. Troughs or cold fronts form
between the highs and are usually associated with low pressure systems over the southern
ocean (Bureau of Meteorology, 1991a). However, these normal weather patterns can also be
substituted by other atmospheric conditions leading to the development of thunderstorms. In
the past, thunderstorm development over N S W has been associated with the following
synoptic weather patterns.
Firstly, it was suggested by Williams (1991) that the above-mentioned common synoptic
weather situations can introduce thunderstorm activity over N S W . For example, in summer,
when the sub-tropical ridge is generally to the south of N S W with centres of high pressure in
the Tasman Sea and the lower pressures over the continent, the predominant humid winds
from the Tasman or Coral Sea can lead to afternoon and evening thunderstorms, particularly
along the ranges or near the trough. At this time the inland trough - sometimes is known as a
'dry-line' - is a boundary between humid air from the east and dry air from the west. This
dry-line advances and retreats with heating and cooling of day and night and is known as a
line of cumulus cells (as a meso-scale convection system). In this situation, high
temperatures and humid air can enhance thunderstorm activity. O n average, a large
proportion of N S W summer rain comes from these thunderstorms which are occasionally
widespread, if there is some upper disturbance present.
Secondly, in the Australian east coast, including the NSW, thunderstorms can be created by
cut-off lows which are described as synoptic-scale cyclonic low-pressure systems (Mclnnes
et al., 1992). These cut-off lows have been long recognised as major sources of severe
weather in south-east Australia (Holland et al., 1987). They are formed when a low pressure
system - normally in the upper air - becomes isolated from the main low pressre system by a
high pressure system that is ridging rapidly east-wards. This process frequently occurs in the
south-east of Australia, particularly along the east coast. The included figure shows a sample
of cut-off low in the region (Figure 2.7).
CHAPTER TWO Literature Review on Thunderstorm Rainfall 45
Figure 2.7 Shows a sample of cut-off low in the region which reaches maximum cyclonic development off the southern N S W coast on 11 July 1987 (From Tapper and Hurry)..
Although there is no accepted general terminology to describe these systems, for
convenience cut-off lows were classified into two groups namely: coastal lows and blocking
lows. Generally they are synoptic-scale systems extending from the surface to the lower
stratosphere. Such systems have been recognised as a major source of severe weather in
south-east Australia. W h e n the cut-off has no manifestation at the surface, it is referred to by
forecasters as an 'upper-level cold pool'. Such systems are usually accompanied with intense
thunderstorm activity and often could be associated with widespread rainfall and flooding
(Hopkins and Holland, 1994).
The southerly Buster is also an unusual synoptic situation which is the name given to an
intense form of cold front that occure along the coast of N S W (Williams, 1991). These
fronts, which often herald the 'cool change' for Sydney, occur mainly in the spring and
summer months also. The most spectacular casses occur at the of a hot summer day. The
wind turns suddenly from the north to the south and blows with some force, with a change in
air temperature of 10 degrees or more. There is not a great deal of cloud associated with the
initial onset. The intensity of southerly busters and their special characteristics appear to be
closely linked with topographic features (Reeder and Smith, 1992). However, the influnces
of these unusual synoptic phenomenan on the nature of thunderstorms have been not
documanted.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 46
(d) DISTANCE FROM RADAR (km)
Figure 2.8 Schematic of the life cycle of the precipitation area of a M C C s as it would appear on rader in horizontal and vertical cross sections during (a) formative, (b) intensifying, (c) mature, and (d) dissipating stages (From Leary and Houze, 1987).
In NSW, it has also been found that meso-scale convective complexes (MCCs) can
introduce thunderstorm activities. This phenomenon was first identified in the United States
(Maddox, 1980). M C C s are a particular class of meso scale (with length scale between 250 -
2500 k m with duration > 6 hours) convective weather systems that occur over the central
plains of the U S A . These systems are generaly much larger than the individual thnderstorms
and lines. In fact, they represent the largest member of the family of convective clouds which
produce a large proportion of the earth's precipitation and thus are important from a
climatological standpoint. M C C s occur in a variety of forms, however, they have several
features in common. For example, they all exhibit a large, contiguous area of precipitation,
which may be partly stratiform and partly convective (Houze Jr., 1993). The precipitation
CHAPTER TWO Literature Review on Thunderstorm Rainfall 47
area of a MCCs exhibits a characteristic life cycle, which is illustrated schematically in Figure
2.8.
In the regional scale a few cases, which occurred over NSW (for December, 1979,
December, 1983 and January, 1990) have been documented by James (1992). These
thunderstorm-producing systems occur when an instability over a large region is released.
Such a self-sustaining system can introduce a heavy rain event or can develop other severe
weather systems. A relatively heavy thunderstorm rainfall can develop, lasting for a
considerable time (more than 6 hrs) and often reaching a peak overnight (James, 1992).
However, it appears that the N S W M C C s are less frequent and generally on average smaller,
have a shorter duration and are less efficient in producing precipitation than those
documented over the United States. Other conditions necessary for M C C s development
include a broad area of high humidity and instability, and the presence of convergence in the
lower atmosphere (Hicks, 1984 and Wilson and Ryan, 1987).
In addition, squall lines were suggested by a number of authors (for example, Williams,
1991) to be an important, but infrequent phenomena in causing thunderstorm activities in
N S W . Williams (1948) and Tepper (1950) have discussed in detail the most notable features
of a squall line. They have attributed to it the following meteorological characteristics: a line
of active thunderstorms moving rapidly; brief but sharp pressure changes; very strong wind
gusts; brief wind shifts, and abrupt temperature falls. Williams (1991) noted that a squall line
is frequently parallel, or nearly so, to a surface front. W h e n thunderstorms develop ahead of
a cold front or a trough-line, conditions are often such that the storms develop along a line
and propagate as a linear system. The line of storms can be several hundred kilometres long.
These conditions were all relevant to the situation on the 21st January, 1977, and the general'
weather conditions appeared to have been favourable for squall line formation.
Consequently, the line of storms developed ahead of a cold front moving slowly across
south-eastern Australia. It was first observed on Sydney Airport radar 55 k m to the south
west of the airport moving rapidly north eastwards. These thunderstorms did extensive
damage to property, one person was killed, and twenty three injured (Morgan, 1979b).
Finally, tropical cyclone and other associated weather features, for example troughs, are also
responsible for a few thunderstorms in the region. From time to time, across the coasts of
N S W , these systems move southwards and cause widespread thunderstorm activity in the
Sydney region. For example, in February 1990, considerable thunderstorm activities were
introduced by the influence of a tropical cyclone "Nancy" and related troughs over the N S W
and Sydney region especially from 7th to 11th. Associated thunderstorms from this activity
produced widespread heavy rainfall over the Sydney region causing flooding in the
Metropolitan areas. Occasionally, Tasman Sea lows can move to the south-west toward the
Sydney region and cause severe thunderstorm activity. For example, between 5th and 12th
CHAPTER TWO Literature Review on Thunderstorm Rainfall 48
November, 1984, Sydney had more than 550 mm of rain from thunderstorms. Earlier, in
February 1984, under similar synoptic conditions to November, 1984, a centre, in the coastal
ranges 60 kilometre south of Sydney, recorded 515 m m of rain in just six hrs and 706 m m in
12 hours, setting new Australian records (Bureau of Meteorology, 1984).
In summary, in producing large thunderstorm activity throughout the State, it could be
suggested that the above-mentioned regional synoptic systems are able to make the
atmosphere unstable if there is enough humidity and there is a suitable upper wind structure,
and finally, if there is a lifting mechanism to initiate convection activities. Generally,
positions of highs and lows over N S W , and also moving troughs and fronts across the State
can introduce thunderstorm activity, and these thunderstorms are occasionally widespread, if
there is some upper disturbance present.
2.7.2 Thunderstorm Development in the Sydney Region
Thunderstorms in the Sydney region can be initiated by local disturbances which occur as
warm, moist air is lifted several kilometres into the atmosphere, producing lightning and, at
times, strong winds, hail, and torrential rain (Colquhoun, 1972; Mitchell and Griffiths, 1993)
(see Plate 2.2).
Nearly all the synoptic weather systems which can introduce thunderstorm activity over
N S W , could probably also trigger more widespread thunderstorms over the Sydney region
by several mechanisms such as fronts or troughs. More recently, investigations into the
occurrence and distribution of thunderstorms within the Greater Sydney Region were
undertaken, in relation to the meso-scale synoptic weather patterns. Three major research
works which appeared are worthy of emphasis here. Therefore, this part of the literature is
fundamentally based upon the following most recent investigations.
The first research was done by Matthews (1993) who examined the spatial distribution and
movement of thunderstorms (from 1965 to 1989) within the region using radar facilities.
Thunderstorms were found to occur in a number of synoptic situations which have been
classified into both frontal and non-frontal systems. In the frontal category were pre-frontal
troughs, pre-frontal and post-frontal systems, while in the non-frontal category were the
inland trough, inland low, offshore low and high in the Tasman Sea. The study of seasonal
variation of thunderstorms distribution has indicated about 75 per cent of thunderstorms
occurred in warm months - spring (September - November) with 30 per cent, and summer
(December - February) with 45 per cent. This study also found that spatial thunderstorm
distributions for the above-mentioned synoptic classes were significantly distinct, physically
plausible and to some extent internally consistent.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 49.
Plate 2.2 Displays thunderstorm development over the Sydney region.
Figure 2.9 presents the anomaly maps of thunderstorm occurrence in the Sydney region,
from Autumn to Summer seasons. Generally, in winter, thunderstorms occur in the east of
the region, particularly over the Tasman Sea. In contrast, in summer they mainly occur over
the land, especially over the elevated parts in the west of the region.
In addition, it was investigated by the above-mentioned study that thunderstorms tracks
within the Sydney area are almost always from a westerly direction - starting mostly in early
afternoon over higher terrain in the west and reaching over the east of the Sydney region
(near and over the City and the Tasman Sea) in the late evening (Figure 2.10). Although
there is a general eastwards movement from the early afternoon to the early evening in the
diurnal distribution of thunderstorms, there are still isolated thunderstorm occurrences that
are independent of time.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 50
Anomaly plot [or autumn Anomaly plot lor winter
Anomaly plot for spring Jl rTTrTWTfTnfTrT
Anomaly plot for summer
Figure 2.9 Presentation of the anomaly maps - using the Terminal Area Severe Turbulence (TAST) radar data - from Autumn, Winter, Spring, and Summer in the Greater Sydney Region. Note that the shaded areas represent areas of low occurrence of thunderstorms with respect to the all years distributions (from 1965-89), while the unshaded areas represent greater occurrence than the all years distribution (After Matthews, 1993).
Another point of interest was that thunderstorms tracks were found to be largely
independent of the synoptic systems (the surface pressure patterns). Matthews (1993) has,
therefore, assumed that thunderstorm movement may be related to the dynamics of the
atmosphere at all levels, particularly the upper-level atmosphere. Thunderstorms occurrences
are also assumed to be related to main topographic factors of the region. This latter point
was again emphasised by M a y (1995, personal conversation). In spite of the topographic
controls upon the distributions and movement of thunderstorms, figures 2.9 and 2.10
suggest that thunderstorms also follow the sources of heat and moisture in the region in their
seasonal and diurnal distributions.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 5/
Figure 2.10 Diurnal distribution of thunderstorm occurrence for the different time periods (local time) in the Greater Sydney Region. Shaded areas represents areas of low occurrence with respect to the all years distribution (1965-89) (After Matthews, 1993).
In the Sydney region, Speer and Geerts (1994) have more specifically presented a synoptic
climatology of flash-floods-producing storms for the period 1957 to 1990. 94 'flash flood'
events - many of which were associated with thunderstorms - have subjectively been
classified into four synoptic-meso-scale groups: 1) Easterly troughs; 2) Pre-frontal systems;
3) Lows; and 4) Post-frontal systems.
1) Easterly troughs events are known to be the most common (39 per cent), and usually the
longest in duration due to the quasi-stationary nature of the surface trough and upper-level
trough. T w o obvious synoptic types within this category are known. The first type occurs
when the surface trough moves east, from a quasi-stationary position mostly on the western
side of the ranges. The second type, referred to as the offshore trough, occurs when a quasi-
CHAPTER TWO Literature Review on Thunderstorm Rainfall 52
stationary surface trough in the easterlies is located just off and parallel to, the coast.
Shanahan (1968) found that associated thunderstorms with these systems can move over the
Sydney region and they can produce heavy rainfalls causing flash floods. Figure 2.11 gives
examples of easterly troughs: a) an onshore trough occurred on 25 March 1968, one and a
half hours before flash flood rainfalls, and b) an offshore trough occurred on April 1985 two
and a half hours before the flash-flood rainfall.
2) Pre-frontal systems (31 per cent) occur typically in a north-westerly flow with a mid-
tropospheric short wave to the west. They are typical of late spring or summer and are more
common in the afternoon. These systems are usually associated with intense though brief
bursts of precipitation. Speer and Geerts (1994) distinguished two types in the pre-frontal
category. The first is characterised by a meso-scale surface low ahead of the main front. The
second type, with a pre-frontal surface trough through Sydney is more common. Associated
thunderstorms with these systems develop in a convergence zone between north-east and
north-west winds at the surface and low levels. They are generally common features in
eastern N S W over the warmer months producing thunderstorms when high pressure in the
Tasman Sea has been advancing moist north east winds. These systems may persist for up to
several days introducing thunderstorm activity with heavy rainfalls and, as a result, flash
floods in the region. Figure 2.11 shows two examples of pre-frontal systems: c) 14
December 1971, two hours before the flash-flood rainfall, and d) 9 March 1989, before
flash-flood rainfall.
3) Lows - which developed from low pressure systems - the majority of events (17 per cent)
classified as lows, evolved from a depression developing on an old front in the Tasman Sea.
The coastal south easterly winds provided low-level moisture flux. Based on location of the
low with respect to the Sydney region, and as for easterly troughs, two types were known.
The first type is characterised by a slow-moving single or complex system of lows to the
west of Sydney over south-east of Australia, and was referred to as an onshore low which
causes 3 per cent of flash floods. The second type occurs with a single or complex low
pressure system over the adjacent Tasman Sea with a ridge along the coast, and was referred
to as the offshore low (14 per cent). For example, on the 23nd November, 1979, such
systems passed over Sydney and the flash-flood was produced from thunderstorms as the
surface low moved across Sydney. Generally, lows exhibit the weakest diurnal and seasonal
variation. Figure 2.11 gives an example (e) of the offshore low, case of 25 October 1960.
4) Finally, Post-frontal systems are rare (13 per cent) and strongly modulated diurnally and
seasonally, towards the warmer periods. They are associated with a southerly change,
aligned roughly parallel to the Great Dividing Range. These synoptic categories were also
known to be important in producing some of thunderstorms, and as result, introducing flash
CHAPTER TWO Literature Review on Thunderstorm Rainfall 53
floods in the Sydney Metropolitan area. Such thunderstorms are the most common
phenomenon, especially in spring and summer. A n associated synoptic chart for this class is
given in Figure 2.11 which shows an example of a post-frontal system (f) causing flash-flood
on 18 March 1990.
Figure 2.11 Presents examples of six meso-scale synoptic weather systems causing thunderstorm activity in the Sydney region. The thick broken line indicates the trough on the mean sea level pressure chart (After Speer and Geerts, 1994). See text for details.
Most recently, the spatial distribution of deep convection in the Greater Sydney area was
examined by Matthews and Geerts (1995), using an archive of 25 years of radar data located
at Sydney's Mascot Airport. This research studied a sets of different characteristic
thunderstorm distributions in Sydney under different synoptic conditions. It was found that
thunderstorms occur in a number of distinctively different synoptic settings, both frontal and
non-frontal. Three types of frontal settings (pre-frontal trough, pre-frontal and post-frontal),
and also three types of non-frontal situations (inland trough, offshore low and offshore high)
were distinguished. Figure 2.12 gives six examples of selected mean sea-level pressure
( M S L P ) patterns in which thunderstorms occurred.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 54
Figure 2.12 Selected M S L P charts illustrating the six synoptic classes: (a) pre-frontal trough (6/5/85 at 0200 U T C ) ; (b) pre-frontal (16/12/85 at 0100); (c) post-frontal (9/1/87 at 1800); (d) inland trough (8/1/85); (e) offshore low (20/6/85 at 2300); (f) offshore high (14/1/86 at 0700). Note that only the last two digits of M S L P in hPa are shown (After Matthews and Geerts, 1995).
Although there are some distinguishing differences between the two above-mentioned
classifications, the differences are of little importance, it can be accepted that all synoptic
weather systems are able to introduce thunderstorm activity and therefore introduce their
associated rainfalls over the region. In one step further, Matthews and Geerts (1995)
compared the findings with data obtained from much more recent thunderstorm detecting
facilities, such as, a lightning network and a new automatically radar system, as independent
sources. They constructed a thunderstorm density model based upon the new radar data for
above-mentioned synoptic conditions. Because of their importance, the thunderstorm density
and lightning density maps are given in Figure 2.13 and Figure 2.14 respectively.
The results (normalised storm probability and density maps) indicate that the distribution of
thunderstorm activity is not the same for all synoptic situations. Although there are distinct
differences in synoptic conditions by which thunderstorm occur, general patterns can be
understood in terms of low-level flow, topography and land-sea differences.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 55
Figure 2.13 Thunderstorm density model based on new radar echoes, under the following synoptic situations: (a) pre-frontal trough; (b) pre-frontal; (c) post-frontal; (d) inland trough; (e) offshore; (f) offshore high (After Matthews and Geerts, 1995).
The more general findings of these investigations indicate that thunderstorm activity is
stronger near the coastline, especially the southern coastline and the northern beaches, due
to the coastal topography. Places such as the Hornsby Plateau, Illawarra Plateau and the
Blue Mountains are also subject to the highest number of thunderstorms. In winter,
thunderstorm cells are generally more c o m m o n offshore (over the coastal areas and over the
Tasman Sea). In summer, on the other hand, thunderstorms are relatively more c o m m o n
over land, except over the Southern Tablelands. Matthews and Geerts (1995 p:133) suggest:
'In many cases for example offshore high, topographically controlled, thermally forced convergence is a primary trigger of convection systems in the region.'
In the Sydney region, although thunderstorms are known to be important components of
many weather systems such as active fronts, troughs or squall-lines (Morgan, 1979b), in turn
thunderstorms can also be initiated by local disturbances mainly caused by climatic-
environmental factors (Linacre and Hobbs, 1977). Broadly speaking, thunderstorms may be,
CHAPTER TWO Literature Review on Thunderstorm Rainfall 56
with caution, classified into two general categories in terms of their origination and
organisation; 1 - the air-mass thunderstorms (thermal) which are more likely to have an
environmental origin, and 2 - big and multi-cell or supercell (Dickins, 1994) thunderstorms
(dynamically) which are largely of synoptic derivation (Alford, 1994). Simply, it can be
viewed that the region can be overwhelmed by both systems during the thundery seasons of
the year.
Figure 2.14 Lightning density for single thunderstorm events based on data from the N S W lightning
detection network, in units of number of strikes per km^ per event, under the following synoptic situations: (a) pre-frontal trough (22/12/92); (b) pre-frontal (09/11/92); (c) post-frontal (23/2/93); (d) inland trough (06/01/93); (e) offshore low (06/12/92); (f) offshore high (23/12/92). After Matthews and Geerts (1995).
In brief, the data from Sydney thunderstorms during past decades may indicate that
thunderstorm development is the result either of the larger synoptic weather systems or,
more specifically, synoptic-meso-scale systems. Both systems are potentially able to provide
an unstable environment in which thunderstorms occur and track. Although, development of
thunderstorms over the Sydney region varies from month to month and reflects the overall
impact of above-mentioned synoptic weather patterns, they may also reflect the effects of
climatic factors (discussed in Section 4) and physical environment parameters by which
CHAPTER TWO Literature Review on Thunderstorm Rainfall 57
thunderstorms may be developed or controlled. In the next section, the role of physiographic
parameters will be examined literally .
2.8 Sydney's Physiographic Parameters and Thunderstorm Rainfall
In the Sydney region there is some evidence indicating the fact that spatial distribution of
rainfall can be influenced by some physiographic parameters such as elevation and distance
from the sea. For example, Linacre (1992) stressed the effect of landform on rainfall
distribution patterns in the Sydney region by showing that isohyets closely paralleled the
height contours. However, in terms of convectional rainfall, little, if any work has been done
relating thunderstorm rainfall to physiographic parameters such as topography, proximity to
the sea and urban centres.
Topography has been suggested to be an important factor in affecting thunderstorm
development in the N S W coastal areas (Sumner, 1983a). In terms of topographic effects,
Williams (1991) mentioned that places along the Illawarra Escarpment experience very high
orographic rainfall, illustrating the effect of local topography and exposure on rainfall. For
example, in an area of more rugged terrain, such as the Illawarra escarpment, just south of
Sydney, intense rainfalls with much longer duration causes flash floods which may be
correlated with thunderstorms (Foreman and Rigby, 1990). In 1983, Cox supposed that in
the Illawarra there are pronounced differences in rainfall totals between wet and dry years,
but the distribution pattern remains quite stable because of the topographic effects.
In the north-west of the Sydney region, it was suggested that, one of the most regular and
predictable types of orographic rain may occur, particularly in warm season conditions, near,
over and adjacent to the Blue Mountains (Gentilli, 1971). This happens, because the general
meridional alignment of this relief along the N S W eastern margins causes an orographic
uplift of the moist air streams which is clearly reflected in the amount of rain. The amount is
sharply increased on the windward side, and gradually decreased on the leeward side of
mountains. Also, it may happen because the daily heating of the hillsides generates warm
upslope winds which continue rising after reaching the mountain ridge-top and trigger deep
convection. These convective clouds can produce thunderstorm rainfall in the afternoons
over the peaks, or downwind if there is cloud drift. This behaviour is shown clearly in the
results of some research (Morgan, 1979a) and the daily thunderstorm patterns of the Sydney
region.
However, these mechanisms may also produce statistically verifiable night-time
thunderstorm activity over the City and the coast line. It can be supposed that thunderstorms
build over the mountains and then travel to lowland areas. For example, during the late
afternoon and evening of the 10th November 1976, thunderstorms developed over elevated
terrain to the south-west of Sydney and then moved over the urban area (Morgan, 1979a).
CHAPTER TWO Literature Review on Thunderstorm Rainfall 58
Rainfall from the thunderstorms was very heavy in some parts of the Metropolitan area.
Both the highest rainfall and rainfall rate were reported from Observatory Hill, where 30 m m
of rain fell in 11 minutes, a rate of 164 m m per hour (Bureau of Meteorology, 1976).
The topography of the coastal region also plays an important role in enhancing surface
convergence near the coast. Holton (1992) found that if orography slopes locally upward in
a downstream direction, vortex lines in the lower layer are compressed and the flow in the
lower layer must move towards the equator to conserve potential vorticity. Also, Leslie et
al., (1987), in modelling east coast cyclogenesis, suggested that without topography no such
convergence and convective concentration occurs and the cyclone development is retarded.
In addition, Speer and Geerts (1994) using radar data, presented examples in which quasi-
stationary thunderstorm cells developed over relatively high topography in the Sydney
region. They concluded that topography would, therefore, aid convective systems to
produce higher rainfall totals over higher ground. More recently, using data recorded by the
L P A T S system (Laudet et al., 1994) it was found that the spatial distribution of lightning
(associated with thunderstorms) is closely related to the topography of the region. The effect
of topography was pronounced as flash data showed marked concentration over the
mountains of the region particularly on and east of the central part of Range. The results
support the concept that topography is a very important physiographic parameter in
controlling thunderstorm occurrence.
At the same time, there is some evidence which shows that coastal areas - proximity to sea
as an important physiographic parameter -can affect the distribution of thunderstorm rainfall
in the Sydney region. Speer and Geerts. (1994) found that the south-easterly winds, oriented
by the coastal ridging, can enhance the low inflow of moisture to the storm. Also, in some
postfrontal cases, the coastal ridging is known to be responsible for the stationary
convergence zone causing thunderstorms to propagate over the coastal areas (Speer and
Leslie, 1994). It was found that, the interaction of fronts with coastal dividing ranges in the
presence of more humid air to the east of the ranges, can lead to severe thunderstorms along
the south coast of N e w South Wales (Reeder and Smith, 1992). Another important point is
that thunderstorm rainfall of longer duration than 6 hours was recorded in areas close to the
coast. (James, 1992). For example, during a series of thunderstorms on 10th and 11th
March, 1975, thunderstorms were extended over the coastal areas of the Metropolitan and
Illawarra districts and intense rainfalls were recorded in costal areas (Armstrong and
Colquhoun, 1976). Meanwhile, some researchers such as Abbs and Physick (1992) believe
that the topography of the coastline has an extra important controlling effect upon
thunderstorm activity in the region.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 59
Finally, it is more likely that, in the region, urban areas affect the distribution of
thunderstorm rainfall to some degree. Although there is no data correlating the thunderstorm
rainfall patterns to Sydney's environment directly, Linacre (1992) mentioned that there is
more rainfall over the City. Investigation of thunderstorms, over Sydney during the past
years, has shown that a sequence of thunderstorm cells originating over the elevated terrain
may, subsequently, enter the severe stage prior to moving (tracking) over the City
(Matthews, 1993). The resultant heavy rainfall, large hail and strong wind gusts can cause
extensive damage throughout the Sydney Metropolitan area. Speer and Geerts (1994)
studied some of the heavy rainfall from thunderstorms during the period 1957 to 1990. They
showed that the slow movement of storms over Sydney causing flash floods, can produce
much more rainfall over the Metropolitan areas.
An important question is why the City is a preferred (favoured) area for more
thunderstorms. In recent years, Tapp and Skinner (1990) and Tapper and Hurry (1993)
examined some aspects of urban climatology and have suggested that in many large cities in
Australia, noticeable heating of air over the urban centre occurs relative to adjacent suburban
and rural areas at night, particularly in winter. Schwerdtfeger (1982) gave evidence (a map)
showing the heat island effect over central Melbourne on a winter night. The warmest part
(8.9°), took in the north-eastern end of the C B D , the western part of Fitzory, and much of
the suburb of Carlton. More recently, Crowder (1995) in comparing rainfall distributions
over Sydney and Melbourne, says that rainfall over the Sydney Metropolitan area varies
from less than 700 m m near Campbelltown to more than 1600 m m on the coast just north of
Stanwell Park. Also, there are wet spots over 1400 m m near Katoomba and southeast of
Hornsby and another one over the centre of the Sydney ( C B D ) (see Figure 14). H e
concluded that Sydney is prone to severe thunderstorms and associated very heavy rainfalls.
Although in Sydney there is no measured data for the urban heat island over a long time
span, the effect of such phenomena upon rainfall distribution is highly reasonable. Several
authors have over the past years approached the modification of Sydney's atmospheric
boundary layer by thermal and mechanical turbulence. For example, Fitzpatrick and
Armstrong (1973 p:18) in a study of effect of the urbanisation on climate in the Sydney area
wrote:
'Although the maps of mean maximum and minimum temperature do not reveal any clearly identified effect of the urbanisation, this cannot be taken to indicate that such effect must be small. All investigations of temperature have shown that one heat island effect over cities is best developed under calm, clear sky conditions that favour maximal daytime heating and the development of strong temperature inversions at night'.
The detailed spatial pattern of temperature within the Sydney area was carried out by
McGrath (1971) using mobile temperature recording equipment at about 1.00 am. on April
CHAPTER TWO Literature Review on Thunderstorm Rainfall 60
24th, 1971. He found a difference of about 5 °C between the City and the outlying rural
areas. Although he did not attribute the difference found to a true heat island, a steep
temperature gradient was observed between closely built-up areas and those having
extensive parkland and open spaces.
In another study, Kemp and Armstrong (1972) examined temperature trends at Observatory,
Hill, Sydney for the period from 1859 to 1971. They concluded that there has been no
change in maximum temperatures over the past years. However over the same period they
indicated that minimum temperatures increased by about 0.6 C - an increase which may
possibly be explained as a 'heat island' effect resulting from increasing industrialisation of
the City.
A study has also been made by Kalma et al. (1973) in spatial and temporal aspects of energy
use by domestic, industrial and commercial sectors and in transport, in the Sydney Statistical
Division (SSD), in 1970. They found that there is a great spatial variation in energy use, and
it was estimated that intensity of energy use ranged from 1 * 1 0 1 0 BTU/sq mi/yr in rural
areas such as Camden and Windsor to 143 * 10*0 in Parramatta and 382 * 1010 in the City
of Sydney. This study indicated that energy use on average days in July was about 20 per
cent greater than on average days in January. In this research they also gave evidence
showing the intensity of annual energy use across the Sydney region. In areas such as the
C B D and surrounding sectors, energy use ranged between 300 and 400 * 10*0 B T U
ye/sq/mile. In contrast, in suburban areas further out, the rate was a minimum (1 * 10 ^
B T U ye/sq/mile) as shown in Figure 2.15. This high spatial variation in artificial heat
generation of Sydney region may be correlated to the urban heat island phenomena.
Examples of estimates of artificial heat released from various urban areas (in 1970s) were
given in Bridgmam (1990). It was expected that artificial heat released would be increased
with the growth of population and density in many cities around the world, including Sydney
specifically in central urban area. Identification of the magnitude of these artificial energy use
is obviously of fundamental importance in understanding of heat island progress over the
Sydney region (Kalma and Byrne, 1976).
CHAPTER TWO Literature Review on Thunderstorm Rainfall M.
Figure 2.15 The intensity of annual energy use in the Sydney region (After Kalma,, et al., 1973).
In addition, Linacre and Edgar (1972) reported evidence (visibility and suspended particulate
as atmospheric pollution) on the surface configuration of Sydney's heat island. Their work
has given improved evidence of the influence of urban development on the climate of the
Sydney area. For example, they gave a typical isotherm map showing the temperature
difference within the City. In another work by Kalma (1974) it was again shown that, in the
Sydney region, considerable spatial and temporal variation in energy use exists. These
studies provided evidence that artificial heat generation is a significant factor in energy
exchange processes over the urban environment when compared with the surrounding less
build-up areas. These studies also discussed the primary processes involved in the formation
of the urban heat islands and they generally concluded that artificial heat generation in the
Sydney region was largely responsible for the downwind temperature increase over the City.
This effect may be maximised by high density building within City centres and may create a
heat island (Davey, 1976) with greater cloudiness and, as a result much more rainfall.
Undoubtedly under specific conditions, for example, calm weather conditions, especially in
summer, the influence of urban aerosols on cloud condensation nuclei or ice nuclei also
appears to be an important factor in the modification of the City's atmosphere. In an
extensive study of the climatology of air pollution by Carras and Johnson (1982) and
Literature Review on Thunderstorm Rainfall
Leighton and Spark (1995), it was found that the Sydney's climate is subjected to serious
pollution events and there are different sources of pollutants emitted to Sydney's atmosphere
(Moss, 1965). Industrial and commercial activities, including the usual forms of transport,
are amongst the important man-made local sources of emission (Linacre, 1970). Although
trends of air pollution levels in Sydney since 1950 showed a general decrease in traditional
pollutants, such as dust and sulfur dioxide, because of less use of coal as a fuel in large
boilers and for transport, new emerging pollutants including lead, hydrocarbons, and oxides
of nitrogen have become of increasing concern in the Sydney region (Paine et al., 1988) (see
Figure 2.16).
HYDROCARBONS EMITTED PER 3-2km GRID SQUARE |kg/h|
Figure 2.16 Spatial distribution of nitrogen oxides emissions from all sources in the Sydney region (After Carras, et al., 1982).
All above-mentioned studies showed a great spatial variation in the distribution of some
pollutants, such as nitrogen oxides emissions from all sources in the atmosphere of areas
located within the City, which has much more pollution than the suburbs. The areas such as
C B D , and south of Parramatta river were among the worst. In 1992 once again, the problem
associated with the polluted atmospheric environment was highlighted by Taylor. H e found
that as the Sydney region expands, the air pollution levels increases, and smog moves west
CHAPTER TWO Literature Review on Thunderstorm Rainfall 63
as Sydney grows due to the increasing concentration of chemical materials such as nitrogen
oxides and hydrocarbons in the atmosphere of the Sydney (see Plate 2.3). Also, Cohen, et
al., (1994) indicated that spatial and temporal concentrations of particles such as lead in
Sydney is higher than other areas. This study emphasised the important role of motor
vehicles in generation of lead in the region.
All aerosol particles, may contribute in formation of cloud condensation nuclei and, as a
result, rainfall occurrence. The pollution products may contribute to cloud formation and to
changes in the drop-size spectra. As such they can either promote or inhibit thunderstorm
rainfall. In case of ice nuclei, particulate matter, especially hygroscope particulates, might
initiate the precipitation process in supercooled clouds. A study of rainfall in Melbourne has
shown that the average rainfall on Sundays is 1.7 mm/d, but during the week the average
rainfall is 2.2. So, weekends are significantly drier than weekdays, presumably because of air
pollution volume changes (Linacre, 1992). Despite intensive efforts devoted to the
understanding of Sydney's atmospheric environment in the past (State Pollution Control
Commission of N S W , 1974 and 1975), the role of pollutants upon the urban precipitation
process has so far remained difficult to identify.
Plate 2.3 Shows smog over central Sydney.
Although, in the Sydney region, there has been some progress in understanding
meteorological processes in the urban boundary layer in the past years, there are still large
CHAPTER TWO Literature Review on Thunderstorm Rainfall 64
areas of uncertainty. Experimental studies of radiation and energy balance at both meso-scale
and micro-scale are limited to the few above-mentioned studies, largely as a result of the
complexities of the urban atmosphere and different landuse patterns with dissimilar surface
materials. M o r e importantly, the relative importance of the urban heat island and surface
roughness in modifying the thunderstorm rainfall distribution is still unclear.
In summary, in the Sydney region, the coastline and nearby hills and high elevated areas, it
has been suggested, have sufficient encouraging physiographic factors in the development of
convective clouds. They may also be supposed to be important parameters in the triggering
of thunderstorm activity and, as a result, more thunderstorm rainfall. It is also more likely
that the urban area (heat island) may affect the special distribution of thunderstorm rainfall.
Because, urban heat island impacts are most noticeable on cool, clear, stable spring and
summer evenings, when most convection cells advance over the urban environment and the
moist coastal margins.
2.9 Conclusions
The thunderstorm is a much more familiar weather event worldwide. It is a combination of
many things all occurring together. Usually, strong gusty winds, vertical currents at higher
levels, heavy precipitation with thunder and lighting, even more spectacular scenes and
occasionally with distractive consequences, are products of thunderstorm activity. Although
there is wide agreement that all thunderstorms require warm and moist air (as the prime
gradients leading to the formation of thunderstorms) in the atmosphere, other suitable
conditions are needed to increase instability and, as a result, to initiate a convective activity.
The roles that may be played by atmospheric instability are very important factors in
thunderstorm development.
Synoptic weather patterns such as fronts, lows, troughs and extreme currents in the free
upper atmosphere are among conditions which are the main factors responsible for the
introduction of the instability in the atmosphere and, thus, the creation of many
thunderstorms. However, synoptic weather systems are probably not always necessary nor
sufficient conditions for the occurrence of thunderstorms. Other trigger mechanisms for
thunderstorm initiation are also important. Over the last few decades, it has widely been
stressed that the development, occurrence and distribution of thunderstorms over a region,
also largely depends upon some climatic factors (air and sea surface temperatures, for
example) and physiographic parameters such as topography and proximity to sea.
As with other places in the world, thunderstorms in Australia can be introduced by the
presence of larger-scale synoptic weather systems and the nature of the prevailing air
masses, for example, lows and fronts. In addition, thunderstorms can be enhanced by
CHAPTER TWO Literature Review on Thunderstorm Rainfall 65
physiographic parameters which have been suggested to be more important parameters in
the occurrence and the controlling of thunderstorms.
On a regional scale, over the NSW and the Sydney region in particular, researchers have
found that the seasonal movement of pressure cells - anticyclonic highs, cyclonic lows and
troughs, determine the type and nature of air that is drawn towards the region and, as a
result, also in part determine thunderstorm occurrence. Meanwhile, when these air-masses
encounter the physical environment with various physiographic parameters, uneven spatial
distribution of thunderstorm patterns can be expected.
While synoptic weather systems control the availability of moisture and other gradients
needed for thunderstorm occurrence, and thus the actual amount of thunderstorm rain that
can fall, site and physiographic characteristics of each specific geographical location could
encourage and determine the spatial distribution of thunderstorm rainfall. Both climatologists
and meteorologists have emphasised that, in the Sydney region, climatic and physiographic
features play an important part in the more local nature of thunderstorm development in
different ways as follows:
Firstly, these investigations suppose an interaction between surface heating and source of
moisture and its subsequent impact on the thunderstorm activities. This may occur in
response to the combination of solar heating of surface layers to a critical temperature, with
the forces of air motions associated with synoptic weather systems and climatic factors of
the region. In Chapter 4, the close relationship between thunderstorm rainfall and climatic
factors will therefore be examined.
Secondly, it was also suggested that hills and mountain ranges can set-off thunderstorms in
potentially unstable airflows and these developments can, in some situations, drift away and
further develop and affect large areas of lowland in Sydney. For example, mountainous
areas, located in the west of the Sydney region, can be subjected to local thunderstorm
development during the warm seasons (late spring and summer months). These areas can
introduce convection systems and release massive potential energy, triggering-off
thunderstorms with intense rainfalls, while most parts of the region remain sunny and
cloudless.
In addition, coastal areas, particularly the high ground near windward coasts, are also
subject to thunderstorms. Areas near the coast may play a large part in the attraction of
thunderstorms deducing heavy rainfalls in association with advancing moist winds from the
ocean.
CHAPTER TWO Literature Review on Thunderstorm Rainfall 66
Finally, it may be proposed that heat-island effects and other physical and thermodynamic
characteristics of the Sydney region, such as pollution and roughness of the City itself, also
help in the development of thunderstorm rainfalls over and near the Sydney Metropolitan
area or over the City with large industrialised and commercial-residential areas. There is
some evidence that thunderstorm rainfall over the City is greater than for nearby suburban
areas. Therefore, in Chapter 6, the spatial variations of thunderstorm rainfall in the Sydney
region will be studied. Then, existing associations amongst above-mentioned physiographic
parameters and thunderstorm rainfall will be examined in Chapter 7.
To sum up, a complex interaction and relationship between synoptic weather systems,
climatic factors and the physiographic environment could be responsible for much of the
thunderstorm activity in the Sydney region. At times that the larger or meso-scale synoptic
weather systems are dominant, widespread thunderstorm activity is pronounced and,
consequently, high rainfalls can be expected. Although local climatic and physiographic
factors, which influence rainfall distribution, may tend to be masked by the nature of such
widespread thunderstorm activity, even in this situation, some degree of regionalization of
thunderstorm rainfall patterns can be climatologically distinguished.
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 62
CHAPTER 3
TEMPORAL DISTRIBUTION OF THUNDERSTORM
RAINFALL IN THE SYDNEY REGION
3.1 Introduction
The main aim of this chapter is to characterise the general behaviour of thunderstorm
rainfall in the Sydney region, over time. Thunderstorm activity can be addressed using a
variety of time scales. The pattern of thunderstorm frequency and rainfall amounts in the
study area are examined at yearly, seasonal, monthly and diurnal levels using measures of
central tendency or dispersion of data. As the distribution of thunder-recording stations
reflects the distribution of major cities, suburbs and dams, as a first approximation, spatially
the sampling network of thunderstorms is uneven. This chapter attempts to understand the
behaviour of thunderstorms using the better thunder-recording stations (with longer more
complete records) in the Sydney region. The N N A technique, defining significant
thunderstorms in the Sydney region, was therefore applied to thunderstorm data.
The sources of the data and the choice of data analysis techniques will be explained in
sections 2 and 3 respectively. Section 4 identifies the distribution of thunderstorms on a
yearly basis. In section 5, the seasonal and monthly distribution of thunderstorm rainfall is
analysed. In section 6 the diurnal variation of the thunderstorm rainfall frequency will be
determined. In the final section, findings of the temporal distribution of thunderstorms in
the Sydney region, can be discussed.
3.2 Data Used
The National Climate Centre (Bureau of Meteorology, Melbourne) provided the raw data
on thunderstorm activity in the Sydney region. Firstly, thunder activities data, which have
been recorded on three magnetic tapes were loaded on a P C computer system. In the
second stage, the University of Wollongong's main frame computer system was utilised to
extract the thunderstorm observations for all thundery days in all thunder-recording
stations. They were summarised for different time-scales - such as diurnal, monthly,
seasonal and yearly time spans. For each thunder-recording station a thunderstorm day was
considered to be a day for which at least one thunderstorm observation was reported.
For this study, according to the World Meteorological Organization (WMO, 1975 and
1988) a thunder observation is defined as the occurrence of a thunderstorm when one or
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 63
more sudden electrical discharges are manifested by a flash of light and a rumbling sound.
The traditional method of recording thunderstorm occurrence is to simply note whether
thunder is heard during the day. All thunderstorms observed during the last hour (past
weather) and also during the observation period (present weather) were counted in this
study, according to the present guide of the Bureau of Meteorology, Australia (Table 3.1).
Table 3.1 Represents a detailed description of the codes of present and past weather used in thunderstorm observations.
Past Weather Code
(Thunder was heard) 9
13
29
91
92
93 and 94
Description Thunderstorm with or without precipitation Lightning seen, no thunder is heard Thunderstorm (thunder is heard) Slight rain at the time of observation Moderate or heavy rain at the time of observation Slight, moderate or heavy hail at the time of observation
Present Weather Code
(Thunder is heard) 17
95
96
97
98
99
Description Thunderstorm without precipitation Slight or moderate thunderstorm with rain Slight or moderate thunderstorm with hail Heavy thunderstorm with rain Thunderstorm with dust and sand storm Heavy thunderstorm with hail
Therefore, for each thunder-recording station a thunderstorm day was considered to be a
day for which at least one thunderstorm observation was reported. A thunderstorm may or
may not be accompanied by precipitation. In this case, code 17 refers to instances where
thunder was heard at the station but no precipitation occurred. Again, if lightning is
observed (code 13) without thunder being heard at the station, the event is not considered
in this research to be a thunderstorm because this can occur at distances remote from the
station, especially at coastal locations with flat topography. Using these codes the daily
thunderstorm rainfall amount (more than 0.1 m m ) and its frequency data were collected for
the period 1960-1993 for each individual thunder-recording station. From these data sets,
the mean monthly, seasonal and annual time-series have been derived. It must be noted that
rainfall on a thunderday is not necessarily produced all or in part by a thunderstorm. This
was addresed by selecting the better thunder-recording stations (see Section 3) and defining
a thunderstorm-day for the Sydney region, using data from a set of selected stations. At
least three thunder-recording stations had to record a thunderstorm on a thunderstorm-day
throughout the region (see Chapter 6).
3.3 Methods Applied
It has been found that many thunderstorms seem to occur in an independent manner in time
and space (Duckstein et al., 1973). Also, the results of researchers such as Sharon and
Kutiel (1986) and Sharma (1987) have indicated that thunderstorm rainfall values are
typically strongly skewed in time and space, occasionally with extremely intense localised
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 62
rainfalls. Therefore, the estimation of the temporal and spatial distribution of thunderstorm
rainfall can be biased by these cases, particularly where there is missing data.
In the case of the Sydney region, this bias was assessed by examining the similarities
(associations) amongst different stations of thunderstorm rainfall. Figure 1.2 shows the
location of the 15 thunder-recording stations in the Sydney region. T o find the relative
interdependence and associations among all existing thunder-recording stations, the
following steps were undertaken.
In the first stage, a computer program was written to find common thunderstorm-days
between 15 key stations listed in Table 3.3 (the computer program number 1 is located in
Appendix A ) . These stations were selected because they had data for at least seven years
continuously and the fewest missing values for the period 1960 to 1993. The specific days,
totalling about 1584 thunderstorm-days, are listed in Table 3.2 (see Appendix B) .
Table 3.3 General geographical characteristics of the thunder-recording stations. N o of stations
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Stations names
Katoomba Richmond Camden A. Bankstown Sydney Airport Sydney R. Office Wollongong Prospect D a m Liverpool Lucas Heights Bowral Parramatta Penrith Campbelltown Picton
Latitude
33.72 33.60 34.05 33.93 33.93 33.87 34.40 33.82 33.92 34.05 34.48 33.80 33.75 34.08 34.18
Longitude
150.30 150.78 150.68 150.98 151.17 151.20 150.88 150.92 150.92 150.98 150.40 151.02 150.68 150.52 150.62
Distance from sea in K m 93 54.5 47 26 8 7.3 3.4 29.3 29.3 16 47 24 56 31 37.3
Altitude inm
1030 19 70 9 6 42 30 61 21 140 690 60 25 75 171
Period of data used** 1987-93 1960-93 1972-93 1969-93 1960-93 1960-93 1972-93 1965-92 1962-92 1962-82 1975-92 1967-92 1967-85 1962-84 1965-75
* The nearest distance from the average coastal line. ** See Table 3.2 (Appendix B).
In the second stage, a clustering analysis technique based on the Nearest Neighbourhood
Algorithm (Tversky, 1983) was used to group similiar thunder-recording stations. The
technique of N N A is discussed at some length by many geographers including, for example,
Cliff, et al. (1975), Cliff and Ord (1981) and Unwin, (1981). The technique has been used
specifically in regionalising climatic variables and clustering point observations of rainfall
(Theakstone and Harrison, 1971). In nearest neighbour clustering, the optimality condition
is for the generated clusters to give the least possible distance amongst all possible cluster
combinations (Dasarathy, 1991). The computer program for the N N A technique uses an
iterative procedure, where at each iteration each data sample is compared to all other sets
of randomly chosen seeds. After each iteration the set of seeds with the minimum distance
is grouped and the central (centroied) of each cluster is calculated. The iterations are
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall W
stopped when the ratio of overall improvement compared with the previous step, is less
than a predefined value. The criterion for clustering is based on Euclidean Distance defined
by the magnitude of cluster members for a particular event for each thunder-recording
station relative to the cluster centre, where N is the number of stations which could be
clustered into K groups. The distance can be written as:
1=1
where D shows the overall distance and D[ shows the distance in cluster /'. Distance in each
cluster can be calculated by summing the distance of all the cluster members from the
cluster centre. The definition of distance depends on data characteristics. A simple
definition is the Euclidean Distance is defined as:
Euclidean Distance = •yj(x1 -x2)2 + (yx -y2)
2 +(*, ~x„)2 +(yf-y„)2
where jq and y\ are the data components. In clustering all thunder-recording stations, all
possible physiographic components of each station such as latitude, longitude, distance
from the sea, and altitude, were used (see Table 3.3). These parameters were selected
because they have been linked to the distribution and variation of thunderstorms over the
study area (see Chapter 2).
Using the NNA technique, seven main clusters (A to E) were found representing distinct
areas of thunderstorm activity in the Sydney region. As it can be seen from a dendrogram
(Figure 3.1), for example, group six includes the Sydney Regional Office, Sydney Airport
and Parramatta stations. Katoomba and Bowral stations which are located in mountainous
areas, have been classified as two separate groups, as has Wollongong located in the south
east of the study area.
Locations such as coastal strips or tablelands in the region, with different geographic and
physiographic characteristics have already been suggested by Williams (1991) to affect the
occurrence of thunderstorm activity. The N N A clustering results support this assumption.
For instance three stations, Camden, Campbelltown and Picton, close to each other in the
southwest of the Sydney basin, group together, as do Sydney airport, Sydney Regional
Office and Parramatta in the centre of the study area. The stations with the longest and
most complete record of data collection in each group were selected for further analysis.
These stations are listed in Table 3.4.
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 11
Index Of Similarity
(Euclidecen Distance Score)
L_
L_
- Katoomba
Bowral
— Parramatta
i
"Sydney Airport
1— Sydney R.Offiee
Liverpool
i
i
I
Bankstown
- Lucas Heigths
- Camden
Picton
CampbeUtoum
- Richmond
• Penrith
Prospect Dam
Wollongong L
Figure 3.1 A dendrogram shows the result of the N N A technique in grouping thunder-recording stations.
Table 3.4 Locality of the seven selected stations.
Selected Stations Groups Locality Katoomba Bowral Richmond Camden Airport Bankstown Sydney R.O. Wollongong
C A D B E G F
Mountain Mountain Near-mountain Far-inland Near-inland Coastal Coastal
3.4 Yearly Distribution of Thunderstorm Rainfall
The broadest time scale over which thunderstorm rainfall varies is the year-to-year
variation in total amounts. In many respects this is also the most important as it represents
the changes of thunderstorm rainfall frequency over the 34 year period from 1960 to 1993.
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 22
Table 3.5 Yearly variation of thunder-days frequency and thunderstorm rainfall amounts (rainfall is in m m ) at 7 thunder-Stations
Year 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 Total
Average
Katoomba
TDF
13 24 22 29 31 30 25 174 25
Rain
190 205 139 350 212 300 163 155S 223
recording stations in
Bowral
TDF
21 25 21 23 24 12 16 19 18 21 15 12 17 21 19 26 27 25
I 362 20.1
Rain
170 102 110 130 101 68 105 67 98 204 102 79 160 188 122 201 175 140
2322 129
the Sydney region.
Richmond
TDF 11 23 15 22 10 15 32 12 16 34 33 20 32 28 14 25 21 29 29 25 18 23 14 26 26 33 11 22 25 22 34 26 34 41 801 24
Rain 118 100 86 125 133 167 90 58 96 312 264 128 161 172 76 207 105 193 186 87 70 135 111 115 126 254 63.2 138 186 136 369 170 183 199 5119
157
Camden A.
TDF Rain
8 14 7 14 9 9 9 14 6 12 6 11 10 14 11 13 20 3 1 12 7 2 212 10
48.4 182 67 115 55 85 40 147 34 78 13 87 75 93 30 73 157 51 9 86 38
1562 74.4
Bankstown
TDF
7 9 6 7 12 6 10 17 12 9 11 10 12 3 20 28 25 16 14 22 18 25 16 8
323 13.5
Rain
20 42 70 124 69 111 129 97 132 114 140 93 10 180 399 221 195 249 215 196 297 146
3249
135
Sydney R.O.
TDF 22 22 19 24 17 17 22 13 14 21 26 14 20 17 9 16 28 21 20 25 11 13 7 15 23 21 15 15 23 20 27 21 23 18 639 19
Rain 136 216 112 231 196 109 133 229 71 227 310 185 158 271 87 322 273 120 113 115 113 110 46 125 487 116 77 196 274 217 251 261 114 213 6214 183
Wollongong
TDF Rain
6 4 3
5 10 9 9 4 5 3
14 9 5 6 8 7 14 12 9 13 155 8
54 48
83 75 210 52 43 49 10
311 98 40 115 142 381 202 235 43 161 2352
125
Table 3.5 summarises the total annual thunder-days over this time-span. T D F represents
Thunder-Days Frequency, and missing years are left as blank cells. The Sydney Regional
office and Richmond stations, recorded data continuously over this period. Annual
thunderstorm rainfall amounts over the same period are also shown in Table 3.5. The
highest mean number of thunderstorm days were observed at Katoomba, Richmond with 25
and 24 thundery-days per year respectively while the minimum average frequency occurred
at Wollongong and Camden stations with 8 and 10 thundery days per year respectively.
To see the variations of thunderstorm rainfall over the study area, the coefficient of
variation, c v = (s /x )*ioo was used.
Where x = the average annual thunderstorm rainfall,
and s = standard deviation.
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 23
This technique was not used to find one-to-one links between thunderstorms and their
associated rainfalls at each thunder-recording station. Thus, it could not be assumed that
the rainfall falling at a station comes from the storm where thunder in heard. It should be
regarded as a descriptive statistical technique, simply representing means and the coefficient
of variations of the yearly thunderstorm frequency and rainfall values in different stations
(given in Tables 3.6 and 3.7 respectively). The greatest coefficient of variation in yearly
thunderstorm frequency (50.2 % ) was observed at Bankstown station about 26 K m far
from the coast. In contrast, the lowest coefficient of variation in thunderstorm frequency
(25 % ) occurred at Katoomba located in the Blue Mountains. Also, Sydney Regional Office
showed a low coefficient of variation with 27 %, located in the east of the study area about
8 k m distance from the Tasman Sea. The highest number of thunderstorm days was
observed at Katoomba and Richmond stations located in the north-west of the Sydney
region, 93 and 55 k m respectively from the ocean.
Table 3.6 Summary descriptive statistics for yearly thunderstorm rainfall frequency, in the Sydney region, from 1960 to 1993.
No. Station Name No. of Mean Max. Min. Range Coefficient
Years of Variation (%)
1
2
3
4
5
6
7
Katoomba
Bowral
Richmond
Camden Airport
Bankstown
Sydney R.O.
Wollongong
7
18
34
22
24
34
20
25
20.1
23.6
9.6
13.5
18.8
7.8
31
27
41
20
28
28
14
13
12
10
1
3
7
3
18
25
31
19
25
21
11
25
28
34.1
47
50.2
27
45.6
The highest thunderstorm rainfall values occurred at Katoomba (223 m m per year) and 183
m m at Sydney Regional Office. Also later station recorded the highest annual rainfall values
(see Table 3.7).
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 14
Table 3.7 Summary descriptive statistics for yearly thunderstorm rainfall amounts, in the Sydney region, from 1960 to 1993.
No. Station Name No. of Mean Max. Min. Range Coefficient
Years of Variation (%)
1
2
3
4
5
6
7
Katoomba
Bowral
Richmond
Camden Airport
Bankstown
Sydney R.O.
Wollongong
7
18
34
21
22
34
19
223
129
150.5
74.4
147.7
182.8
123.8
350
204
369
182
399
487
381
139
67
58
9
10
46
10
211
137
311
173
389
441
371
34
29
46.8
61.5
62.5
50
82.6
The annual variation of thunderstorm frequency and rainfall for the two stations, with a
complete 34 year record, Sydney Regional Office (Figure 3.2) and Richmond (Figure 3.3)
were graphed in more detail. It should be noted that these stations are not representative of
the thunderstorm variation over the entire Sydney Region.
Sydney Regional Office
Rainfall • Thurxfer-days
Figure 3.2 Yearly variation of thunder-days frequency and thunderstorm rainfall at Sydney Regional Office station (1960-93).
Comparison of the number of years above and below average in all stations shows that
there is a fluctuating pattern with high and low years. It was found, on average, for
example that in 1969, 1975 and more significantly in 1984, there were some considerable
thunderstorm rainfalls in the Sydney region.
Distribution of Thunderstorm
Richmond
a «
&
O)
g
Figure 3.3 Yearly variation of thunder-days frequency and thunderstorm rainfall at Richmond station (1960-93).
To gain an appreciation of the longer term variability of thunder-day frequency and rainfall,
the data in Figures 3.2 and 3.3 were analysed using Normalised Residual Mass curves
( N R M ) , used by the Bureau of Meteorology (1991a). The N R M can be defined as the
accumulated difference between the actual annual thunderstorm rainfall for each year and
the mean annual thunderstorm rainfall over total years of the record, divided by the mean of
these factors. The N R M for the Sydney region (the average of two above-mentioned
stations) from 1960 to 1993 is shown in Figure 3.4. This graph clearly shows sequences of
wet or dry thunderstorm rainfall years.
•o s
.a •a
s
•NRM Rainfall NRM'Thunder-day
100.0 T
80.0 -•
-40.0 -•
-60.0
it H Ol w .9 •a
I
Figure 3.4 Normalised Residual Mass curves of annual thunderstorm rainfall for three important thunder-recording stations (1960-93).
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 76
The results of this section indicate, the average frequency of thunder-days and
thunderstorm rainfall amounts over the mountainous and urban areas adjacent to the coast
are much greater than over the inland areas in the Sydney region. This result confirms the
work of Griffiths, et al.(1993) who found that the number of severe thunderstorms in N e w
South Wales is increasing. They also give evidence showing a pronounced maxima in the
distribution of thunderstorms in recent decades, particularly in the Sydney region.
However, these results are not yet valid indications of thunderstorm rainfall variations over
the study area, because the increase might be a function of greater population densities and
better of data reporting (see Chapter 6 for spatial variations). Overall, the pattern for yearly
variation of thunderstorms at different stations are not similar. Comparison of the number
of years above and below average in all stations show that there is a fluctuating pattern
with high and low years.
3.5 Seasonal and Monthly Distributions
In the Sydney region there is a considerable seasonal variation in thunderstorm rainfall
throughout the year as shown in Figure 3.5.
Figure 3.5 Seasonal distribution of thunderstorm rainfall in different stations in the Sydney region.
As expected, in response to the warm environment and unstable atmosphere, thunderstorm
activity is greatest during late spring (October to November) and summer (December to
February) and weakest during autumn (March to May) and winter (June to August). For
the whole region the maximum falls are in late spring and summer, however there are
considerable differences. Some stations, for example Wollongong, show a maximum in
spring rather than in summer. The graphs for the Sydney Regional Office and for
Bankstown are very similar with a peak in summer and a secondary peak in spring. In
contrast, Katoomba shows a peak in summer and a much smaller secondary peak in
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 71
Spring. Camden and Richmond stations which are located in inland areas, experience a
summer maximum with little thunderstorm activity in autumn and winter.
The range of seasonal values are generally considerable at all stations. Average seasonal
thunderstorm rainfall amounts are given in Table 3.8 for select stations. Both Richmond
and Katoomba, which are located in the west of the Sydney region receive a high
percentage of rainfall in summer rather than in other seasons. In contrast, stations which are
located in the east of the Sydney region near the coast, for example Wollongong and
Sydney Regional Office, have considerable thunderstorm rainfall (on average 32 per cent)
in both autumn and winter seasons.
Table 3.8 Average seasonal thunderstorm rainfall (in m m and % ) for selected stations.
Stations
Katoomba
Bowral
Richmond
Camden
Bankstown
Sydney R.O.
Wollongong
Average
Spring (SON)
Rain m m
43.5
32
46.9
27
43.5
58.5
39.5
41.5
%
19.1
24.1
31.1
36.3
29.4
32
37.5
28.7
Summer (DJF)
Rain m m
141
60 4
65.1
33.3
60.5
62.5
46.5
67.03
%
61.5
45.6
43.1
44.8
41
33.6
32
46.32
Autumn (MAM)
Rain m m
32.3
30.2
25.8
11.7
32.8
40
21.5
24.07
%
14.2
22.7
17.1
15.7
22.1
22
18
16.6
Winter (JJA)
Rain m m
11.4
10
13.1
2.4
11
21.5
15.5
12.12
%
5.6
7.5
8.7
3.2
7.4
12.4
12.7
8.4
In light of these comparisons, it appears that the seasonal response of thunderstorms varies
across the region. Despite this variation, in general, late spring and summer are the peak
seasons of the year for thunderstorm activity, 72 per cent of all thunderstorms occurred at
these times. Thunderstorms also tend to account for a higher percentage of rain-days
toward the end of the thunderstorm season.
The monthly distribution of thunderstorm rainfall at different stations is shown in Figure 3.6
in more detail. Thunderstorms occur most frequently in November and December, and least
frequently in May, June and July. Generally, the warm summer months, October to March,
clearly dominate. However, the peak months for thunderstorm rainfall differs amongst
stations. For example, stations such as the Sydney Regional Office, Camden and
Bankstown, have peak thunderstorm rainfall in November. Unlike these stations,
Katoomba, because it is a mountain station, receives more thunder rainfall in January and
February. Both coastal and inland stations exhibit two maxima, in November and February
during the course of the year.
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 18
Bankstown -A Camden
Katoomba
Bowral Sydney R.O
•Richmond
• Wollongong
Oct Nov Dec
Figure 3.6 Monthly distribution of thunderstorm rainfall in the Sydney region for different stations.
The percentages of the average monthly rainfalls due to thunderstorms are shown in Table
3.9. The highest proportion occurs, on average, in the Sydney region in November with
31.4 per cent, but this rate varies for different stations.
Table 3.9 The percentage of average thunderstorm rainfall to mean monthly rainfall in different stations, in the Sydney region.
Stations Katoomba Bowral Richmond Camden Bankstown Sydney R.O Wollongong Average
Month (1987-93) (1975-92) (1960-93) (1972-92) (1970-91) (1960-93) (1973-93)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Yearly
32.2 29.7
13.3
4.4 1.5 3.3 0.3 7.9 9.1 6.5 26.3
27.8
15.3
30 28 13 5 1.2 3 0.3 7 8.2 7 25 26 12.8
23.5
18.2
17.3 9.0 4.9 7.1 12.9
9.6 22.5
18.9
31.6
29.9
18.0
14.4
11.4 4.5 7.5 2.4 0.0 4.6 1.4 5.3 13.5
18.8
16.7 9.0
18.3
20.1
15.1 11.3 5.9 4.7 5.4 8.3 10.9
13.4
36.7
28.2
15.6
17.7
21.6
18.6
7.5 4.7 8.2 2.3 11.9
13.1
18.9
42.3
22.3
14.9
8.0 6.0 8.0 1.2 4.1 8.4 1.1 5.4 12.4
4.7 27.4
15.0
8.4
20.6
19.3 12.8
6.5 3.5 5 3.8 7.4 11.6
11.8
29.7
23.7
13.42
For example, in November, in the Sydney Regional Office about 42 percent of monthly
rainfall is obtained from thunderstorms. In contrast, Katoomba receives only 26.3 per cent
of its rainfall from thunderstorms at this time of the year. In the winter months, M a y to
July, all stations obtain less than 6 per cent of their rainfall from thunderstorms.
Distribution of Thunderstorm Rainfall
3.6 Diurnal Variation
The importance of the diurnal cycle in the Sydney region is clear, and this work is in line
with that of earlier investigators. During the past decades, this most regular and predictable
diurnal distribution of thunderstorm activity has produced studies all over the world by
Brooks (1925), Tubbs (1972), Wallace (1975) and Tucker (1993) for U S A , Oladipo and
Mornu (1985) in Zaire, Barkley (1934) for Australia, Grace et al. (1989) for South
Australia, Treloar (1991) for Melbourne and Williams (1991) and Griffiths et al., (1993) for
the Sydney region. Generally, all these investigators indicated that in many parts of the
world, during the summer months, convectional processes predominate and can produce a
distinct diurnal distribution and variation in thunderstorm activity and an induced
precipitation amount mainly in the afternoon or early evening.
This section will describe the diurnal variation of thunderstorm rainfall at various parts of
the study area during the spring, summer and autumn seasons. In the Sydney region, there
are some problems with the data (provided by the Bureau of Meteorology) as they relate to
thunderstorm observations. Firstly, there are insufficient thunderstorms to define a clear
pattern for winter. Second, for some diurnal time spans there are not enough data for all
seven stations considered. Also, some stations report every 3 hours, some twice and others
only once a day. In data-set, there are no thunderstorm observations for 1, 4, 7, 10, 13, 16,
19, 22 and 24 hours, based on the N S W Local Standard Time (LST). These problems with
data caused some spatial and temporal gaps in illustrating the diurnal variation of
thunderstorm rainfall throughout the Sydney region.
Therefore diurnal patterns are only, shown for Katoomba, Richmond and Sydney Regional
Office stations. These are representative of three different geographical locations located in
the Sydney region, including mountainous, inland and coastal areas respectively. They also
have sufficient data for the purposes of this study in comparison with other thunder-
recording stations. Moving eastward across the region, there is a gradual transition to a late
afternoon and early evening maximum over the City and coastal areas. This diurnal pattern
is shown by data for the three stations located in the region (figures 3.7-3.9).
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall EQ
katoomba (1987 -93)
40
co co un to oo oi *-•
Figure 3.7 Diumal variation of thunderstorm rainfall frequency for three thunder seasons at Katoomba station.
At Katoomba, over the mountains, the highest thunderstorm activity occurs in the
afternoon and a second highest in late afternoon (see Figure 3.7). However, this station has
the fewest number of thunderstorms at midnight and early morning.
Richmond 0960-93)
BSj?ig03SD •SbnrH?(DaF) S AiiumflVlAM)
40
Figure 3.8 Diumal variation of thunderstorm rainfall frequency for three thunder seasons at Richmond station.
The Richmond station, which is located near the base of the mountains, has a maximum
thunderstorms between 1700 and 1800 LST, but there is an additional one in the late
afternoon or early evening, between 1400 and 1500 (see Figure 3.8).
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 81
Finally the Sydney Regional Office station (Figure 3.9) has one maxima in the late
afternoon (1700-1800 LST). However, it is clear from the results, that a considerable
number of thunderstorms have a nocturnal nature in the western and in the eastern parts of
the study area. There are also some differences in diurnal patterns regarding different
seasons.
Figure 3.9 Diumal variation of thunderstorm rainfall frequency for three thunder seasons at Sydney Regional Office station.
It can be seen that in all cases at Sydney Regional Office, there is an afternoon-evening
diurnal pattern between 17 and 21 LST. In contrast, Katoomba shows a distinct afternoon
pattern (14-17) during the summer months. January and February, in particular at
Katoomba, seem to be more characterised by an afternoon (12-18) pattern than the other
months.
On average, the results of the diurnal variation analysis indicate that in most of the region
to the west and central parts of the Sydney region, thunderstorms exhibit a strong late
afternoon maximum, particularly during summer months. In the eastern part, thunderstorms
show marked diurnal distribution with maxima during the late afternoon and earlier
evening, however, thunderstorms may also occur at any time of the day or night, in the
Sydney region.
3.4 Discussion
This chapter has presented a temporal distribution of thunderstorm frequency and rainfall
amount in the Sydney region for the period 1960 to 1993 using data from the seven
selected thunder-recording stations. It is clear from the results that the temporal variation
of thunderstorm frequency and rainfall amount over the Sydney region varies from year to
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 82
year. During a calendar year, two seasonal periods with different thunderstorm rainfall
characteristics were distinguished: September-March and April-August. It was found that
in the September-March period thunderstorm activity predominates, particularly during
November through to February.
Thunderstorms are most common in the spring and summer during the afternoon or
evening hours. Generally, thunderstorm weather begins in late October and increases quite
abruptly in November as was shown by Ryan (1992) who found that in N S W , both severe
and less severe thunderstorms are most common in the summer months. There are,
however, alternating periods of high and low thunderstorm rainfall amounts during the
spring and summer seasons. The results of the diurnal variation analysis indicated that the
thunderstorm regime in the Sydney region is an 'afternoon/early evening' type. This result
agrees with the work of Griffiths et al. (1993) and Batt (1994).
In the summer months, the maximum values of thunderstorm frequency are observed west
of the study area over the mountains. It is evident from the data that the mountains to the
west of the Sydney region receive many more storms than lowland areas between the
mountains and the coastal areas. Thunderstorms are least frequent in these lowland areas.
The areas near the coast and City, however, generally received greater thunderstorm
rainfall amounts on average in the same period (1960 to 1993). Explanations for the
temporal distribution and variable nature of thunderstorms over the Sydney region are
complex. Various mechanisms, which can introduce or enhance thunderstorm activity, have
been proposed by different authors for the observed thunderstorm patterns in the region.
These may reflect the overall impact of three important controlling factors that include: 1)
the synoptic weather patterns, 2) the local climatic factors and 3) physiographic parameters
of the Sydney region.
3.7.1 The Role of Synoptic Weather Patterns
In the past, atmospheric conditions leading to the creation of thunderstorms have been the
subject of detailed investigation by different authors (Hales, 1978; Winkler, 1988). They
have suggested that convection rainfall often develops when synoptic weather patterns and
meso-scale mechanisms promote instability in the atmosphere or enhance present unstable
conditions. More recently, Konrad and Meentemeyer (1994) found that various synoptic
scale features such as lows and fronts, can be connected with heavy rainfall from
thunderstorms over the Appalachian region. These synoptic features provide a supportive
environment on the meso-scale (10-100 km) for the development of convective cells which
produce heavy rainfall as they move and interact with one another (for more details see
Chapter 2).
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 8J
In the Sydney region, while local convection as a result of maximum solar heating occurs
mainly in the afternoon or evening, the timing of thundery weather still depends upon
timing of the passage of synoptic weather systems which may occur during the day or
night. These close relationships between thunderstorm activity and weather patterns have
been studied in the past by several researchers such as Morgan (1979b); Griffiths et al.
(1993); Matthews (1993); Speer and Geerts (1994). These studies indicate that there are
some synoptic conditions which favour thunderstorm development in the Sydney region.
Firstly, occasional lows, formed over the north of the Tasman sea, move close to the NSW
coast and produce considerable thunderstorm activity, causing very heavy rainfall in the
region. Secondly, it was found that in some cases thunderstorms can occur with, or ahead
of an active front leading to thundery showers and that these thunderstorms are
occasionally widespread if there is some upper air disturbance present (Williams, 1991)
Finally, investigators such as Matthews (1993) and Speer and Geerts (1994) - who more
recently studied the formation and structure of thunderstorm events in the Sydney region -
have found that the occurrence of thunderstorms in the Sydney region can also be
correlated to the synoptic-meso-scale weather systems such as: easterly troughs; pre-frontal
systems lows which developed from low pressure systems, and post-frontal systems. In
such situations, thunderstorms can move over the region, particularly in the spring and
summer months, when the predominant winds are humid north-east or south-east
originating from the Tasman or Coral Seas. To relate some specific thunderstorm events to
type of synoptic conditions, several examples are given in Chapter 6 in Table 6.4 and
Appendix C.
It is also possible that the effectiveness of night thunderstorms in generating rainfall is
probably different from daytime thunderstorms, due to the expected differences in humidity
and temperature patterns produced by the above mentioned types of synoptic systems.
Although the synoptic weather systems are very important in the production and
enhancement of thunderstorms, this study will not analyse the effects of these weather
patterns upon the thunderstorm activity in the region. The association between synoptic
weather patterns and the development of thunderstorms in the Sydney region was discussed
in more detail in Chapter 2.
3.7.2 The Effect of Climatic Factors
Synoptic scale weather patterns allow for a broad understanding of thunderstorm rainfall
occurrence over the study area. However, the fact that all stations in the Sydney region
have at least a diurnal maximum in the afternoon or early evening, suggests that the diurnal
distribution of thunderstorms may be controlled by local climatic factors such as air and
sea-surface temperatures and air humidity. Explanations for the prevalence of afternoon-
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 8J
evening thunderstorms over a region such as the Sydney area (with variable physiography)
are complex. Various mechanisms can be proposed for the observed diurnal thunderstorm
patterns.
One such group of mechanisms are those based on thermodynamic actions (for example,
solar radiation). It seems that most thunderstorms are due to local convection induced by
diurnal surface heating particularly in summer months. This occurs because high
temperatures are available from the heating of ground surfaces and there is a great deal of
moisture in the air, originating from a warm, nearby ocean.
This mechanism can be applied to the Sydney region as a simple explanation for the
maximum thunderstorm activity in the late spring and summer. That is 1) a diurnal heating
over the land and 2) an abundant supply of moisture from the Tasman Sea, which not only
increases the amount of precipitation produced, but also the degree of conditional
instability in the atmosphere. This may suggest an investigation of the possible relationships
between thunderstorm rainfall and some climatic factors such as, the sea-surface and air
temperatures and relative humidity in the region.
3.7.3 The Impact of Physiographic Parameters
More likely, as was discussed in Chapter 2, it is also possible that some types of
thunderstorms could be controlled by physiographic parameters such as topographic
features, proximity to the ocean and landuse patterns. These parameters play an important
part in the more local nature of thunderstorm development in the Sydney region. Previous
studies such as Astling (1984), and Smith (1979 and 1985) have already shown that hills
and mountain ranges can set off thunderstorms in potentially unstable airflows and these
developments can, in some situations, drift away and further develop and affect large areas
of lowland. The Sydney region is walled by a mountain range to the west, so, it is possible
that the most regular and predictable types of orographic thunderstorm activity may occur
in the warm season conditions to the west of Sydney. In this area, the daily heating of the
hillsides may generate warm up-slope winds which continue rising after reaching the
mountain top. In this situation, the heated air rises to form convective clouds which can
trigger deep convective systems. Then, this diurnally forced convection may produce more
thunderstorm activity when the thunderstorm is forced to travel some distance away from
the mountains towards the coastal areas (Morgan, 1979a).
Alternately the combination of mountainous terrain and moist, warm and unstable air
masses may provide the most favourable conditions for thunderstorm development. This is
why they are more common over mountains and about the coastal areas particularly in
summer. In this case, the violent thunderstorms which can be triggered by, for example, the
CHAPTER THREE Temporal Distribution of Thunderstorm Rainfall 85
physical environment also may be enhanced by convergence, a high atmospheric advection
or an active front.
The resulting thunderstorms which occur primarily over the mountains and coastal areas,
are most important evidence signalling that topography and proximity to the sea can
control the temporal thunderstorm occurrence and also associated rainfall distribution. This
may reflect a tendency for some of thunderstorms to develop over the mountains, then
move eastward towards the coastal area. It seems quite likely that the afternoon maximum
in summer thunderstorms along mountainous parts of the study area and the late evening
and night-time maximum on coastal areas can be explained by this mechanism.
3.8 Summary and Conclusion
Careful study of thunderstorm rainfall amounts and frequency at the different stations
located in the Sydney region, indicated that thunderstorms show marked diurnal and
seasonal variation. They are most frequent in the summer months and during the late
afternoon and early evening, but there are some recognisable differences between stations
in the region. Thunderstorms are most frequent over the west of the region where they may
be initiated by the air rising over the mountains, and less frequent over the lowland interior
of the Sydney region. However, it is evident from the above results that the stations which
are located in coastal areas, near the ocean, receive more thunderstorm rainfall than those
located inland or in the nearby mountains. This result does not hold, however, for
thunderstorm frequency, because the periods of monthly maxima of thunderstorm activity
do not necessarily coincide with the periods of rainfall maxima in all of the study areas.
It is obvious from the evidence that there is no single hypothesis that is capable of
explaining the nature of temporal variation of thunderstorm activity in different parts of the
Sydney region. As the results of this study indicated, the distribution of thunderstorms over
the Sydney region varies over time and space. O n one hand, these results may reflect the
overall effects of some of the synoptic scale weather patterns upon thunderstorm activity
which have been widely studied by many researchers in the region.
On the other hand, because the temporal and spatial variation of thunderstorms in the
Sydney region is relatively high, they may be affected by the above-mentioned climatic
factors and physiographic parameters of the region. Therefore, in Chapter 4, the more
detailed associations among additional climatic factors and thunderstorm rainfall will be
analysed. Chapters 6 and 7, will then address the spatial variation and distribution of
thunderstorm rainfall with an emphasis on the possible associations between thunderstorm
rainfall and physiographic parameters of the Sydney region. This will open a new approach
for future studies on a regional scale.
CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables 86
CHAPTER 4
THUNDERSTORM RAINFALL AND CLIMATIC VARIABLES
4.1 Introduction
In Chapter 3, it was argued that the distribution of thunderstorm rainfall in the Sydney
region may be affected, to some degree, by some important climatic variables such as air
and sea-surface temperatures. The overall goal of this chapter is, therefore, to determine
the association of three main climatic background variables, sea-surface temperature, daily
air temperatures (maximum and minimum), and mean relative humidity with thunderstorm
rainfall amount and its frequency. This will be done using monthly data mainly from
Richmond, Sydney Airport and Sydney Regional Office. These stations, having the longest
data in the region.
In section 2 the data sources and analytical techniques are given. In the first stage of data
analysis, in section 3, descriptive statistics describe and summarise single variables in order
to demonstrate the general characteristics of these variables. In the second stage of the data
analysis, in section 4, some types of statistical techniques, such as simple correlation
techniques, determine the significant levels of associations amongst variables. Section 5
examines the effect of independent variables separately upon the dependent variables, using
a stepwise multiple regression technique. Finally the reasons, for statistically significant
comparisons, are outlined in section 6.
4.2 Data Sources and Analysis Techniques
To find the possible associations between thunderstorms data and climatic factors, data
were obtained from the Sydney Regional Office on a monthly basis. Sydney's air
temperature and humidity records for three synoptic stations, namely: Sydney Airport;
Sydney Regional Office, and Richmond, were available (this is the most appropriate range
of stations with data available for this analysis). The data were first described on a monthly
basis, and were then calculated using mean daily maximum and minimum values (one-half
of mean daily maximum plus minimum) from 1960 to 1990.
Tables 4.1 to 4.6 (see Appendix B) show the monthly thunderstorm rainfall frequency and
thunderstorm rainfall amounts for the three above-mentioned stations. These stations were
selected because: (1) they are the main synoptic stations recording thunderstorm events;
(2) they have the longest time-span records; and (3) they have complete records of the
CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables -XI
necessary data. These data were restricted to the period 1960 to 1990 because sea-surface
temperature was available only for this period.
The sea-surface temperature data was obtained from the Australian Oceanographic Section
(Sydney Division). The data was collected at a station off of Port Hacking (34° 05' S, and
151° 12' E ) which performs measurement on a semi-regular basis. Most, but not all the
years contain observations for each month of the year. Sea-surface temperature
observations are usually taken weekly. Therefore, monthly means were calculated by
aggregating the weekly data and then averaging these values. Where a single month was
not observed, its value was assessed by a linear interpolation and then used as part of the
time series.
To analysis the data and to find the possible associations among these data sets, some
simple to complex statistical techniques were used. The following methods form the basis
for all analysis in the current chapter:
1) A simple correlation technique was employed in order to determine the possible
causal relationships between dependent (thunderstorm rainfall) and independent variables
(air and sea-surface temperatures) and the extent to which the variables are interrelated.
This statistical technique is used when both the independent variables and the dependent
variables are measured on a ratio scale.
2) A correlation coefficient test was undertaken for preliminary hypothesis testing.
This technique was also employed for the purpose of determining the reliability of the
variables.
3) For the last stage of analysis, a stepwise regression was used. By using this
technique, independent variables were entered into the equation separately according to the
strength as predictors of the dependent variables. Consequently, the regression coefficients
provided estimates of the effect of each of the independent variables on thunderstorm
rainfall frequency, holding statistically constant the effects of the other variables included in
the equation.
4.3 Description of Variables
In this chapter, both thunderstorm rainfall amount and its frequency have been assumed to
be dependent variables affected by above-mentioned climatic variables. Addressing this
assumption, and to find possible associations between these independent and dependent
variables, it is first necessary to describe each of these variables using monthly-based
averages. This will facilitate analysing, comparing, and measuring the correlations between
CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables 88
the different variables. Table 4.7 gives simple descriptive statistics for the monthly
distribution of thunderstorm data.
Table 4.7 Description of thunderstorm data.
Stations
Statistics
Minimum
Maximum
Range
Mean
Std. Dev.
Variance
Sum
N
Sydney Airport
TRF
1
9
8
2.1
1.4
1.9
531
252
TRA
0.1
265.5
265.3
21.9
29.5
868.9
5533
252
Sydney R.O.
TRF
1
9
8
2.1
1.4
1.9
501
237
TRA
0.1
334
333.8
23.4
34.5
1187.4
5626
237
Richmond
TRF
1
8
7
2.5
1.7
2.9
561
227
TRA
0.1
126
125.8
20.1
20
405
4568
227 N = The number of months with thunderstorm rainfall (> 0.1 m m ) in the sample T R A = Thunderstorm Rainfall Amount, TRF = Thunderstorm Rainfall Frequency
It is evident from Table 4.7 that the total number of thunderstorm rainfalls recorded (1960-
90) at the Richmond station with 561 thunderstorms is greater than the Sydney Regional
Office and Sydney Airort stations with 501 and 531 thunderstorms respectively. In
contrast, the total amount of thunderstorm rain at the Sydney Regional Office (5626 m m )
and Sydney Airport (5533 m m ) is higher than at the Richmond station (4568 m m ) .
4.3.1 Air Temperature
Air temperature was primarily assumed as a function of the amount of solar radiation
received on the ground which can be an important climatic factor affecting thunderstorm
activity (Critchfield, 1987). Table 4.8 summarises air temperature data on a monthly basis
between 1960 and 1990 only for those months having thunderstorms with at least more
than 0.1 m m rainfall. The data are measured for the three selected stations.
Table 4.8 lists average maximum and minimum temperatures and extremes for three
stations in the Sydney region. It is evident that there are considerable differences in
temperature between these three stations. Generally, the highest extremes and variance
occur at Richmond, while stations, which are located in the east of Sydney, show less
variability. Figure 1.5 shows the average daily temperature for different months in the study
area.
CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables 89
Table 4.8 Means and extremes of temperature at three selected stations.
Line 1: Mean Daily Max. Temperature Line 2: Mean Daily Min. Temperature Line 3: Average Daily Temperature N= Number of months
Stations
Sydney Airport N=252
Sydney R.O. N=237
Richmond N=227
1 2 3
1 2 3
1 2 3
Mean
22.91
14.10
18.51
22.88
15.15
19.01
25.33
13.18
19.26
Min.
16 5.2 10.6
16.2 7.1 11.65
16.6
2.8 9.95
Max.
29.2
21 24.6
28.6
21.1
24.4
33.3
19.5 25.65
Range
13.2 15.8
14
12.4
14 12.75
16.7
16.6
15.7
Std. Dev.
3.43
4.22 3.78
3.17
3.73
3.43
3.94
4.33
4.04
Variance
11.8
17.82
14.33
10.07
13.94
11.77
15.56
18.83 16.36
4.3.2 Sea Surface Temperature
In the Sydney area, it has been assumed that the sea-surface off the coast has a major
influence on rainfall on a regional scale (Priestley, 1964, 1970; Hopkins and Holland,
1994). This hypathesis has never been tested for thunderstorms alone. Also, it was assumed
that the ocean waters adjacent to the coast can provide atmospheric moisture, and as a
result, affect the temperature patterns in the region. Table 4.9 gives the average monthly
sea-surface temperature.
CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables _20
Table 4.9 Monthly and yearly sea-surface temperature data (°C) at Port Hacking. Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Yearly 1960
1961
1962
1963 1964
1965
1966 1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982 1983
1984
1985
1986
1987
1988
1989
1990
Mean
21.4
20.8
21.6
19.3 21.5
20.5 21.1
20.8
22.2
21.7
21.9
21.9
19.9
21.9
21.4
20.7 21.7
20.6
21.6 21.0
21.4
21.8
21.6
21.0
18.6
21.7
22.5
21.3
20.4
20.5
21.0
21.1
22.0
21.7
22.0
22.1 22.3
21.8
21.4 21.7
20.5
19.9
21.4 22.8
20.4
21.6
22.4
21.5
23.1
20.6
22.3 21.5
22.1
23.0
21.3 22.3
22.5
20.6
22.9
23.8
22.5
22.1 22.3
21.9
21.7
22.1
21.8
22.2
20.8
21.0 20.9
21.4
22.7
19.9
22.4
20.4
21.4
22.4
21.5
21.9
23.4
21.4
21.3
21.4
22.3
22.6
22.0 19.7
21.9
20.3
21.3
21.0
21.4
21.1
24.1
21.6
20.3
18.2 20.6
21.7
20.5
19.3 21.1
21.0
21.4
20.1
21.1
20.3
20.4
20.9
22.4
20.0
20.9
20.9
21.4
20.2
21.1
21.6
20.9 20.4
19.9
21.3
20.4
19.8
19.9
21.0
23.1
20.7
18.3
18.5 19.2
19.1 19.2
19.3 19.8
19.4
18.9
19.3
18.3
18.9
19.0
19.4
19.5
19.7
20.6
19.0
20.1 18.8
19.0
20.4
20.9 18.9
18.9
19.2
20.4 19.0
19.5
20.6
19.9
19.4
17.0 17.1
17.6
18.2
18.7
18.4 19.0 17.5
17.4
17.4
16.6
17.2 17.8
17.4
17.7 17.8
19.5
16.9
17.6 17.9
18.1
18.1
18.7
17.1
17.9
18.0
17.8
17.7
18.0
18.5
18.2
17.8
17.8
15.9
16.7
17.3
16.9
17.7 16.6
16.0
16.0
17.3
15.1
15.9
17.4
17.3
16.7
16.3
18.8
15.4
16.9
17.0
16.2
16.7
17.4 15.5
15.9
16.7
17.2
18.4
18.0
17.3
16.6
16.8
17.1
15.4
15.5
15.9 16.9
17.2 16.7
16.4 16.1
16.2 14.8
16.5
16.9
18.3
16.8 17.0
18.6
15.2
16.3 15.7
16.8
16.0 16.6
16.3
15.4
16.2
16.3
16.4
16.0
15.7
16.0
16.4
16.9
15.4
16.0
16.5
16.5
16.7 17.6
16.5
16.5
16.0
16.5
16.0
16.6
16.8
16.3
17.7
18.3
17.7
16.2 16.1
16.4
17.1
17.2 16.1
16.7
18.0
16.9
17.0
16.5
16.0
16.8
16.7
18.1
18.1
16.9
16.5
17.3
17.5 18.2
17.1
16.1
16.5
17.6
16.4
17.3 18.1
17.3 17.7
17.8 17.8
17.4 16.4
17.0
17.2
18.0 18.3
16.8
18.3
18.4
18.5
19.1
19.4
16.4
17.5
19.1
18.0 19.0
18.3
18.8
18.9
19.1 18.3
17.3 20.1
18.5
16.9
19.1
19.4
18.7
18.8
19.6
17.2
18.5
18.5
18.0
18.3
18.4 19.4
20.8
19.3
19.0
18.9
19.6
18.2
16.2
18.6
19.0
19.5
19.5
19.8
19.3
19.7
20.1
19.6
20.4
20.3
20.2
19.9 21.8
21.0
20.4
19.7
20.9
19.6
19.6
20.1
19.7 20.6
21.6 20.1
20.5
21.9
19.3
21.1
20.0
18.9
22.1
20.2
19.1
18.4
18.9
18.9
19.1
19.0
19.3 18.8
18.8
18.7
18.7
18.6
19.0
19.5
19.3
19.1
20.3
18.5
19.1
18.7
19.0
19.5
19.5 18.8
18.8
19.3 19.4
19.4
19.2
19.1
19.4
19.1
0 U
tn
SST - at Port Hacking
24T
22± 20+ 18--16--14--12--
+ + + + + + Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Average Monthly Variation of SST (1960-1990)
Figure 4.1 Average monthly variation of the sea surface temperature in °C.
CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables 91
Figure 4.1 plots the average monthly variation of sea-surface by calender month over the
period 1960 to 1990 (372 months), at Port Hacking, with 34°05 S and 151°12 E for 0
depth. It is clear that sea-surface temperature had a maximum of 22 °C in February and a
minimum of 16.40 °C in August.
4.3.3 Air Humidity
Humidity, or the water vapour content of the air, it is suggested, is an important
meteorological element both in terms of the development of weather patterns and in terms
of the efficiency of living systems such as convection cells (Lutgens and Tarbuck, 1982).
Both the absolute humidity and relative humidity are known as important indicators in
computing atmospheric moisture amounts in creating or affecting convection systems (see
Chapter 2). M a n y climatologists recommend that the absolute humidity, as measure of
independent variable, should be used in correlating air humidity to thunderstorm variations.
They regard the relative humidity as more or less meaningless. Unfortunately, the absolute
humidity, as an important climatic variable, was not availble in the Bureau of Meteorology
sources. Thus, for estimation of absolute humidity, there is a need to have other humidity
parameters such as: dry-bulb and wet-bulb temperatures, atmospheric pressure or
saturation vapour pressure at the wet-bulb temperature (Abbott and Tabony, 1985). Still,
the calculation of absolute humidity is a complex task and there is a variety of methods,
using a set of different equations. Without such data, this is very costly in terms of
computing time, with difficulty in evaluating of results (Sargent, 1980).
Therefore, in this study the relative humidity was used, which is the maximum amount of
water vapour that the atmosphere can hold. The relative humidity of the air at a given
temperature is the ratio (expressed as a percentage) of the actual vapour pressure to the
saturation vapour pressure. Relative humidity is defined (World Meteorological
Organization, 1988) by
U= 100 (e/ed) per cent,
where e = ambient vapour pressure in millibars, and
ed = saturation vapour pressure in millibars with respect to water at the same
pressure and temperature.
Generally, this is dependent on the temperature of the air and increases with increasing
temperature. Consequently, in this study, if temperature is to be used as a measure of
convection activity, relative humidity must also be taken into consideration.
According to the report of the Sydney's Bureau of Meteorology (1991a), the maximum
relative humidity nearly always occurs near dawn and the minimum at about noon during
CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables 92
summer, and at 2 pm or 3 pm during winter, at about the time of maximum temperature.
The mean relative humidity at 3 p m in the three stations in the Sydney region is given in
Table 4.8. This time of day was adjusted in the analysis because many of the convection
activities take place in the afternoons. This matter was discussed in Chapter 3.
Table 4.10 Simple statistics of the relative humidity (in per cent) in the Sydney region, from 1960 to 1990 (372 months).
Station Sydney Airport Sydney
RO. Richmond
Mean 55.22
56.5
47
Min. 30
33
21
Max. 78
72
71
Range 48
39
50
Std. Dev. 7.7
7
8.7
Variance 59.4
48.9
75.3
Also, Figure 4.2 shows the average monthly variation of the relative humidity at 3 p m by
the calender month over the period of 31 years. The period of analysis is 1960-1990.
Figure 4.2 Monthly distribution of the mean relative humidity at three stations in the Sydney region (1960-90).
4.4 Correlations Matrices of Variables
To find possible associations between the different variables some analytical procedures
have been established. First, in order to determine the associations between thunderstorm
rainfall frequency and thunderstorm rainfall amount as dependent variables, and air and sea-
surface temperatures and air humidity as independent variables. A n initial correlation has
been separately introduced for each group of variables. The correlation found does not
depend on data distribution normality. Because of the nature of thunderstorm data no
CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables 9J
transformation procedure was applied. Table 4.11 summarises the correlation matrix of
thunderstorm items in the sample for the period beginning from 1960 and ending to 1990.
Table 4.11 The correlation matrix (associations) between dependent variables.
Variables
T R A Sydney Airport
T R A Sydney R.O.
T R A Richmond
T R F Sydney Airport
T R F Sydney R.O.
T R F Richmond
Yi
Y 2
Y 3
Y 4
Y 5
Y 6
Y i
1
0.82
0.40
0.64
0.57
0.45
Y 2
1
0.43
0.56
0.61
0.46
Y 3
1
0.44
0.48
0.72
Y 4 Y 5 Y 6
1
0.80 1
0.64 0.67 1
All associations are at 0.05 significant level T R A = Thunderstorm Rainfall Amount, TRF = Thunderstorm Rainfall Frequency
In this correlation matrix, each of the thunderstorm items was correlated against the others.
They were interrelated in the range of approximately 0.4 to 0.82. These correlation
coefficients m a y not show, on average, high correlation, but they indicate that all
dependant variables are positively associated, and all associations are significant at 0.05
level. Also, an attempt was made to see if there are any associations between air, sea-
surface temperatures and humidity among the three above-mentioned stations. Therefore,
again, a simple correlation method has been employed in order to estimate the associations
between independent variables.
Table 4.12 Correlation matrix for independent variables.
Variables
SST at Port Hacking
Sydney Airport
Sydney R.O.
Richmond
Max. Tem.
Min. Tem.
Rel. Hum.
Max. Tem.
Min. Tem.
Rel. Hum.
Max. Tem.
Min. Tem.
Rel. Hum.
* i
x2
x3
x4
x5
x6
x7
x8
x9
Xio
*i
1
.75
.80
.50
.77
.81
.56
.44
.78
.30
x2
1
.95
.22
.99
.96
.40
.60
.94
-.12
x3
1
.44
.95
.99
.56
.58
.98
.13
x4
1
.25
.42
.92
.11
.47
.79
x5
1
.97
.40
.60
.94
-.08*
x5
1
.56
.59
.98
.09*
X 7
1
.20
.60
.70
x8
1
.57
-.13
X 9
1
.14
Xio
1
* Non-significant at 0.05 level
Table 4.12 shows that there are generally high positive correlations among independent
variables. It also may indicate that the association between sea-surface temperature and air
temperatures at Sydney Airport and Sydney Regional Office are higher than the association
CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables JL4
at Richmond station which is the weakest. It must be noted that the relationship between
temperature and air humidity is not entirely independent. According to these correlations, it
can be concluded that all independent variables which have been used in this study have a
com m o n relationship to each other.
In one stage further, to examine the coefficient of correlation between the thunderstorm
data and the independent variables, it was decided to find the possible correlations,
However, a problem which may arise in the study of thunderstorms on the basis of monthly
rainfall is brought about by the inclusion of the data from those months without any
thunderstorm rainfall. If these months are included in the computation they will increase the
correlation coefficient significantly without entailing any increase in information on the
thunderstorm rainfall incidence. This problem has been identified by Cornish, Hill and
Evans (1961) and Sumner and Bonell (1990). For this reason, months with zero values
were excluded from the analysis.
Table 4.13 summarises the regression analysis between the climatic variables and
thunderstorm rainfall values for the 3 stations. This table reveals distinct associations
between independent variables and thunderstorm rainfall frequency and thunderstorm
rainfall amounts in the study area. However, there are two points to consider. Firstly, in
some cases associations are not at significant levels. Secondly, as can be seen from Table
4.13, there is a general decline of relationships between dependent variables and sea surface
temperature with the distance inland. For example a 0.0009 significant level at Sydney
Airport declines to 0.05 at Richmond for thunderstorm rainfall frequency values.
Table 4.13 Linear regression coefficients of dependent variables by independent variables.
Dependent Variables Stations and
Independent Variable
Sydney Airport
SST Max. Air Tem.
Min. Air Tem.
Rel. Hum.
Sydney R.O.
SST Max. Air Tem. Min. Air Tem.
Rel. Hum. Richmond
SST Max. Air Tem. Min. Air Tem.
Rel. Hum.
Thunderstorm
Rainfall
R*
.21
.29
.32
.20
.14
.22
.25
.22
.12
.29
.34
.13
Frequenc p**
.0009
.0001
.0001
.001
.05 .0005 .0001
.0005
.05 .0001
.0001
.04
Thunderstorm
Rainfall Amount
R
.17
.12
.17
.19
.13
.09
.12
.17
.05
.07
.16
.20
P
.008
.05 .008
.002
.05 NS .05 .007
NS NS .01 .002
* Regression coefficient, ** Probability level NS = Non-significant correlation at 0.05 level
CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables £5
4.5 Multiple Associations Between Variables
At this stage of the data analysis, to ensure that each major independent variable makes a
statistically significant contribution to the predictable variance of thunderstorm rainfall
amount (here the focus is on h o w much rather than h o w often), and in order to examine the
relative importance of each climatic variable, a stepwise multiple regression technique was
introduced. This technique was considered a 'stepwise solution' which is c o m m o n
computational procedure in regression analysis having been used in many studies (Ohring,
1972). For example, Bryant (1985a) used this technique for correlating beach erosion with
sea-level rises. Therefore, this technique, as a rank ordering of the total correlations of
major independent variables with thunderstorm rainfall, was separately applied to two
stations (with the longest records), Sydney Airport station (the nearest station to Port
Hacking) and Richmond station (the furthest station from Port Hacking).
Generally, this technique can predict as much variance in the dependent variable as is
possible from the composite of independent variables. This process is complicated by the
fact that the independent variables may be correlated with each other and, consequently,
each predicts the "same part" of the variation in the dependent variable. For example, sea-
surface and air temperatures may correlate with each other (see Table 4.12).
By using a stepwise multiple regression procedure it was therefore possible to evaluate
systematically the relative contributions of important variables in the explanation of
thunderstorm rainfall. This statistical technique was also used in order to measure whether
there is a cumulative effect of several variables on thunderstorm behaviour. Independent
variables in this regression equation were mean daily maximum and minimum temperatures,
sea-surface temperature and mean relative air humidity, with thunderstorm rainfall as the
dependent variable. The independent variables were selected according to the literature
framework of this study, because in Chapter 2, it was hypothesised that all of these four
factors would contribute significantly to the explanation of thunderstorm rainfall.
Each independent variable was entered into the regression equation in order to determine
its unique contribution in relation to the other three. The order in which the independent
variables are entered into the equation had no impact on the outcome because each variable
is treated as though it is the last variable to be entered. The stepwise regression procedure
selected the strongest independent variable in the first stage and at each stage a new
variable was added to the equation. The results of the stepwise regression are presented in
Tables 4.14 (a) and 4.14(b).
The independent variables were introduced into the regression equation, summarised in
Tables 4.14 (a) and 4.14 (b), in the order in which they increased the explained variance in
M
thunderstorm rainfall. In both stations, the rank ordering of variables in terms of their
predictive strength are: mean daily minimum air temperature; sea-surface temperature;
mean relative air humidity; and mean daily maximum air temperature.
The first step identified the minimum air temperature as the best single predictor of
thunderstorm rainfall. Sea-surface temperature is the second strongest predictor of
thunderstorm rainfall ( F[30.8], p< 01). This variable adds 6 per cent to the explained
variance in thunderstorm rainfall at the Sydney Airport station.
Table 4.14 (a) Results of stepwise multiple regression analysis of thunderstorm rainfall at the Sydney Airport station (n=252 ).
Step
Number
1
2
3
4
Total
Predictor Variable
Minimum Air Temperature
Sea-Surface Temperature
Mean Relative Humidity
Maximum Air Temperature
Multiple
R
0.35
0.42
0.44
0.45
R
Square
0.12
0.18
0.19
0.20
Variance
Added in
%
12
6
2
1
21
F ratio
to enter *
30.8
24
18
14
Number of
Variable in the
Equation
1
2
3
4
All F values are significant at 0.01 level
Table 4.14 (b) Results of stepwise multiple regression analysis of thunderstorm rainfall at Richmond station (n= 227).
Step
Number
1
2
3
4
Total
Predictor Variable
Minimum Air Temperature
Sea-Surface Temperature
Mean Relative Humidity
Maximum Air Temperature
Multiple
R
0.32
0.34
0.35
0.36
R
Square
0.11
0.12
0.13
0.137
Variance
Added in
%
11
1
1
1
14
F ratio
to enter*
29.4
16.2
11.4
10.1
Number of
Variable in the
Equation
1
2
3
4
* All F values are significant at 0.01 level
Tables 4.14 (a) and 4.14 (b) also show a significant relationship between the relative air
humidity at each station and thunderstorm rainfall. This variable is recognised as the third
major predictor of thunderstorm rainfall and adds about 2 per cent to the explained
variance in the equation. W h e n the 4 variables are included in the regression equation, the
amount of explained variance in thunderstorm rainfall behaviour increases to about 21 per
cent at the Sydney Airport and about 14 per cent at the Richmond station. The stepwise
CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables 91
relationships are not high, and despite the significance, the variance explanied is very low,
therefore, other factors must be operating.
As the result of the stepwise regression technique indicated, all 4 independent variables
have significant relationships with thunderstorm rainfall. A comparison of these tables
clearly indicates that both sea-surface and air temperatures can affect the thunderstorm
occurrence throughout the Sydney region and they can explain some of the variance
statistically. However, the percentage of variance explained is not high generally, and it is
not the same for the two selected stations which are located in the east, near the coast, and
in the west of the Sydney region.
4.6 Discussion
In this chapter - using the available data taken from the limited of stations - first, some
descriptive statistic techniques have been used to summarise, present and compare the
distribution of thunderstorm data on a monthly basis. Then, to find the possible
associations among some of the climatic variables which may affect thunderstorms,
different kinds of statistical procedures were considered. Some simple correlation methods
and a stepwise multiple technique were used to find the percentage contribution of each
independent variable upon thunderstorm rainfall. Results indicated that there are possible
causal relationships between the above-mentioned climatic variables and thunderstorm data
specially for coastal stations. This relationship becomes weaker further inland.
The evidence involves relatively low correlation coefficients, similar to those reported by
Priestley (1964) and Hirst and Linacre (1978) for monthly rainfall values (see Chapter 2).
The results of the stepwise regression technique indicated that thunderstorm rainfall
amount (explaining about 21 per cent of the variance at the Sydney Airport station and 14
per cent at Richmond) is associated with three main climatic factors; air, sea-surface
temperatures and air humidity. Three distinct effects come to mind as likely to cause these
associations between variables in the region.
4.6.1 Effects of Sea-Surface Temperature
The direct effect of sea-surface temperature upon the rainfall process was shown by
Priestley and Troup in 1966. The ocean waters adjacent to the coast can provide
atmospheric moisture and moderate temperatures and therefore affect rainfall patterns in
the region (Rochford, 1977). Apparently the importance of sea-surface temperature to the
climate of the Sydney region has been recognised for a long time. For example,
investigation has revealed that the east coast current which carries w a r m tropical water
southwards along the N e w South Wales coast can affect the climate of the region (Lough,
1992). The positive correlations between rainfall in eastern Victoria and the w a r m sea-
CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables 28
surface temperatures from the Coral Sea, which was found by Whetton (1989), also
supports this positive correlation. Therefore, although the ocean temperature varies in both
space and time, it can also significantly influence the distribution of rainfall patterns in the
region.
Supposing other affecting factors to be equal, the warmer the water, the warmer and more
moist will be the lower layers of air reaching the coast. In addition, there will probably be a
greater tendency for convection activity. In 1978, Hirst and Linacre indicated that the
onshore winds, which can control the sea-surface temperature, may also enhance
convective rainfall by bringing in moist warm air to coastal areas. In this case, a warmer sea
surface would cause instability of the coastal atmosphere, increasing the tendency to
convective rainfall.
Although it is evident from the result of this study that the association between sea-surface
temperature and thunderstorm rainfall is positive during the calendar year, it seems this
association is stronger in autumn/winter than the spring/summer seasons. This was
emphasised by Colquhoun and Batt, in a personal conversation (Bureau of Meteorology,
N S W Regional Office, 1994). This is when the land-sea temperature difference is greatest
(Holland et al. 1987). Also, Hopkins and Holland (1994), found that the East-Coast
Cyclones show a preference for formation in the autumn/winter months which occasionally
create very heavy rainfalls along the east coast ranges.
On the other hand, the results of this study indicate that the association between coastal
sea-surface temperature and thunderstorm rainfall at the Richmond station (which is more
than 55 K m inland) is less or even non-significant. A simple explanation may be that in the
west of the Sydney region, because there is less moisture and because it is further from the
warm easterly winds off the ocean, the chance of thunderstorm occurrence with intense
rainfall is lower than in the coastal areas.
4.6.2 Associations Between Air Temperature and Thunderstorms
A relatively high association between air temperatures (minimum and maximum) and
thunderstorm rainfall amount probably indicates that the incidence of high air temperature
can cause high thunderstorm activity in the region. This simply means that the air
temperature should also be considered as one of the factors which is able to create or
enhance thunderstorm activity.
Many researchers in the field of thunderstorm activity have found that air temperature is an
important climatic factor in creating or enhancing a convection system. For example,
Lutgens and Tarbuck (1982 p:237) wrote:
CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables 92
"All thunderstorms require warm, moist air, which, when lifted, will release sufficient latent heat to provide the buoyancy to maintain its upward flight. Although this instability and associated buoyancy are triggered by a number of different processes, all thunderstorms need an unstable atmospheric environment in which the instability can be enhanced by high surface temperatures."
To these features can be added the effects of unequal heating of the land surfaces,
particularly in summer months when the clear areas can be warmed rapidly by solar
radiation (Baines, 1990). Probably this uneven heating can generate vigorous convection
which leads to the growth of storms in a matter of hours. It has been shown in chapter 3
that most thunderstorms develop in the afternoons in the spring and summer months, when
the potential for convection is usually the greatest and adequate high air temperatures are
available.
In the USA, Benjamin (1983) found that some severe thunderstorms were the result of
differential heating, differential advection and local topography. In addition to these
factors, Golde (1977) has shown that vigorous thunderstorms can occur along an active
cold front or in squall lines in the warm air ahead, at any time of the day or night.
It may be supposed that surface heating is generally not sufficient, in itself, to cause
thunderstorm activity, and any factor that can destabilise the air, aids in generating a
thunderstorm. A s Smith (1975 p:13) mentioned:
"The importance of surface features increases markedly as the scale
of climatic reference clirninishes and it is only at the very lowest
levels of the atmospheric boundary layer that surface influences
become strong enough to create really special phenomena".
A high air temperature may indirectly cause, or enhance, other associated atmospheric
phenomena which should be considered as important factors in introducing or causing a
convection activity in the region. M a n y investigators (for example, Simpson, 1964 and
Atkinson, 1981) have highlighted the importance of unequal heating in coastal plains. They
linked these phenomena to both local convection and to the role of sea breeze fronts in the
generation and enhancement of meso-scale systems such as thunderstorms.
In Australia, sea-breezes have been studied extensively by some researchers who have
found that the summer months are times of the greatest sea breeze development (Lyons,
1977). Hobbs (1971) noted that for the N S W coast the incidence of sea breezes increases
as the summer progresses. Clarke (1955 and 1960) and Drake (1982) suggested that sea
breeze penetration is greatest in southern of N e w South Wales. For example, at Nowra,
CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables 1M
about 15 km north-west of Jervis Bay, Mathews (1982) found that the magnitude of the
sea-breeze component depends on the land-sea temperature difference. The occurrence of
temperature differentials leads directly to pressure variations that give rise to air movement.
At the mesoscale level, this effect may cause local sea-breezes and formation of thermal
lows over land masses in summer. Also, Sumner (1983b) proposed that local winds and
proximity to the sea may cause storm development which is dependent largely on the
presence of an escarpment. These studies generally indicate that the diurnal variation of
land and sea heating is the cause of sea-breezes.
In the Sydney coastal areas, because of the apparent difference between the temperatures
of the land and of the adjacent ocean, sea-breezes are generated by cool air from the ocean
replacing warmer air rising over the land. In 1974, Linacre and Barrero showed the
positions of the sea-breeze front at various times in the Sydney region. They concluded
that, although the sea-breeze front moves inland to a distance which depends on the day-
length and the speed of the front, it is stronger in the mid and early-afternoon, particularly
in summer months. Therefore, the sea breeze is generally most pronounced in the late
spring and summer, and during the early afternoon hours which have the highest daily
temperatures. This is an atmospheric phenomenon which may contribute, in a general way,
to the convection mechanisem and as a result to thunderstorm enhancement (Abbs and
Physick, 1992). This may be one of the reasons thunderstorms are most common in the
afternoon and warm months of the year.
4.6.3 The Role of Air Humidity
Finally, the role of the moist air available in the surrounding atmosphere can also be
statistically seen to be an important factor in initiating convection activity, because it makes
the atmospheric environment more unstable. As Moran and Morgan (1991) indicated,
thunderstorms usually develop in unstable atmospheric environments as a consequence of
uplift caused by one or more of the following: (1) frontal activity, (2) orographic effects,
(3) surface convergence, or (4) intense solar heating of the land surface.
According to these mechanisms, it was suggested that the available moisture in the air can
help a convection development when dense cold air overlies warm, moist air which is less
dense. Therefore, many thunderstorms require warm, moist air which will release sufficient
latent heat to provide the buoyancy necessary to maintain its upward flight.
Although this instability and associated buoyancy are triggered by a number of different
processes, all thunderstorms need a moist atmosphere to keep their life cycle. A trigger
such as solar heating, a front, or a trough-line can then begin the development of a
thunderstorm. Thus, high heat energy and water vapour stored in the air can be converted
CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables Ml
into wind and electrical energy introducing much more instability into a convection system,
which may then organise and produce much rain.
Eagle and Geary (1985 p:2) both point to the importance of moist air in the increasing of
rainfalls from well-organised and widespread thunderstorms in the region. They suggested
that:
'The atmosphere is modified by the ocean surface over which it moves. Given a sufficient transport time over the sea, air in the lower layer will achieve a balance with the temperature and vapour pressure at the underlying surface. With favourable conditions the water vapour may be adverted vertically through the air column. Ocean conditions have no obvious immediate influence to the coastal location but if winds are onshore a short term effect may ensue, as is the case with coastal shower situations,.
They found that during early November, 1984 a north easterly airstream originating in low
latitudes, was moving coastward across the W a r m East Australian Current. At this time the
waters of this current were one to two degrees above average for the time of year. As a
result, the temperature and humidity of the airstream which affected the coastal areas, was
largely sustained.
In coastal locations, this positive temperature anomaly was very favourable to the
maintenance of a w a r m moist air mass and then conducive to thunderstorm rainfall with the
presence of uplift mechanisms. All previous evidence has indicated that, on average, coastal
areas in the Sydney region experience many more gradient winds which are both stronger
during the day or night, and which have a higher percentage of humidity.
As the results of the statistical analysis have indicated, there is a considerable amount of
unexplained variance which may suggest other independent variables need to be
incorporated into the regression model. It seems certain that suggested climatic variables
are not the only factors which explain all the variation of thunderstorm rainfall in the
region. This occurs because, in a complex three dimensional atmospheric environment, in
which convection activities take place, there must be several independent variables
affecting the development of a thunderstorm system.
More importantly, it is well known that the occurrence of a thunderstorm also depends
upon the vertical distribution of temperature and humidity in the atmosphere. Most often,
moving convection systems track from places where they create and affect the other
surrounding low land areas. Therefore, it is clear that accurate atmospheric information on
the convective motions within, about and beneath a thunderstorm system, is necessary to
CHAPTER FOUR Thunderstorm Rainfall and Climatic Variables M2
understand the nature of convective activity and, as a result, to explain the associated
rainfall amount from thunderstorms for a specific location.
While the quality of data and the number of stations used in this study may not be precise
enough to confidently take the calculated coefficients as predictive values of thunderstorm
activity in the region, the results obtained statistically, indicate that the air and sea-surface
temperatures, and air humidity can be linked to thunderstorm development and amount of
precipitation. These factors seem to be more effective climatic factors, particularly in the
coastal areas where moist air currents and warmer sea-surface temperatures can cause
instability of the atmosphere, increasing the tendency to convective rainfall. This last factor
is probably less important further away from the coast where the moist local winds arrive
later or not at all.
4.7 Summary and Conclusion
Results obtained statistically demonstrate that the associations between some of the
environmental-climatic variables; such as air and sea temperatures and relative air humidity
and thunderstorm rainfall, are mainly positive. There are some possible causal correlations
among independent and dependent variables, particularly in the east of the study area, near
the ocean. They may reflect the combined effects of all these environmental variables upon
thunderstorm rainfall. It appears that the correlation between sea-surface temperatures and
thunderstorm rainfall roughly decreases with distance from the coast. In contrast, the
correlation between air temperature and thunderstorm rainfall amount becomes stronger
with increasing distance from the coastline.
In summary, it is more likely that the effects of above-mentioned climatic factors upon
thunderstorm rainfall patterns result from a complex climatic interaction. It is clear from
the total variance discovered that there are definitely other independent variables which are
also responsible for spatial variation of thunderstorm rainfall amounts in the region.
Therefore, to visualise these variations in space, the variation and distribution of
thunderstorm rainfall will be examined in Chapter 6.
CHAPTER FIVE A Review on GIS Systems Ml
CHAPTER 5
A REVIEW ON GIS SYSTEMS
5.1 Introduction
GIS can be used to visualise the spatial pattern of thunderstorm rainfalls by emphasisng
the physiographic features of the Sydney region. The current chapter reviews an overal
GIS methodology which could be applied for data with a spatial nature, and follows a
progression of topics, becoming more specialised in the following chapters. Chapters 6
and 7 will, thus, deal with analysis and modelling of thunderstorm rainfall data and
physiographic parameters of the study area.
The literature on GIS is vast and spread over a large number of areas, representative of
many disciplines and covers an enormous number of applications. The most relevant
sources for the material presented in this thesis are, therefore, selected and explained.
Sections 5.2 to 5.6 bring together the relevant conceptual issues of GIS. First, some of the
currently used definitions, the common purposes, the principle mechanisms and operating
systems of GIS are defined. Then, the use of GIS for geographical applications and its use
in climatology are reviewed. The application of GIS techniques in spatial modelling of
thunderstorm rainfall is also examined. Data sources and those technical aspects of the
S P A N S (which is an acronym and stands for SPatial ANalysis System) are explanied in
sections 5.7 and 5.8 respectively. Finally, GIS potential errors are outlined in section 5.9.
5.2 What is a GIS ?
GIS is a computer technology consisting of hardware and software that is used to
produce, organise, and analysis information (Aronoff, 1989). In fact, GISs are computer
software for managing data that are spatially distributed over the Earth (Bonham-Carter,
1994). Maguire (1991) states that GISs are computer systems capable of storing,
analysing, manipulating and displaying spatial data from the real world which can be
represented spatially in a computer environment (Dangermond, 1986).
Accordingly, GIS is able to provide natural resource managers with the tool to merge
spatial data and their attributes into computerised data base systems allowing input,
storage, retrieval and analysis of geographically referenced data (Calkins and Tomlinson,
1977). With this capacity for spatial and temporal modelling of the real world, GIS as a
technology has been developed to accomplish the complicated tasks which are grouped
CHAPTER FIVE A Review on GIS Systems WI
together as 'GIS' functions. Two selected definitions of GIS are as follows; Aronoff (1989
p:39) "any manual or computer based set of procedures used to store and manipulate
geographically referenced data". Koshkariov et al. (1989 p:259) "a system with advanced
geo-modelling capabilities". Many of the definitions are relatively general and cover a wide
range of subjects and activities (Tomlinson et al., 1976; Moore et al., 1981).
GISs have three important components- computer hardware, sets of application software
modules and a proper organisation context (Burrough, 1989). These three components
need to be in balance if the system is to function satisfactorily. Maguire and Dangermond
(1991) believe that four basic elements of GIS, which operate in an institutional context
are: computer hardware, computer software, data and liveware. However, some
researchers think that GISs are the result of linking parallel developments in many separate
spatial data processing disciplines (Cliff and Ord, 1981).
Essentially, all these disciplines are attempting the same sort of operation, mainly to
develop a powerful set of tools for collecting, storing, retrieving, transforming, and finally
displaying spatial data from the real world for a set of particular purposes. These sets of
tools were combined to constitute a GIS environment (Burrough, 1989). In other words,
GIS should be thought of as being very much more than a means of coding, storing, and
retrieving data about aspects of the earth's surface (Goodchild and Kemp, 1990). In fact,
GISs are designed to bring together diverse spatial data sources into a unified framework,
often employing a variety of digital data structures, and representing spatially varying
phenomena as a series of data layers as models from the real world (Prisley, 1986; Rhind,
1988).
5.3 Purpose of GIS
The purpose of using a GIS system for geographical and other applications can be reduced
to about six activities dealing with spatial data: 1) organisation, 2) visualisation, 3)
combination, 4) analysis , 5) modelling, and 6) query (Bonham-Carter et al., 1988;
Burrough, 1989; Goodchild and Kemp, 1990)
1) Organisation is the ordering of information according to logical links (Bonham-
Carter, 1994). Anyone w h o has collected a large mass of data for a particular purpose
knows that data organisation is essential. Data can be arranged in many different ways, but
all the data has to be spatially referenced in GIS. For example, a table of geographic data
may be interesting for viewing relationships between elements, but without knowing the
locations of samples, the interpretation of spatial patterns and relationships with other
spatial data, such as geographic features, cannot be made and understood (Johnston,
1987). A GIS must be concerned not only with location, but must also organise data to
allow the extraction of other types of information (Aronoff, 1989). Because, the GIS can
CHAPTER FIVE A Review on GIS Systems Ml
organise data both by spatial and non-spatial attributes, the efficiency and type of data
organisation effects all the other five activities and is therefore of fundamental importance
(Maguire, 1989).
2) Visualisation is an important technique for analysing, explaining and
understanding the distribution of a phenomenon on the surface of the earth (Buttenfield,
1987). Using new technology capacities, the graphical capabilities of computers are
exploited by GIS for visualisation (Dangermond and Smith, 1988). Generally, visualisation
is the assessing of information through the use of sight which is normally carried out using
the video monitor, but other output devices such as colour printers are used for hard-copy
displays (Intera Tydac, 1992a). Often, visualisation is enhanced in a GIS system by
specialised methods using colour, perspective, shadowing and other means. One of the
immediate benefits of this function of GIS is that visualising data stimulates the mind in
ways which are different from traditional data analysis procedures (Cuff and Mattson,
1982).
3) In a GIS, combination is the bringing together of data sets. Data used in GIS
often come from many different sources, are of many different types (even with different
spatial nature) and are stored in different ways (Flowerdew and Bantin, 1989). GIS
provides the tools and method for combining, or integrating, these data into a format
which allows the data to be compared. This process of creating a c o m m o n form of the
data or the bringing together spatial data from a number of sources, is described as data
integration. The role of the GIS as an 'information integrator' was examined by several
researchers on various approaches. D o E (1987 p:2) states that, 'The benefits of a GIS
depends on linking different data sets together.' Dangermond (1989 p:25) said that:
'A GIS brings information together, it unifies and integrates that information. It makes available information to which no one had access before, and places old information in a new context. It often brings together information which either was not or could not be brought together previously'.
This is one of the really powerful features of GIS in which the ability to link several maps
together provides various kinds of models. The benefits that follow the integration of
diverse information are widely recognised.
4) One of the important stages in the GIS environment is the analysis of the results
of previous stages or the process of inferring meaning from data (Berry, 1986). In fact,
analysis is the interpretation and the study of data and information that have been
collected. With GIS, the relationships between different spatial data and their associated
features can be measured and understood. Spatial analysis in a GIS simply means, the
analysis of spatial data. For instance, the area cross-tabulating of two maps may lead to
A Review on GIS Systems 106
useful conclusions about the relationship between the two map layers. Therefore, with GIS
the relationships between different spatial data and their associated features can be
measured and understood (Samet, 1989).
5) Just as it is possible to analyse spatial information to extract knowledge, it is also
possible to use known relationships to model geographically the outcome of a set of
conditions (Intera Tydac, 1992b). This function of GIS is helpful for assessing models
from patterns in the data. Normally, the final purpose of many GIS studies is often for the
prediction and modelling of data. For example, a number of data layers can indicate new
sets of maps which could be combined to predict the suitability of the final desired model
(map). Such a map may then be used as a basis for making exploration or landuse
decisions (Dickinson and Calkins, 1988). In other words, prediction is sometimes a
research exercise to explore the outcome of making a particular set of assumptions, often
with the purpose of examining the performance of a model (Alberti, 1991).
6) Finally, a strong feature of GIS is the ability to query intellectually the underlying
data simply by moving a pointer around on a map. Since all data in a spatial database are
geographically referenced, a pointer to location means access to all data associated with
that location (Intera Tydac, 1993). Spatial query is a complementary activity to data
visualisation, because it would permit the user to find the special circumstances of each
case, by searching the name and other particulars of characteristics of individual
geographic features in the selected locations of interest. Generally, GIS provides tools for
two types of interactive query: geographical information of a location and its attributes
(Unwin, 1981). This powerful function of GIS allows the user to enjoy the dynamic query
of attributes of up to 19 map layers, simultaneously.
5.4 How GIS Operates
At its most basic level, a GIS can be viewed as a simple input / output process. Data goes
into the GIS (such as collected data), some form of processing occurs (averaging of data
for different areas), and information comes out (perhaps in the form of a map). Regardless
of its complexity the input / output view of GIS is a useful starting point from which to
examine h o w the technology actually works. However, in order to understand the basic
operations in a GIS environment, it is first necessary to understand the main structure and
functionality of the GIS in which the data must be processed.
CHAPTER FIVE A Review on GIS Systems Ml
5.4.1 Data Structures in GIS
There are currently three common data structures used by geographical information
systems; 1) vector, 2) raster, and 3) quadtrees (Ibbs and Stevens, 1989). Each structure
has an associated set of characteristics, some good, some bad (Bonham-Carter, 1993).
1) The Vector format was defined as positional data in the form of co-ordinates of
the ends of line segments in a point, line or polygon format (Intera Tydac, 1993). This is
the most c o m m o n method for representing spatial data in which, 2-D space is assumed to
be continuous and allows very precise representation of locations, lengths, distances and
areas. Locations are described by coordinate pairs, and these pairs are the fundamental
building blocks from which spatial entities such as points, lines, and areas are composed.
In a vector structure, points are represented by a single x, y coordinate pair, while liner
entities and area entities (polygons) are composed of straight line segments joining two
coordinate pairs (vertices). The attribute of the values for point, line, and polygon entities
are typically stored independently of the entity's spatial representation. Generally, the
vector structure is ideal for representing point (rainfall stations) and linear features such as
rivers, and for cartographic map production. This structure is also very useful for
topological relations, but is very limiting for overlay modelling procedures (Cook, 1978)
2) The Raster format is spatial data expressed as a matrix of cells or pixels, with the
spatial position implicit in the ordering of the pixels. The simple raster data structure
represents 2-D space as an array of matrix of square or rectangular grid cells. Each grid
cell represents a square or rectangular portion of the Earth's surface. The resolution of
raster data is determined by the size of the cell on the ground, thus, raster data represent a
discrete space where the locational precision is dependent upon the size of a grid cell
(Brown and Norris, 1988). Each grid cell is assumed to have only one value for any given
attribute. A grid cell attribute value may represent a point measurement (for example,
elevation) or an integrated areal measurement (for example, landuse map). In a raster data
structure, points are represented as individual cells, while lines and areas are represented
as clusters of adjacent pixels. The coordinated precision of raster data is constrained by
cell size. Generally, the raster structure is ideal for representing continuous data, such as
elevation and is excellent for multiple map overlays, but it is poor for certain data
approximation (Knaap, 1992).
3) Finally, the Quadtree format is a data structure for thematic information in a
raster database that seeks to minimise data storage. In fact, this kind of data structure is a
hierarchical grid based data structure which is used to improve the storage efficiency of
either its raster or vector counterparts (Ibbs and Stevens, 1989). A hierarchical spatial data
structure is one which is developed through a process of regularly subdividing the space
A Review on GIS Systems
occupied by geographical entities on a map layer into regular spatial units (Intera Tydac,
1993). This process continues until each unit produced by the subdivision is occupied by
spatial entities with similar attributes (see Figure 3.1).
Each data structure has its merits and its pitfalls (Ibbs and Stevens, 1989). Generally,
vector data structure are used for digitising data and cartographic purposes which use (x,
y) coordinates to describe point, line and area features. In this format, data structure
retains information about the consecutiveness and adjacency of features, but are
computationally more demanding. The raster data structure is, however, useful when
combining satellite imagery, which is already in raster format, into the database and this is
used for analysis (Johnston, 1987). A raster data structure is formed by a matrix of regular
cells, each a specified size and area (Knaap, 1992). M a n y GIS have the capacity to use
both data structures. The quadtree structure is ideal for representing both continuous data
and discrete polygonal data. In other words, it can be thought of as a raster structure with
the ability to have a variable sized grid cell (Webster, 1992).
Figure 5.1 Schematically represents different data structures used in a GIS: (a) raster and quadtree (b) points and lines (vector) and (c) polygons.
5.4.2 Functionality of Data in GIS
In GIS, realistic spatial models of the world, called entities, can be developed using these
structures. Entities are points, lines, areas, surfaces and networks (Martin, 1982). A n
entity has a spatial dimension which identifies its geographical location. GIS data
structures are able to accept both spatial and non-spatial data in any GIS project.
Therefore, identification and collection of relevant structure and data are essential
(Webster, 1990). Data used in GIS often come from many different sources, are of many
types, and are stored in different ways. These mechanisms should be summarised into 6
stages as follows:
CHAPTER FIVE A Review on GIS Systems Ml
1) Geographic data sources which can be imported into the GIS environment
include: paper maps; aerial photographs; satellite images, and digital data from other areas
which can be combined to create new complex maps or tables (O'Neill et al. 1992). This is
another source of geographic information which is not often thought of as being
geographic. These data are mainly tabular databases or files of records such as weather
station observations (rainfall records) or water samples records.(databases) which are
often geographically referenced. If the underlying structure of the geographic reference
system is known (latitude and longitude), it is possible to transform and integrate this
information into thematic data which can then be processed in the GIS environment
(Bonham-Carter, 1994).
2) After the data are collected and integrated, the GIS must provide facilities which
can contain and maintain the data (Brown and Norris, 1988). Effective data management
has many definitions but should at least, include all of the following aspects: data security,
integrity and maintenance abilities. In fact, data management refers to the ability of a GIS
to manage functions efficiently, the ability to link to other data types and transfer data in
compatible formats (Davis, 1991).
3) Data processing operations are those performed on the data to produce
information. In GIS, data on its o w n may be impossible to interpret and data processing is
not an end in itself. It should turn data into a form that is informative, that helps the user
decide what to do next and whether more data processing or qualitative analysis should be
done. Data processing produces images, reports and maps.
4) Data integration and conversion is only part of the input phase of GIS. What is
required next is the ability to interpret and analyse, quantitatively and qualitatively, the
information that has been collected. This ability to analyse and manipulate spatial data that
has led to the use of GIS for both statistical and deterministic modelling (Cressie, 1991).
Analysis is carried out on data organised as maps, and also on data organised as tables.
Using the analysis function of GIS, it is possible to explore existing relationships between
the data sets.
5) The ability to model geo-referenced information is critical in a GIS (Webster,
1992). In the geoscience fields, especially geographical exploration, this type of overlay
modelling has been done for years, typically with several maps and a light table. The main
objective is to create a new map which highlights areas which meet a certain set of criteria
favourable for modelling. The GIS allows geographers to combine maps to produce new
maps, without struggling with the variable scale and projection problems (Rasuly, 1991).
CHAPTER FIVE A Review on GIS Systems LM
6) Finally, one of the most exciting aspects of GIS technology is the variety of
different ways in which information can be presented, once it has been processed by the
GIS. Traditional methods of tabulating and graphing data can be supplemented by maps
and three dimensional images. Also, tables and figures, having results, can be transformed
into maps which reveal spatial or non-spatial entities. The use of GIS technology allows
information to be viewed on the computer screen, plotted, as paper maps, captured as a
image or slide and used to generate a computer file. Generally, visual communication
which is the most important aspect of GIS technology, can be enhanced by the diverse
range of output options (Webster, 1990).
5.5 Implications of GIS Techniques in Climatology
The GIS has been widely used in recent years for natural resource planing and
management (Alberti, 1991 and Davis, 1991) and solving complex problems associated
with multiple-use of land resources (Martin, 1985). Initially, the origins of GIS lie in
environmental management (DoE, 1987), but uses of GIS have expanded to incorporate
private and government planning in areas such as: property and land parcel data; transport,
and distribution networks; civil engineering; defence; industrial site selection; and water
supply application (Tomlinson, 1987; Johnston et al., 1988).
In addition, GISs are used in many environmental spatial analysis and modelling situations.
Technical and applications-oriented workers from many fields (for example, ecology,
hydrology and geography) are interested in the use of GIS (Ferrier and Smith, 1990).
Recent environment applications can be expanded to include: survey design, dynamics and
distribution of soil (Moore et al. 1981), individual species and soil-climate modelling (Duff
and Eamus, 1992), vegetation communities (Head et al., 1992), bushfire patterns (O'Neill
et al., 1993) and habitat modelling (Marthick, 1995). All these studies found that the GISs
can be used in the handling of environmental problems. But these are only a few
applications within the general GIS literature in climatology which is both highly disparate
and complex.
Currently, there is a fast growing interest in using GIS methodology within physical
geography (Rasuly, 1993) and environmental science, which can be characterised as
"Physical-Environmental GIS" (Riddle, 1991). For example, Maguire (1989 p:222) said
that:
"The synthesis of geographical facts relating to the locational properties of spatial entities and their associated attributes is a necessary counterbalance to analytical studies carried out in physical and human geography'.
CHAPTER FIVE A Review on GIS Systems Ul
From 1991 there were rapid increases in further developments in the use of GIS for
research in physical geography and the environmental sciences (Raper, 1993). This interest
is growing fast, because a GIS can store both cartographic data, showing topography or
individual themes, such as soils or rainfall distribution, and attribute data associated with
the spatial entities (points, lines and polygons), that were represented in Figure 3.1.
Therefore, in many respects a set of disparate data can be only linked by GIS techniques.
The methodological problems and applications of this new sub-field have resulted in a
number of publications. For example, an application research was introduced to estimate
crop yield in south western Ethiopia (Simmons, 1986). Using GIS it was possible to
perform a series of map overlays of climatic and soils factors from which predictions of
crop yields were calculated. In this study three input maps (climate zones, elevation and
soil types maps) were used to produce different classes of climatic suitability.
Although, GIS has been used for a variety of projects, many with environmental themes,
there are examples of GIS techniques being used in climatic studies. Recently, its use in
the atmospheric and climatic fields have been concentrated on the modelling of spatial
impacts of climatic events or conditions. For example, Michener (1991) assessed the
ecological disturbances due to hurricane H u g o in 1989 by integrating a large quantity of
data with different sources.
The topic of GIS and climate is very new, but, because of the ideal application of the GIS
technology to environmental subjects, there is already a strong tendency to use the GIS for
climatic purposes. In many circumstances, new technology allows the rapid mapping of
point or polygon climatic variables, the correlation of maps, and the use of maps as
variables in computer models.
In the literature, there are some examples of the use of GIS which can demonstrate its
suitability to Climatology. For example, Johnson and Worobec (1988) used GIS
techniques in the study of spatial analysis of insects in relation to weather conditions. In
this study, the abundance of adult grasshoppers was correlated to monthly rainfall,
monthly hours of sunlight and annual grasshopper counts. The grasshopper distribution
was estimated from the previous year's grasshopper population in a close association with
climatic variables which were successfully constructed using GIS techniques. In another
attempt, in Italy, a GIS application for climatological analysis and productivity estimation
was applied (Ciaramaglia et al., 1992). This paper described the research that was aimed at
developing climatologically based rainfall-landscape planning models, using the GIS
technology.
CHAPTER FIVE A Review on GIS Systems 112
Also, GIS techniques were used in some studies of climate change. For example, in 1990,
Aspinall and Miller described a modelling procedure which mapped climate change
scenarios on a national and regional scale. The procedure was applied through a raster-
based GIS system which allowed integration of land cover data from remotely-sensed
sources with scenarios of climate change for impact assessment. Using climatic data such
as an accumulated growing temperature and the length of the growing season, a variety of
agricultural land-suitability assessments were derived for both current conditions and for a
future scenario of climatic change. Therefore, attempts to assess the possible impact of
climate change on agriculture and natural ecosystems are increasingly drawing upon GIS
in order to gain the regional and national picture required for policy-relevant results
(Brignall et al., 1991).
GIS, as a tool, was similarly used to bring together different elements of the climate of a
region and its physiographic attributes. For instance, Strobl (1992) modelled the spatial
distribution of climatic elements in high-relief terrain using GIS techniques. Various
topographic, atmospheric and surface data are combined in the S P A N S GIS environment
to asses the climate variations in the Alpine Regions. In Australia, a GIS system was also
applied for visualisation and demonstration of some environmental factors such as sea-
surface temperatures and rainfall distribution to estimate drought scenarios (Beswick et
al., 1993). It was generally suggested that ultimately, spatial modelling of climate elements
should necessarily replace the use of old hand-drawn maps (they have been good - but
their contents are non-reproducible) which give limited results.
5.6 Application of the GIS in Resolving Problems in Rainfall Analysis
Clearly, there is no apparent relationship between the thunderstorm rainfall - the main
topic of this thesis - and the GIS techniques discussed here. However, various aspects of
the GIS technology can be orientated towards to solving some problems in the study of
the rainfall distribution. There are some distinct advantages using a GIS in the study of the
spatial distribution of rainfall described below:
1) For a climatologist the understanding of the spatial distribution of a climatic
variable, say rainfall variation, over a specific area, is a very important task (Berry and
Marble, 1968; Rasuly, 1993). For elements such as rainfall maps, GIS produces good and
satisfactory information of the spatial distribution of rainfall, which is of interest not only
from a climatological viewpoint, but also for its importance in different fields such as
agriculture, hydrology, water resources, atmospheric pollution or even in flood control.
The estimation of the spatial distribution of rainfall is a complex and lengthy task. But,
when detailed information concerning the rainfall records is available, the use of GIS in
constructing the distribution maps, it is a matter of only several hours. The contouring
CHAPTER FIVE A Review on GIS Systems 113
approach is probably the most common and thus the most familiar to climatologists and
hydrologists. During the last two decades, the need for an efficient and rapid method of
contouring and computing areal estimates of rainfall from rain gauge data has been a
demanding task (Chidley and keys, 1970 and 1972). Currently, a GIS system, for example
S P A N S software, creates a Triangular Irregular Network (TIN) between the points
(rainfall stations) and interpolates a surface (rainfall map) model in a very short time
(Intera Tydac, 1993).
In the literature there are a few examples of direct and indirect use of GIS in the study of
rainfall distribution. For example, in 1990, Eklundh and Pilesjo suggested that it is possible
to create a rainfall data base explaining the variation of mean rainfall in Ethiopia, using a
GIS including a digital elevation model. Currently, at the Canadian Climate Centre
preparations are being made for the production and publication of long term monthly
climatic variables such as rainfall data. For example, Sajecki (1991) used S P A N S GIS to
produce a set of sample maps for the temperature, precipitation and sea level pressure
elements so that they may be included in climatic atlases. Finally, Bryceson and Bryant
(1993) created the continental rainfall maps for Australia by interpolating between the
sparse rainfall-recording stations. They suggested that GIS techniques can be used to
mitigate the climatic variables, for example rainfall, by a better supply of information. This
is a significant improvement in the ability to accurately interpolate point rainfall data and
allows a greater confidence in using GIS techniques to create rainfall maps from point
rainfall data for modelling purposes.
2) In all practical spatial analysis of rainfall distribution, climatologists and others
have to estimate the areal distribution of rainfall from point measurement. This can be
done with methods ranging from simple arithmetic averaging to sophisticated
computerised interpolation and extrapolation techniques (Watson, 1992). The
development and spread of personal computers, equipped with GIS software - for example
the S P A N S GIS - provides an ability to maintain and exploit the climatologists ordering of
information in ways never before attempted. In this way, different rainfall maps can be
drawn and compared with each other by computer and summaries provided by whatever
set of areal distributions seems necessary to the investigator.
3) Historically, the study of the spatial organisation and distribution has always been
an important factor to many climatologists, especially when rainfall maps can be correlated
to the main physiographic parameters, for example, the topography or landuse patterns of
a specific area. The area based statistics and a standard overlaying feature of a GIS allows
for the estimation of the areal distribution of rainfall based on physiographic parameters
(Webster, 1992). In this way, a GIS system can be employed to the data due to the
subsequence analysis for integrating, constructing and exploring relationships between the
CHAPTER FIVE A Review on GIS Systems 1L4.
different variables involved. Generally, the functionality of a GIS system allows for the
construction of the elevation, aspect, distance and landuse maps. These maps can be
compared with maps of rainfall distribution based on rain-gauge observations. Therefore,
describing and explaining all possible variables which may contribute in the distribution
and variation of such rainfall, is made possible by GIS.
4) More importantly, overlaying techniques provided by GIS can also be used to
create a set of new maps with specific aims (Bernhardsen, 1992). This should be done in
the GIS environment, according to the rule based combination of maps and some specific
overlay modules, it is possible to evaluate a set of values of maps. Berry (1993:111) states
that, 'in GIS, overlaying maps go beyond traditional procedures of "sandwiching" map
sheets on a light-table'. In a GIS, procedures for point-by-point, regionwide, and mapwide
summaries can be described. Using such overlaying techniques in the GIS environment, for
many climatological purposes, a series of further digital distributed maps of the physical
environment can therefore be correlated to the rainfall distribution models. In GIS it is
possible to specify, analysis and display several raster-base maps simultaneously. For
example, the topographic information, which is referenced as the primary requirement for
rainfall distribution information, is the essential database to be included in the GIS
structure.
5) Finally, all maps provided by GIS can very efficiently convey information about
the earth's surface with an adequate selection of the colour palette. B y the assignment of
colours to values of the variable and gradually varying the number of colours, considerably
interesting effects can be achieved in the displaying and visualising of the topographic and
rainfall maps (Max et al., 1993; Kelly, 1994).
Nowadays, GISs have enormous scientific importance and, more significantly, they are
already being used to make valuable contributions to the understanding and solution of
environmental problems. Currently, interest in GIS is expanding rapidly and it is therefore
reasonable to expect that GIS should also be carefully used to solve some of the problems
in climatology by a better modelling of information. In this way, although the climate will
never be controlled, the use of accurate data and powerful computing technology and
sophisticated software such as S P A N S GIS may allow greater access to methods of
monitoring rainfall distribution patterns spatially.
To sum up, the GIS as a representative of recent technology, can not only be used to
analyse climatic variables, but can also be adapted to examine the spatial aspects of rainfall
distribution. Therefore, some geographers (for example Maguire, 1989) think that there
could be more advantages in using GIS in the study of the climatic variables such as
CHAPTER FIVE A Review on GIS Systems 111
rainfall distribution, if they can be related to the other climatic or physiographic factors
simultaneously.
5.7 Data Sources on GIS System
Although the learning of a GIS technique seems a difficult task, in the current study only a
short time was spent establishing a database and in converting / translating existing maps
and spatially-referenced data into a S P A N S GIS system. The various types of data created
for this study included elevation data, rainfall data including the geographic locations of
rainfall stations, a basemap, a proximity map and a landuse map. These are outlined below
together with a brief description of their sources.
The Digital Elevation Model (DEM) data was obtained from the Australian
Surveying and Land Information Group (AUSLIG, 1993). These data are basically
produced for mapping and geographic information systems. D E M data were collected by
digitising all spot heights on 1:100 000 maps and selected points from 20 metre contours.
Heights together with location information (on A M G Easting and Northing) were
recorded in ASCII format. Using an excel computer program, the elevation data were first
prepared in a specific text format. Then they were imported to the S P A N S GIS
environment to establish a digital elevation model which represents a continuous property
of the topography in the region.
To model the general distribution of thunderstorm rainfall patterns in the study area,
a sets of point data from rainfall stations (see Chapter 6) were entered into the GIS
environment. Within the GIS, the rainfall data were analysed and integrated with other
physiographic data, in the modelling of thunderstorm rainfall distribution in the Sydney
region.
To evaluate rainfall maps by a number of physiographic parameters, some GIS
internal methods have been used to construct the basemap, the proximity and landuse
maps of the study area. For this purpose, a database was created for the study area and the
digitised data was converted into raster or vector format and entered into the S P A N S GIS
(see Chapter 7 for more details).
5.8 Methods Used in a SPANS GIS
This section will explain the SPANS GIS module and all methods used in which the data
have been analysed to construct GIS models. S P A N S is a microcomputer-based
geographic information system which were developed by Intera Tydac (established in
1982). S P A N S GIS is currently being used and supported world wide by professionals and
decision makers attempting to solve complex spatial problems. Therefore, the S P A N S line
CHAPTER FIVE A Review on GIS Systems 116
of software products, as a tool, was selected for the organisation, integration and analysis
of the geographic information obtained for the spatial study of thunderstorm rainfall
distribution in the Sydney region.
In this study, the power of the analytic and modelling capabilities of the SPANS GIS
allowed the researcher to work with a climatic phenomenon of spatial nature (for example,
modelling the distribution of rainfall). The S P A N S version 5.3.1 was used for many spatial
analyses in this study. Therefore, the following stages have been proposed as three more
general categories: data entry, model building and model analysing procedures.
5.8.1. Data Input
Two GIS systems have been employed to enter the data and subsequent analysis. First, an
Environmental Resource Mapping System, E - R M S (1989) was used for digitising a
basemap of the Sydney region. The basemap boundary was entered by manually digitising
from a 1:250,000 scale map of the Sydney region. The accuracy of digitising is estimated
to being within 80 m 2 of the indicated location on the map. The E - R M S system was
developed by the National Parks and Wildlife Service of N e w South Wales. Using this
system, a basemap of the Sydney region was entered in digital form, then edited and
converted into a grid cell format to be exported. After that, the data export module of E-
R M S allowed data to be exported to the S P A N S GIS. Because many S P A N S operations
require a basemap, this study first established a basemap to define the boundaries of the
study area in the S P A N S GIS system. The basemap must be a binary map, that is, it could
not contain classes other than 0 and 1.
In the second stage, a SPANS raster module was used to transform the basemap to a
raster-base format to be imported into the GIS environment. Then, a S P A N S was also
used to enter the D E M data. Generally, this system accepts any A S C I file, this can be data
related to the location of rainfall stations and their associated attributes (rainfall amounts
or other statistic values). A digital elevation data set consisting of approximately 20741
points (Australian M a p Grid coordinates), together with elevation in meters was imported
into a S P A N S GIS. Six 1:100,000 scale maps covered the study area, four of which
extend beyond it. The data approximates a 20 m grid which roughly covers an area of
9170.36 km 2. These data were imported into a GIS coverage system showing point data
and displayed on a computer screen to provide a visual impression of the distribution of
sample elevation points. Imported data were then checked for possible errors or
corrections.
CHAPTER FIVE A Review on GIS Systems 111
5.8.2 Model Building
Once the data were integrated into SPANS, various techniques were used to analyse the
data sets. A major part of the analysis involved the generation of elevation and rainfall
maps from the point data sets. Several functions of GIS, for example, a contouring
method, were used in S P A N S to convert the data to thematic maps.
Firstly, a set of elevation data (with point structure) was used to create a digital
elevation model by establishing topological relations between the elements using a
rectangular grid (or elevation matrix) with a Triangulated Irregular Network method
(TIN). TIN structures are based on triangular elements, with vertices at the sample points.
Generally, the TIN surface can be constrained to pass through the point data. In this case,
the contouring program was used to convert point data representing spatially continuous
phenomena into classified, trend surface maps such as elevation maps which were used
then for further analysis. After the TIN was created, some classification schemes, for each
specific data, were applied to produce the desired classifications. The accuracy and
reliability of this technique has been computed by Weibel and Heller (1991). They found
that the surface models can be used to create, analyse and display surface information.
Also, the SPANS contouring module which interpolates a surface map from a point
data set through a process of triangulation, was used to generate maps of thunderstorm
rainfall distribution based on rain-gauge station observations. This surface was constrained
to pass through the data points. Generally S P A N S GIS supports both linear and non-linear
implementations and it allows extrapolation outside the convex hull defined by the data
points. In this study, a linear interpolation model which computes a linear interpolation
surface, was applied for the data. During data analysis stages, a query module containing a
query capability was used to verify the final results. The query function of S P A N S GIS
was also used to perform and confirm all geographic information in relation to locations
specified on the map layers.
Secondly, some of the information related to topography such as aspect and
elevation maps were automatically produced in the S P A N S environment. The D E M
quadtree was used to create an aspect map which is measured in azimuth degrees. In
S P A N S GIS, a map of the aspect is computed from a grid elevation map. In fact, the
aspect is the orientation of the steepest slope with respect to north and is computed as an
angle clockwise from north. A slope facing north has an aspect of 0°, facing east, 90°,
facing south 180°. If it is a flat surface (no slope) it has the value 360°. The aspect map
derived was used to analyse and identify the relationship between rainfall distribution and
exposure.
CHAPTER FIVE A Review on GIS Systems JM
The next important specific aim was the production of a proximity map. Proximity
to the sea was suggested to be an important physiographic variable affecting the
distribution of thunderstorm rainfall patterns in the region. A distance map was therefore
generated by creating concentric buffers, with 10 k m distance (a arbitrary classification),
around the average coastline. In this map 10 different classes have then been expressed as
buffer zones which have been used in a proximity analysis of the thunderstorm rainfall
distribution.
A set of satellite images and also hard-copy maps of the Sydney region were used to
create the landuse map of the Sydney region which covers the whole of the study area. All
procedures, which were taken to establish the 'specific landuse' map the Sydney region,
are described in Chapter 7.
5.8.3. Model Analysing
Within a SPANS GIS there are several analytical functions which allow a user to explore
the possible relationships between the data sets and associated map layers. One such
function was, for example, used to determine the average thunderstorm rainfall amounts
for each of the topographic, proximity and landuse classes.
An area-based analysis function of a GIS was also used to analyse single map
characteristics or determine the content of each area covered by different topographic
classes. A statistical report was then produced, for example, to give the average rainfall
for each class.
In addition, using an area cross-tabulation technique it was attempted to find the extent of
the correlations between the digital elevation map, the aspect map, the proximity classes
and the landuse patterns with thunderstorm rainfall distribution. Generally, the results of
the cross tabulation can indicate the possible correlations between the two map layers.
Statistically, chi-square coefficients are used as the measure of the degree of correlation,
association or dependence of a thunderstorm rainfall map to the topographic maps. Some
examples of the GIS functions which have been used for data building or data analysing
have been given in the current chapter, other advanced GIS functions, which could be
used in the analysing of thunderstorm rainfall data, are given in detail in Chapter 7.
5.9 GIS Potential Errors
There are some advantages in using the GIS method in evaluating the spatial distribution
of rainfall from thunderstorms. One of the advantages is the ability to model and display
the results as colour maps which show the spatial pattern of rainfall variation over the
study area. The contouring approach is probably the most used and thus the most familiar
CHAPTER FIVE A Review on GIS Systems 119
to those who are interested in studying rainfall distribution in space. The most significant
advantage of using a S P A N S GIS, in this study, lies in its modelling capabilities. With
S P A N S , simple to complex models have been generated, modified, and regenerated to be
compared to the originals in a matter of hours.
However, there are two main potential errors or problems with using a SPANS GIS.
Firstly, SPANS's TIN method, as an interpolation technique, was required to approximate
the surface behaviour between sample points. The S P A N S contouring module interpolates
a surface from a point dataset through a process of triangulation which honours the data
points. In the triangulation method, the surface passes exactly through each known data
value, and interpolation is only affected by the heights at the three vertices. Thus, the size
of the zone of influnce of a point is affected by the density of the surrounding points
(rainfall stations). In small triangles (dense points) the effective zone of influnce of a single
observation is correspondingly small, whereas in large triangles (sparse points) the zone of
influnce is large. T I N method is desirable in cases where the values at the data points are
known to have relatively small errors, such as elevation data. However, where the samples
of a surface are associated with errors due to sampling and measurement, such as rainfall
observations, there are relatively large errors as compared with the overall spatial
variation. In such situation, an alternative interpolation method that produce smooth
surfaces, and do not necessarily honour the data points, should be used (Bonham-Carter,
1994).
Secondly, because the interpolation technique in SPANS GIS is based on a raster based
format, the resultant rainfall maps may not be completely smoothed to reduce noise in the
data. It is because, surface modelling of spatially continous field variables (such as rainfall
values) involves interpolation from the irregularly-spaced samples to a raster format. Each
interpolated point is simply a cell over which the variable (rainfall or topographic values) is
constant. The resultant raster map has a relatively continous values dependent upon
classes used rather than discrete values of observed in the field. It must be noted that, in
this study, the data taken from rainfall stations for interpolation purposes had discrete
nature. GIS technique was, however, successfully used (Skidmore 1989 and 1990) to
interpolate the rainfall data, digital terrain data, and to identify terrain position and to
calculate aspect values from a girded digital elevation model.
5.10 Summary and Conclusion
The SPANS GIS can helpe to visualise, organise, combine, analyse, model and question
the real data from Sydney's climatic environment which has been spatially organised in a
computer environment. In other words, the power of a GIS is in its ability to integrate,
manipulate, and process data from different sources. Data with spatial and non-spatial
CHAPTER FIVE A Review on GIS Systems 120.
nature could be handle to provide information and models which aid in the understanding
of the Sydney's geographic location and its attributes. In Chapter 6, the GIS interpolation
techniques could be used for mapping of thunderstorm rainfall variations throughout the
Sydney region.
The combination and display of map layers in pairs is also an important aspect of GIS,
because it allows the examination of spatial relationships between spatial phenomena such
as a rainfall m a p and a topographic map, for example. Although the ultimate goal of most
GIS studies involves multiple data layers, the relationship between map pairs is often an
exploratory first step, and may determine h o w features of one or both maps are to be
enhanced or extracted for subsequent analysis (see Chapter 7).
Multiple maps could be obtained using overlay techniques in a GIS environment. The
ultimate purpose of this study would be to combine spatial data from diverse sources
together, inorder to describe and analyse interactions, to make models, and to provide
support for decision-makers. Chapter 7 will, therefore, present some models of interst to
climatologists, to show h o w they can be implemented in a GIS environment. Multiple
maps also help to illustrate the models with reference to two applications: selection of
geographic areas which are suspect for the highest rainfall values, and their associations
with physiographic maps of the Sydney region. These techniques could be also supported
using statistical procedures. It may be concluded that although the topic of GIS and the
climate is relatvely a new notion in Australia, some functions of GIS can be applied to the
raw data sets to create new products such as rainfall maps. Other products such as
landuse, aspect and elevation maps could also be suited for climatic applications.
CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall 121
CHAPTER 6
THE SPATIAL VARIATION AND DISTRIBUTION
OF THUNDERSTORM RAINFALL
6.1 Introduction
Information on the distribution of thunderstorm rainfall in space is very important in a
variety of applications. In the Sydney region, the knowledge of the spatial variation and
distribution of thunderstorm rainfall is limited to a few case studies (Bahr et al., 1973;
Morgan, 1979a; Nanson and Hean, 1985). Although various aspects of the thunderstorm
activity of the Sydney region have been examined over the years, two aspects that have not
been thoroughly examined are: the long term variability of thunderstorm rainfall of the
region over a long period of time, and the relationships between the local physiographic
parameters and the distribution of thunderstorm rainfall patterns for the whole of the
Sydney region. The former is examined in the current chapter. The latter is the subject of
the next chapter.
The purpose of this chapter is to analyse the spatial variation and distribution of
thunderstorm rainfall in the study area. First, in sections 2 and 3 the data and methods used
are described respectively. Section 4 examines the methodology developed for the
generation of reliable data on daily thunderstorm rainfall events, based on specific criteria.
Then, in section 5, the spatial variation of thunderstorm rainfall in the Sydney region, using
gamma distributions, is analysed. The g a m m a distribution is used to find the probability
distribution of thunderstorm rainfall amounts at each rainfall station. Finally, in section 6,
the spatial distribution of thunderstorm rainfall patterns are constructed using a GIS
technique. Thunderstorm rainfall maps are based on average seasonal values and the
biggest thunderstorm rainfall events for each month used in the study.
6.2 Data Selection
Unlike most previous studies that examined single but major thunderstorm rainfall events
over a short period in the region, (for example, Williams, 1984; Colquhoun and Shepherd,
1985), the present study views the distribution of thunderstorm rainfall at two long time-
scales namely Spring (October, November, December) and Summer (January, February,
CHAPTER SLY Spatial Variation and Distribution of Thunderstorm Rainfall 122
March) and over 34 time period. The study also considered the largest thunderstorm
rainfall events for each of the warm months (October to March). The definition of seasons
is not conventional by Bureau of Meteorology standards. There are two reasons for this
choice. Firstly, as it was shown in Chapter 4, three important climatic factors (air, sea
temperature and air humidity) are high during October to March in comparison to the
other months of the year. This seasonality affected the temporal and spatial distribution of
thunderstorms considerably (see Chapter 2). As shown in Chapter 3, the warm months
clearly dominate thunderstorm rainfall with the most thunderstorm activity, and more than
72 per cent of thunderstorm rainfall. Secondly, in the cold months of the year including
September, thunderstorm rainfall is not common enough to give a reliable indication of
thunderstorm distribution throughout the region.
The precipitation data were provided in three data sets supplied by the Bureau of
Meteorology and the Sydney Water Board. The original data set was collected on a daily
basis and was converted to monthly or seasonal records, where needed. Thunderstorm-day
records were extracted from the original 3 data sets, using three different computer
programs written for this purpose (see Appendix A, computer programs 2, 3 and 4).
A prime consideration of the present study was to determine the spatial variation and
distribution of thunderstorm rainfall over a region that extended beyond the Sydney
Metropolitan area for as long a time-span as possible. During the processing of the data
set, it was noted that for some stations there was a considerable amount of missing data.
Stations with less than 10 years of records were excluded from the analysis. Mooley and
Crutcher (1968) in a study of rainfall in India investigated the number of years of record
needed to stabilise the gamma parameters. Although Weisner (1970) indicated that from
25 to 50 observations of precipitation data are needed to give a stable frequency
distribution, Bridges and Haan (1972) estimated that with 100 observations, there is a
negligible 0.6 percent chance of error (see Section 3 for functionality of gamma
distribution). Therefore, in this study, in order to ensure stability in the statistics, stations
with fewer than 100 thunderstorm observations for the entire period were excluded. The
resulting data set consisted of 191 stations (134 from the Bureau of Meteorology and 57
from Sydney Water Board) covering the period 1960-1993.
To show that the two networks of rainfall stations were comparable the data were
subjected to statistical techniques. First, to find any possible difference between the means
of the 2 data sets, an analysis of the variance ( A N O V A ) technique was applied (Webster
and Oliver, 1990). A n F value of 2.36 was calculated showing that there is not a significant
difference between the means of the two sets at the 0.05 level of significance (see Table
6.1).
CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall 123
Table 6.1 Difference between two sets of stations (the Sydney Water and the Bureau of Meteorology) according to their rainfall means. _ _
Number of Mean in F-test Groups Stations m m Std. Dev. (SWs vs. BMs)
Sydney Water stations (SWs) 57 14.57 1.769
Bureau of Meteorology stations 134 14.99 1.761 F = 2.362*
(BMs) T = 1.537
Total 191
* non-significant at 0.05 level
To confirm this result, the NNA technique (see Chapter 3) was applied to the data. This
technique takes into account the range and structure of the data at each rainfall station.
All rainfall stations from the Sydney Water Board and the Bureau of Meteorology
clustered based on 8 optimum clusters. The results indicate that the Sydney Water Board
rainfall stations (57 stations) were randomly interspersed with the Bureau of Meteorology
stations (134 stations) at the 05 level of significance.
Finally, to test the results of the NNA technique,the nearest Bureau of Meteorology station
was found to each of the 57 Sydney Water Board stations (see Table 6.2, Appendix B )
using the S P A N S GIS Spatial Query function (see Chapter 5). For each pair of those
stations, the commonality statistical association in thunderstorm rainfall was then evaluated
using the correlation coefficient (Hutchinson, 1970; Davis, 1973). Only rainfall grater least
0.5 m m at each pair of stations for Spring and Summer thunderstorms from 1960 to 1993
was used in this analysis. These correlations were then plotted against distance and a linear
regression performed on the results (Figure 6.1). This model describes the relation between
the correlation coefficient and the interstation distance of rainfall stations (Stol, 1972).
Figure 6.1 indicates that the correlation coefficient between the pairs of stations decreases
with increasing distance (n = 0.957) in a linear fashion. The best corresponds occurs when
stations from the two data sets lie within 5 K m of each other. Within that distance the
correlation between paired stations approximates 0.9.
CHAPTER SLY Spatial Variation and Distribution of Thunderstorm Rainfall 124
Regression Fit Y= 0.956853-1.64E-02X
R-Squared = 0.957 95.0% Confidence Bands 95.0% Prediction Bands
v '-••"fv-™"mwr "• r'"'"1 i r n T ™ m'T 0 5 10 15 20 25 30 35
Distance Figure 6.1 Relation between correlation coefficient (r) and interstation-distance (in K m ) of pairs of thunderstorm rainfalls in the region.
The results of the ANOVA, the NNA and the correlation coefficient techniques make it
clear that there is no significant deifference, at the 0.05 level of significant, between the
two networks of stations, in their recording of thunderstorm rainfall in the region.
Accordingly a dense network (combining the Sydney Water Board and the Buerau of
Meteorology stations) ensures that a reliable spatial distribution of thunderstorm rainfall
over the study area could be constructed. The combined rainfall records covers an area of
9172.21 square kilometres.
It was impossible to separate the precipitation amounts for thunderstorms from the daily
rainfall total received on any thunderstorm-day. As the works of Sharon and Kutiel (1986)
suggest, most rainfall from an individual event containing a thunderstorm is likely to come
from the convection associated with that thunder. Hence in this study, for each individual
thunderstorm-day event which matched the criteria, it was assumed that all the rainfall in
that thunderstorm-day was the result of thunder activity (the validity of this assumption
will be discussed latter in Section 4). Three hundred and forty seven relatively intense and
widespread events between October and March occurred in the Sydney region for the
period 1960-1993.
The list of the rainfall stations used, together with their geographical coordinates and their
elevations above mean sea level, are given in Table 6.2 (see Appendix B ) . The spatial
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CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall 126
distribution of these stations is shown in Figure 6.2. Generally, the distribution of stations
reflects the distribution of major population consentrations, suburbs, dams, post offices and
rail stations. The possibility of spatial bias exists with this type of sampling network, but no
better one was available.
6.3 Techniques Used
T w o sets of techniques were used to analyse the variation and spatial distribution of
thunderstorm rainfall in the Sydney region. Firstly, in order to allow direct inter-station
comparisons and to find the probability distribution of thunderstorm rainfall amounts at
each station, the gamma distribution was used. Thom (1958) introduced the gamma
distribution with two estimators - which are in fact a minimal number of summarising
measures. The mathematical functionality of the gamma distribution has been widely
discussed by, for example, T h o m (1958, 1968) and Shenton and B o w m a n (1970).
In fact, the probability density function is one of the statistical characteristic
measurements of the spatial distribution of thunderstorm rainfall that should be
determined. The gamma distribution with two parameters is therefore the most flexible
class of probability density functions and has extensive applications in the analyse of
rainfall data (Bridgman,1984) and thunderstorm rainfall modelling (Easterling and
Robinson 1988).
This method was first introduced by Thom (1958) as a frequency analysis:
*, ^ l r-i ~XIP P>0 m f(x) = -z^—-xrle ; ' 0)
P r(y) 7 > 0 wherex is thunderstorm rainfall amount, beta (fi) is the shape parameter of the
distribution, g a m m a or alpha (y ) is the slope parameter and r is the gamma function of
? • These parameters were estimated for each station by the maximum likelihood method
(Thom, 1958). In this method the best estimate of gamma is given by
A = Inx--Unxi (2)
n
1 + JTT473A 1 AA
_! + [! + Ajlnx -1 / rillnXi) 13]1/2
0r 7 " Wnz-UnHnXt) (4)
CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall 127
where x is the mean thunderstorm rainfall amount and n is the number of thunderstorm
rainfall occurrences on a daily basis in the data set. The best estimate of beta is then
P=X/Y (5)
Some curves from the family of gamma distributions, and probability density functions,
are graphically illustrated in Figure 6.3 for different values of a and p. a is the shape
parameter which determines the peakedness of the curve. Figure 6.3 (a) shows that the
probability density function can take the shape of an exponentially decaying curve (a = 1)
or the shape of a normal distribution (a = 4). A value below 1 indicates an exponential
decrease in the probability density function from a maximum of infinity at zero x. W h e n a
equals one, the exponentially decreasing curve has a zero x intercept at 1/p. A value
greater than unity produces a curve with zero probability density function at zero x, a
rapid increase to the maximum probability density function and a slow decay thereafter.
As a increases, the peakedness and skewness of the curve decreases, and the curve
approaches that of the normal Gaussian distribution. This implies that as a increases the
range of x commonly occurring also increases. The skewness of the probability density
function increases and the peakedness decreases, as P increases.
0.8
^ 0.4
0.2 •
0
(a)
\ cc = l
/ \ A ~ N ? = 4
5
- ^ ^ a = 8
10
X
15 2 0
0.2 •
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/Ty=2
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(b)
R = 4
4 6 8 1
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Figure 6.3 The gamma density function for (a) four a values, P = 1 and (b) three P values, a = 4.
To calculate the alpha and beta values, a computer program was thus written using
M A T L A B , the MATrix LABoratory programming language (1994). Both alpha and beta
values were extracted for 191 rainfall stations in the Sydney region. Then alpha and beta
values were contoured over the study area using the SURFER computer program, version
5.01 (1994). This is an objective mapping technique which interpolates to a grid by fitting
CHAPTER SLY Spatial Variation and Distribution of Thunderstorm Rainfall 128
a polynomial to a set number of control points surrounding each grid point. Results were
subdivided by season - spring (October to December) and summer (January to March),
representing the two main seasonal thunderstorm rainfall regimes in the region for the
period 1960-93. There are some distinct advantages for using g a m m a function. For
example, it is one of the best probability models which could be fitted to data with a
skewed nature, as it summarises thunderstorm rainfalls data by a minimal number of
measures. Also, using gamma function, it is possible to group all stations by similar alpha
or beta values, with direct comparisons between stations.
In the second stage, the thunderstorm rainfall data were mapped using the SPANS GIS
interpolation module. This is an objective mapping technique which interpolates data with
a spatial nature using a "Triangulated Irregular Network" (TIN) interpolation method
(Intera Tydac, 1993). It is less likely that areas with sparse distribution of rain-gauges can
lead to some overestimated problems using this interpolation technique. For obtaining the
general distribution of thunderstorm rainfall models (and evaluating rainfall by a number of
physiographic parameters in the following chapter) a standard GIS linear interpolation
method was used. In this way, an average depth for two seasons, mainly Spring (October
to December) and Summer (January to March) from 1960 to 1993, were determined. Also,
for each month of the warm season (October to March) the biggest thunderstorm rainfall
events from the record were analysed. These maps are based on data from 191 rainfall
stations (see chapters 5 and 7 for more details about GIS techniques).
6.4 Thunderstorm Rainfall Selection Criteria
Thunderstorms occur randomly in time and space (Duckstein et al., 1973; Fogel and Hyun,
1990). The amount of rainfall shown in chapters 3 and 4, is strongly skewed over time and
space, low rainfall occurs often during thunderstorms while heavy and intense rainfalls are
rare. Most previous work considered thunderstorm rainfall to be convective no matter
what the rate was, or the season of occurrence. For example, the U.S. Weather Bureau
(1947) examined rainfall totals for days with thunder and daily totals for the summer
months in the U S A . In another work Sharon and Kutiel (1986) isolated heavy convective
rainfall by assuming a rainfall rate of 20 mm/hr or more. Both of these studies assumed the
majority of rainfall to be convective in nature by the season of occurrence.
In order to study only significant thunderstorm rainfall events across the whole Sydney
region, the data were constrained using a number of analytic stages and associated criteria
as follows:
In the first step, by using the NNA method, the associations between 15 thunder-recording
stations, in the Sydney region were found for thunder-days in which thunderstorms have
been observed (see Chapter 3 for more details). The results have indicated that in the
CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall 129
Sydney region, there is a definite statistical relationship between many of the stations when
there is a thunderstorm-day. These associations are strong among some stations.
According to these associations, all 15 stations were then clustered into seven groups.
Each similar group may represent an area for the development or occurrence of
thunderstorm activity, in the region. In fact each cluster can be considered to be a
representative of an area for thunderstorm activity based on general geographic
characteristics (for example distance or locality). Associations obtained may indicate a
relative interdependence among different clusters statistically, and as a result, suggest a
c o m m o n thunderstorm activity for the whole of the Sydney region.
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1972-93 (139 Events) %L*a»m*n
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% Excaadanea
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Richmond
1960-93 (650 Events) % Lasa than
. , Population 1
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% Eicaaoinca "°~~ —
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1960-93 (556 Events) % La«a thu
. Pop ula Hon 1
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Thunderstorm Rainfall In m m
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Thunder-recording
Stations
Richmond Katoomba Bowral Sydney R. 0. Bankstown Camden Airport Wollongong
Average Value
Extracted rainfall
Values
11.0 10.0 9.0 17.0 7.5 7.0 16.0
11.0
Figure 6.4 Probability of exceedence diagrams for 7 selected thunder-recording stations located in the Sydney region.
In the second step, daily thunderstorm rainfall amounts were studied in order to determine
a standard value to be known as, a thunderstorm rainfall event, in the region. For this
purpose the 'probability of exceedence' technique, modified by Bryant (1991) for the
CHAPTER SLY Spatial Variation and Distribution of Thunderstorm Rainfall 130
Macintosh computer, was used. Probability of exceedence diagrams could be constructed
by the following equation:
Exceedence probability = M(N+1)_1 100 %
where N = the number of ranks
M = the rank of the individual event (highest = 1 )
This technique was applied graphically using the amount of rainfall, recorded during
thunderstorm events, on a special semi-logarithmic paper for each station. Note that the
amount of rainfall values (in m m ) from thunderstorms was plotted along the x- axis, while
the probability of exceedence (in per cent) is plotted along the y-axis.
It is generally assumed that straight line segments plotted on this paper form normal
populations. The intersection of the two straight lines forms the boundary between two
normal sub-populations (see Figure 6.4). All thunderstorm rainfalls plotted in this fashion
show two distinct populations on straight line segments. A n advantage of this technique is
that the probability of both low and high rainfall values from thunderstorms can be
determined. The average value of these line intersections using data from 7 selected
thunder-recording stations was 11 m m (see Table 6.3). Also, the average rainfall for all
3070 thunderstorm events (taken from all thunder-recording stations) was 10.4 m m for the
region. This analysis indicates that there appears to be 2 types of rainfall associated with
thunderstorms in the Sydney region. In the first type, small thunderstorms with less than 11
m m of rainfall are very common, representing 95 per cent of all thunderstorms. In the
second type, rainfall exceeding 11 m m was rare but copious. This latter event represented
5 per cent of all thunderstorms, accompanied with extreme rainfalls.
Table 6.3 Thunderstorm rainfall values extracted from the intersection of two populations using probability exceedence graphs, for the Sydney region.
Row
1 2 3 4 5 6 7 Average
Thunder-recording
Stations
Katoomba
Bowral
Richmond
Camden Airport Bankstown
Sydney RO.
Wollongong
Year
From- To
1987-93
1975-92
1960-93
1972-93
1969-92
1960-93
1972-93
Extracted rainfall
values
10 9 11 7 7.5 17 16 11.0
For this work, a thunderstorm-day event was, therefore, defined as the occurrence of any
storm with at least 11 m m of rain or more in at least one of these 7 stations. Accordingly,
CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall 13J.
three criteria had to be met before a thunderstorm's rainfall was considered a significant
event.
Criteria 1. Three thunder-recording stations had to record a thunderstorm on the same day
- at least 2 of the clusters of stations, defined in chapter 3, had to be represented.
Criteria 2: At least one of these stations had to be a main station: Richmond and Sydney
Regional Office. The Sydney Airport station could be also the main station, because it does
appear to associate well with the Sydney Regional Office station.
Criteria 3: At least one of the main stations had to have 11 or more millimetres of daily
rainfall on the day of a thunderstorm.
In this way a thunderstorm rainfall event could be characterised by above-mentioned
criteria. Thus, all rainfall data recorded by any rainfall station - on a thunderstorm day
which met this study's criteria - were considered to come from a or more convection
systems which introduced thunderstorm activity for whole of the Sydney region.
Accordingly, three hundred and forty seven common thunderstorm-days were selected for
the period 1960 to 1993 to be used in a spatial analysis.
6.5 Spatial Variability of Thunderstorm Rainfall
The spatial variation of thunderstorm rainfall in the Sydney region is illustrated best by
values of the g a m m a distribution. The g a m m a distribution has been used in several research
works associated with rainfall and thunderstorms (for example, Simpson, 1972; Robinson
and Easterling, 1988). A similar approach, using the two-parameter g a m m a distribution
(the empirical distribution of Thorn's maximum likelihood estimators), was adopted for use
in this study. Estimates for alpha and beta were estimated using only those observations
where measurable rainfall occurred. Hence, the two parameters, alpha and beta, describe
the probability distribution of rainfall amounts only from thunderstorm observations giving
measurable rainfall which match with this study's criteria.
In spring, the spatial distribution of the alpha parameter is shown in Figure 6.5. The lowest
alpha values (which indicate a high probability of rainfall amounts from thunderstorms),
less than 0.8, can occur in the eastern parts of the Sydney region, over the Metropolitan
area and to the north of the City. Alpha values between 0.7 and 0.9 can be also seen over
the two major topographic features of the study area, mainly over the Blue Mountains and
the Illawarra Plateau.
In the central part of the region and to the south-west (south of Bowral), alpha values
increase to greater than 1.1. The geographical distribution of the values for alpha, shown
CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall 132
by Figure 6.5. The lowest values occur south-east of the region, where alphas with less
than 0.70 can be seen. Areas which are located in the southwest of the Richmond and
Camden station appear to have the highest alpha values (alpha > 1.2).
Longitude On decimal)
Figure 6.5 Geographical distribution of alpha value, Spring (Oct to Dec), over the Sydney region. H designates areas of alpha > 1.1, in contrast L represents areas of alpha < 0.8.
The geographical distribution of beta for spring (October to December) is shown in Figure
6.6. Beta values greater than 32 - which indicate a high probability of rainfall amounts
from thunderstorms - can be seen for the topography of the south-east of the study area,
just over the Illawarra Plateau. In comparison with the lowlands of the Sydney region.
Over the Blue Mountains, beta values are also relatively high, more than 22 for Katoomba.
Again, beta values more than 23 can also be seen over the eastern part of the City. The
central part of the Sydney region, for example in the south-west of Richmond, and areas
located in the south-western corner of the study area (at Bowral), show very low beta
values, less than 12 on average.
The summer situation is considerably different, because the alpha values, to some degree,
increase over the Illawarra Plateau, and an unclear pattern dominates the non-coastal
areas. The highest values for alpha occur in the middle portion of the Sydney region, for
example near Campbelltown (alpha > 1.2), and in the south of Camden Airport, where
alpha values exceed 1.4 (see Figure 6.7).
CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall 133
In contrast, along the coast, beta values show an extension of coastal influence, particularly
over the Illawarra Escarpment, over the City and north of Sydney just over the Homsby
Plateau (beta > 27). These values generally decrease moving towards the west and south
west, over the central parts of the study area. The values for beta are generally quite low in
this area, being less than 10 near and south-west of the Camden Airport (Figure 6.8).
Figure 6.6 Geographical distribution of beta value, Spring (Oct to Dec), over the Sydney region. H designates areas of beta > 20, in contrast L represents areas of beta < 11.
Generally, the distribution patterns indicate four main thunderstorm rainfall areas in the
Sydney region, better shown by the alpha and beta values. Over the eastern part of the
study area extremely high beta values can be seen (a high beta value represents a high
probability of rainfall amounts from thunderstorms) indicating that, the coastal location is
very important in the distribution of thunderstorm rainfall amounts.
In contrast with the centre of the study area, which shows a greater amount of spatial
stability for low beta values, the Metropolitan area and the nearby northern suburbs show
sharply increasing or decreasing beta values. For example, over the City there are high beta
(or low alpha) values indicating the significant centres for the occurrence of thunderstorm
rainfalls. Also, the areas most likely to produce high rainfall from a thunderstorm event
during the w a r m months include the northern suburbs of the City. Therefore, considering
CHAPTER SLY Spatial Variation and Distribution of Thunderstorm Rainfall 134
both the alpha and beta values, the chance of high rainfall from thunderstorms over an area
located in the centre of the Sydney region (for example, Prospect D a m ) is much less than
thunderstorm rainfalls which could occur over the Metropolitan area or its north-western
suburbs.
Lougltudo (In decimal)
Figure 6.7 Geographical distribution of alpha value, Summer (Jan to Mar), over the Sydney region. H designates areas of alpha > 1.1, in contrast L represents areas of alpha < 0.8.
Over the Blue Mountains, in the north-west of the study area, the probability of the rainfall
amount from thunderstorms is high. O n average, over the Illawarra Plateau, located in the
south-east of the region, the probability of the rainfall amount from thunderstorms is
approximately twice the amount recorded over an area in the central low lands of the
Sydney region. These areas tend to experience the lightest rainfall from thunderstorms.
The rest of the region including the central part of Sydney and the foothills and higher
areas to the south-west comprise the fourth area, on average a low probability in
thunderstorm rainfall. The lowest beta (or the highest alpha) values occur through a large
portion of the study area, in the centre of the Sydney region with a north-east to south
west direction.
CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall 135
Longitude (in decimal)
Figure 6.8 Geographical distribution of beta value, Summer (Jan to Mar), over the Sydney region. H designates areas of beta > 20, in contrast L represents areas of beta < 10.
The probability distributions of thunderstorm rainfall amounts at each station were
summarised by use of the g a m m a distribution for total events for the spring and summer
seasons. These probabilities have established different spatial patterns throughout the study
area. In other words, the curve-fitting process and calculation of alpha and beta values,
allows us to map and describe the probability distributions, but does not reveal h o w much
rain may fall from an individual thunderstorm. To test the reliability of this technique in
modelling thunderstorm rainfall distribution, there is a need to compare results with
surface rainfall maps. Thus, in the following section the 'average thunderstorm rainfall
value' is the object of primary interest, not only for comparison purposes with probability
patterns, but also for mapping its variations in rainfall over the Sydney region. The basic
question, from a climatic view point, is' what is the spatial distribution of average
thunderstorm rainfall patterns, taking into account the associated data which were used for
g a m m a distributions?
6.6 Spatial Distribution of Thunderstorm Rainfall
The study of the spatial organisation and distribution of thunderstorm rainfall has always
been an important factor to many climatologists and meteorologists (Hobbs, 1972; Sharon,
1983; Bryant, 1991 and Batt, et al., 1995). This section generalises the point observations
CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall 136
(rainfall data) into a spatial maps of thunderstorm rainfall (mean area rainfall values) over
the Sydney region. Thunderstorm rainfall maps were constructed using a standard GIS
interpolation method. Constructed GIS maps successfully show the apparent detail of
rainfall variation over the study area described by alpha and beta values. These should
provide a logical and effective approach to the derivation of an area mean. The mean is
obtained by applying an area weighting (TIN) interpolation method to the mean rainfall
between each pair of consecutive isohyets. Spatial distributions of two seasonal averages
and the biggest daily events for each thundery month, from 1960 to 1993, are described in
separate sub-sections.
6.6.1 Average Event Values
The distribution maps obtained from the available data are most effective in showing the
general trends in the distribution of thunderstorm rainfall, over the study area. The average
thunderstorm rainfall map for spring is shown in Figure 6.9. The wettest part of the region
is found in the south-east of the Sydney region at the top of and over the Illawarra Plateau
where the rainfall per event averages between 20 and 23 m m . Just to the west of this
region, in the Burragorang Valley, averages are less than 10 millimetres. Areas with
thunderstorm rainfall in excess of 20 m m are found near and over the City. Generally, the
coastal areas show high thunderstorm rainfall at this time of the year, and the high elevated
areas, such as a section of the Blue Mountains, have more rainfall from thunderstorms.
Figure 6.10 displays the average thunderstorm rainfall per event for the summer season.
During the period, the Blue Mountains (Katoomba) and the Wollongong Escarpment show
distinct locations for thunderstorm rainfall distribution. In the east, over the Sydney
Metropolitan area, there is a high average rainfall (more than 22 m m per event). Another
location for rainfall maxima is the Hornsby Plateau which is located to the north of the
Sydney. The driest areas are located in the low-lying central plain, with a north -south path
in the region between the mountains in the west and the coastal areas. The south-west of
the region shows an average of less than 10 m m rainfall from thunderstorms.
On average, figures 6.9 and 6.10 indicate that the maximum rainfall occurred over the
eastern parts of the Sydney region, and more precisely on the north-west part of the City
near Turramurra, over the Illawarra Plateau and Escarpment, and over a small section of
the Blue Mountains. The minimum rainfall occurred in the south-west of the Sydney region
and over inland areas. Of particular interest, are the low rainfall amounts over the Southern
Tablelands (for example, at Bowral located in the south-west corner of the study area) and
near the Campbelltown basin (just the west of Lucas Heights).
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CHAPTER SLY Spatial Variation and Distribution of Thunderstorm Rainfall 139
Although thunderstorm rainfall distribution is especially characterised by an extremely
irregular area pattern, a marked gradient in rainfall from east to west can be observed. At
the same time, these isohyet maps feature a number of cells or centres produced by
thunderstorms over the City, northern suburbs and elevated areas. Generally, it can be seen
that these rainfall distribution maps agree with g a m m a distributions patterns. However,
some researchers, such as Chuan and Lockwood (1974) and Sumner (1988), have already
suggested that individual thunderstorm events analysis may offer the soundest basis for
determining the spatial distribution of thunderstorm rainfall. Therefore, in the second
stage, the individual biggest events for the warm months, October to March respectively,
have also been considered. In this way it was expected that the more intense thunderstorm
rainfall events may be distinguished from the more uniform average thunderstorm rainfall
patterns.
6.6.2 The Biggest Events
The impact of thunderstorm rainfall, from the point of view of flooding processes taking
place in the Sydney region from time to time, is very important (Colquhoun and Shepherd,
1985). Therefore, the purpose of this section is to model the amount of rainfall from
individual thunderstorms likely to be recorded on thunder-days with flash flooding in the
region. Rainfall events, in this study, were defined by the daily occurrence of measurable
precipitation for periods (days) with available data. This definition provided some heavy
thunderstorm rainfall event samples that displayed relatively widespread characteristics.
Table 6.4 General descriptions for the 6 biggest thunderstorm rainfall events in the region.
Main synoptic Areas subject to flash flooding
Date weather patterns
Events by
Month
October
November
December
January
February
March
23-25 1987
5-12 1984
9-11 1988
19-22 1991
7-11 1990
10-11 1975
Fronts and Low
pressure systems
Troughs and Low
pressure systems
High pressure system
and Fronts
Low pressure
system and Fronts
Tropical Cyclone
"Nancy" and Troughs
Tropical Cyclone
'Alison' and Troughs
East of Sydney, Metropolitan area
Georges, Nepean and Hawkesbury Rivers
Rose Bay, Kensington, Metropolitan area
Hawkesbury Rivers and Mawaira area
Sydney Metropolitan area, Illawarra are
north-east of Sydney
Turramurra, Parramatta areas
and north-west of City
Metropolitan area, north-western
suburbs
Dapto, Wollongong City, Metropolitan area
Mount Keira
All thunderstorm events, included in Table 6.4, were not defined according to their
percipitation produced. They have been selected, because they were the biggest
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CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall 142
thunderstorm rainfall events, in the data-base for each month, introducing floods in the
region. The biggest events are, therefore, presented here to characterise thunderstorm
rainfall in a climatologically more meaningful way. This also includes reference to the
synoptic patterns leading to the situations in which these thunderstorm events occurred.
The biggest and most widespread events for each of the 6 months, October to March are
presented in Table 6.4.
October 1987: In this month five cold fronts moved through NSW and caused some
widespread rainfalls (Bureau, of Meteorology, 1987b). However, the major features of the
month were the low pressure systems that intensified some thunderstorm activities,
bringing widespread rain to the region. These occurred most frequently in the eastern half
of the State. O n the 22nd and through to the 25th, a low pressure system moved from the
west of the State to the north coast (see Appendix C, synoptic charts 6.1 from the 23nd to
the 25th October, 1987b) and brought wide-spread thunderstorm rainfalls to the study
area.
The rain was often heavy, particularly in the south-east of the study area. The heaviest
rainfall for October occurred on, and adjacent to, the Illawarra Escarpment with Cataract
D a m recording 600 m m rain which was a very high rainfall, well above the monthly
average. The heaviest fall in a one day period during the month was 343 m m at Cataract
D a m in the 24 hours to 9 am on the 25th. Another heavy thunderstorm rainfall occurred in
the Metropolitan area. Severe thunderstorms brought minor to moderate flooding in the
Metropolitan area overnight on the 24th / 25th, and, as a result, a number of deaths were
attributed to the thunderstorms. At the same time, minor to moderate flooding occurred in
the Georges River (north-east of Lucus Heights), Nepean-Hawkesbury River (west of
Sydney Basin). Further details on the spatial distribution of rainfall from these
thunderstorms are shown on Figure 6.11.
November 1984: On the 14th of this month, moving troughs, upper air disturbances, and a
coastal low over the Tasman Sea (Jessup, 1985) together produced some very intense
thunderstorm rainfalls, and as a result, floods in many parts of the Sydney region (synoptic
charts 6.2 from the 5nd to the 12th November, 1984, see Appendix C). During this period
atmospheric soundings showed that the airmass above Sydney was moist and unstable up
to 300 mb. Also the temperature sounding had warmed moticeably. This event was marked
by a period of intense thunderstorm rainfall activity which led to severe flash flooding in
the Sydney Metropolitan area (Bureau of Meteorology, 1985). Flooding was most severe
at Rose Bay and Kensington and other inner City areas. In the period from the 9th to the
12th, major flooding was reported at the Hawkesbury River, to the west of Sydney area.
Rainfall was, however, very much above average in many coastal districts.
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CHAPTER SLY Spatial Variation and Distribution of Thunderstorm Rainfall 145
During this time, Sydney received one of its highest rainfalls, more than 500 mm. The
monthly rain totals for this month exceeded 300 m m in the Metropolitan and Illawarra
areas. Extremely heavy rainfalls of over 580 m m occurred in Sydney's City area and the
adjacent eastern suburbs and over the Illawarra Plateau in the south-west of the Sydney
region. At Observatory Hill in the City, 235 m m was recorded in the period from 9 am on
the 8th to 9 a m on the 9th. This was the highest 24 hour November rainfall on record. The
driest part of the Metropolitan area was in the outer western areas with rainfall totals of
less than 150 m m . Sydney's rain was the highest for the State. By contrast the rainfalls in
the western half were mostly well under 120 m m , except for Katoomba which had more
than 300 m m of rain for the same period. Full details of the spatial distribution of
thunderstorm rainfall for this event indicated that in the Sydney Metropolitan area the
rainfall from thunderstorms on the 8th and the 9th gave a record-breaking intense rainfall
for November over the City and near the City areas, particularly the suburbs just east of
the City (see Figure 6.12).
December 1988: This month was dominated by a series of high pressure systems that
moved into the Tasman Sea and directed warm, moist, unstable air across the State
(Bureau, of Meteorology, 1988). By the 8th, cold fronts had moved into the Sydney area
bringing thunderstorm activity (see Appendix C, synoptic charts 6.3 from the 9th to the
11th of December, 1988). Rainfalls from these thunderstorms caused considerable damage
in the Sydney region. For example, on the 9th, a thunderstorm in the Metropolitan area
dropped 35 m m of rain in 30 minutes at the Sydney Regional Office, where flash flooding
resulted. During this event the Metropolitan area and the North-east of Sydney received
more that 110 m m rain. The areas just to the west of Bankstown and the Illawarra
Escarpment were subject to high intense rainfalls from these thunderstorms (see Figure
6.13).
January 1991: Between the 19th and the 22nd of January, 1991, a low pressure system
accompanied by a series of fronts located at the western side of the Sydney region - was
developed over inland N S W bringing widespread thunderstorms over the Sydney region
and causing considerable damage in the Metropolitan area from 19th to 22 January 1991
(Appendix C, synoptic charts 6.4). Some of the meteorological conditions such as low-
level moisture, thermodynamic instability, and a lifting mechanism, reduced the
atmospheric stability throughout the region, and as a result produced some thunderstorms.
It was reported that a severe thunderstorm moved from the south-west of Camden, over
Parramatta and through Turramurra and Palm Beach before moving out to sea (Bureau of
Meteorology, 1991b). Very heavy rain, up to 90 m m , in a severe thunderstorm was
reported in the north-west of the City. The heaviest rainfall occurred to the north-east of
the Turramurra area under the growing thunderstorm cells (Spark and Casinader, 1995).
CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall 146
This caused extensive damage and minor flash flooding in the areas, just north of
Parramatta. Generally, during this time, the central parts of the Sydney region, from the
south-west to the north-east, received a large amount of rain from moving thunderstorms.
Other parts of the study area had, on average, less than 20 m m of rain (see Figure 6.14)
February 1990: Between the 7th and the 11th February, 1990, after tropical cyclone
"Nancy" passed southwards from the Coral Sea (where it had developed) over the Sydney
region, a trough caused unstable conditions bringing thunderstorm activity (see Appendix
C, synoptic charts 6.5 from the 7th to the 11th February 1990). These unstable conditions
caused further thunderstorms and rains, particularly in the Metropolitan area and flash
flooding in the northern and north-west suburbs (Bureau of Meteorology, 1990). Over this
period, other parts of Sydney, specially the southern parts of the Sydney region, had less
than 40 millimetres of rainfall (see Figure 6.15).
March 1975: Tropical cyclone "Alison" and other associated weather features such as
upper level currents and a trough line dominated during the first half of this month (Bureau
of Meteorology, 1975). As a result, thunderstorms developed in the Sydney area on the
10th and the 11th March, 1975. A moist air mass had been advected to the coast by a
tropical cyclone well to the north. This air mass was almost saturated. A n upper level low
moved from the west across the area (see Appendix C, synoptic charts 6.6 from the 10th
to the 11th March, 1975). Temperature and dew-point temperature soundings at Sydney
Airport showed that the instability in the upper atmospheric was high enough to provide
enouph buoyancy for thunderstorm development.
All these weather situations caused torrential rain of more than 440 mm to occur in the
Illawarra district and Sydney Metropolitan areas. The isohyet Figure 6.16 shows March
thunderstorm rainfall as an extremely localised event, with maximum falls over the south
east of the study area. This event was described by Armstrong and Colquhoun (1976).
They showed that the thunderstorm rainfall was concentrated over Kiama and Mount Keira
just west of Wollongong. There was major flooding in Dapto, located south of
Wollongong, and in the Sydney Metropolitan area which resulted in flash flooding. Except
for a small area over the Blue Mountains, much of the western parts of the region,
particularly over the central plains at Richmond and Camden, had rainfalls of under 50
millimetres.
In brief, from the general climatological point of view, thunderstorm rainfall distribution is
meaningful in terms of the synoptic processes leading to such high variations in space.
Spatial variations of the seasonal averages and thunderstorm rainfall values for the biggest
events may, however, suggest that the great spatial variability of thunderstorm rainfall, in
the Sydney region, can not only be attributed to the synoptic weather patterns.
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CHAPTER SLY Spatial Variation and Distribution of Thunderstorm Rainfall 149
6.7 Discussion
This chapter has presented details of spatial variation and distribution in thunderstorm
rainfall resulting from the spring and summer averages, and from the biggest thunderstorm
rainfall events for each of the six months (October to March for a 34 year period, from
1960 to 1993). The results described in this chapter, are only representative of the Sydney
region with its prevailing climate type. Because of the arbitrary criterion for selection of
the thunderstorm events , the results can only be generalised for relatively big and
widespread thunderstorms.
The criteria outlined above (in section 6.4) were employed in the determination of the
spatial distribution and variability of thunderstorm rainfall observations using data supplied
by the Sydney Water and the Bureau of Meteorology of the Sydney region. Using these
criteria, it was therefore suggested that over a long period of time there will be a series of
thunderstorms passing over each individual station, on Sydney's thunderstorm-days,
producing various precipitation amounts. Given a sufficient time period (at least 10 years
in this study and more than 100 observations) this will produce general thunderstorm
rainfall statistics. A n immediate benefit of using this criteria is that it is possible to consider
many thunderstorms which developed further, and became larger and lived longer, over the
study area in the past 34 years. Examples of these types of events occurred in March 1975
and November 1984.
In order to allow direct inter-station comparisons, a gamma function was applied to
thunderstorm data. The probability distribution of thunderstorm rainfall amounts at each
station can be expressed by two summarising measures, the alpha and beta values. During
the last decades, this technique has been widely used by many investigators in meteorology
and climatology. For example, Mooley and Crutcher (1968) used it in India for rainfall
analysis, while Simpson (1972) used the g a m m a distribution in single-cloud rainfall analysis
in the south of Florida. This technique was also applied to the study of the spatial
distribution of rainfall in the Hunter Region (located at the north of the N S W ) by
Bridgman (1984). Richardson (1982) modelled the distribution of daily rainfall amounts
from 10 locations in the United States. Later, Easterling (1989) used this technique to
differentiate between different regions in the U S A in terms of thunderstorm rainfall
patterns. Finally, Fogel and Hyun (1990) applied the g a m m a distribution technique to data
to simulate the spatial variation of thunderstorm rainfall in the U S A . They all concluded
that, unless other models can be shown to have a clear advantage over the g a m m a
distribution for a given application, the g a m m a distribution should be the appropriate
choice of models for most applications.
CHAPTER SLY Spatial Variation and Distribution of Thunderstorm Rainfall 150
In the present study, the results obtained by using the gamma distributions have established
three main spatial patterns in the Sydney region, indicating topographic, coastal and non-
coastal (inland) areas. The distribution of mean thunderstorm rainfall amounts confirms the
reliability of these patterns. The geographical distribution of beta and alpha values illustrate
that, in total, coastal areas are subject to thunderstorms with a high probability of rainfall.
In the south-west extension of the coastal area, over the Illawarra Escarpment, topography
influences thunderstorm rainfall amounts. The relationship between rainfall and topography
of the Illawarra Escarpment was already detected by several other researchers (Bryant,
1982 and Cox, 1983), where a distinct daily thunderstorm high rainfall amount can be also
seen in both spring and summer season averages and even for some of the biggest event
values. High thunderstorm rainfall totals in the vicinity of elevated topography of the
Illawarra Escarpment suggest orographic enhancement of instability, particularly for sites
facing the east. At these times of the year the prevailing easterly moist winds provide much
of the moisture needed for thunderstorm activity in the region (Sumner, 1983b).
In the area west of Sydney, over the mountains, there appears to be at least two different
patterns to thunderstorm rainfall. The Blue Mountains area, located in the north-west of
the study area has high rainfall, particularly in the summer months. This may be due to
orographic influences. Over the Southern Tablelands located in the south-west corner of
the Sydney region, however, the topographic influence disappears, showing considerably
lower rainfall amounts from thunderstorms. This low annual rainfall was confirmed by the
Sydney's Weather Bureau (1979). A more recent investigation by Matthews and Geerts
(1995) suggested that, in summer, thunderstorms were relatively less likely over the
Southern Tablelands.
Along the coast, in the eastern part of the Sydney region, the greatest proportions of
rainfall from thunderstorms occurs over the Central Business District ( C B D ) and over the
Turramurra area just north of the City. The increased roughness associated with variations
in tall buildings could also affect the spatial distribution of thunderstorm rainfall. The
pronounced highest rainfalls over and nearby City may support the theory of the heat
island, and particularly the mechanical effect of an extended urban area on rainfall
enhancement by promoting atmospheric instability (Goldreich and Manes, 1979). The
surface roughness, caused by many tall buildings mainly located in the Sydney's
Metropolitan area, may increase mechanical turbulence, thus increasing the instability. In a
case study over London it was suggested that the mechanical effect of the urban area may
be of prime importance in the process of urban enhancement of precipitation (Atkinson,
1975).
As discussed in Chapter 2, these areas are also under the influence of sea-breeze
circulations, which work with relatively high surface temperatures and readily available
CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall Ml
moisture from the nearby ocean to enhance convective rainfall. This influence may extend
northward and southward along the coastlines during the summer months. The effect may
also be due to the effect of urban heating or particles provided by pollution, both factors
can enhance thunderstorm rainfall. These will be discussed in the following chapter.
In addition, the gamma parameters indicated that there was a relatively low incidence of
high-intensity thunderstorm rainfall in the Sydney basin centred on the Hawkesbury -
Nepean river between the Blue Mountains to the west and the coastal areas in the east.
This low can extend over the adjacent mountain region and to the south-eastern parts of
the study area. There, thunderstorm rainfall is predominantly low, especially for the biggest
thunderstorm events. These general patterns of thunderstorm rainfall distributions may be
attributed to the following phenomena.
First of all, the selected events can occur when some kind of low level cyclones, fronts,
troughs or local convection systems are active overhead or in nearby areas. These systems
are known to initiate or enhance thunderstorm activity in the region (Bryant, 1991). This is
of course not surprising, since synoptic systems can potentially produce heavy
thunderstorm rainfall over large areas (Speer and Geerts, 1994). The systems which are
favoured for thunderstorm activities and associated rainfalls, have a maximum frequency in
spring and summer (Matthews and Geerts, 1995). However, during the late spring and
summer months, isolated convective thunderstorms may occur in any part of the study
area. These events have also been known to contribute to the production of a high portion
of total thunderstorm rainfall observations. Thunderstorm development in the Sydney
region which was discussed in Chapter 2 supports this idea.
Secondly, the main rain-bearing depressions moving from the north-east to the south-west,
or from the east to the west, can be influenced by the ocean when they are passing over the
coast. Generally, very moist air over the N S W coast are advected from warm tropical and
sub-tropical waters associated with the moisture of the East Australian current. These
conditions occur predominantly in the period of January to M a y when the sea-surface
temperatures are higher and the prevailing and saturated easterlies winds cause
thunderstorms to move inland to adjacent coastal areas from this convection (Eagle and
Geary, 1985). The passage from the sea to the land, together with the forced ascent due to
physical environment effects, are thought to be responsible for some of the spatial patterns
observed.
Superimposed on the above pattern are the effects of localised topography. It appears that
the coastal area, where 'the Metropolitan area' is located, particularly on the Hornsby
Plateau has experienced much larger thunderstorm rainfall amounts. Also, the uplands to
the west of the Sydney region (Katoomba), and the Illawarra Escarpment located in the
CHAPTER SLY Spatial Variation and Distribution of Thunderstorm Rainfall HI
south-east of the region, show clear topographic enhancement of rainfall during
thunderstorm rainfall events.
Although this work has used data from selected thunderstorm days for the Sydney region
and also the six biggest major thunderstorm rainfall events from each month, the results
obtained could be linked closely to the major synoptic weather patterns and physical
features of the Sydney region. Because of the large number of observations used to
determine thunderstorm rainfall values, if provided the best available mathematical estimate
of the probability of thunderstorm rainfall amounts and, as a result, the true average
amount of rainfall from thunderstorms could be expected. Generally, most of the studies in
the region, cited above or in the literature, have explicitly concentrated upon one or two
short terms, severe thunderstorm events (Batt, et al. 1995), or thunderstorm rainfall
(Armstrong and Colquhoun, 1976). The present chapter, however, has focused on the
study of thunderstorm rainfall in general during the warm seasons over a 34 year period in
order to define more precisely the regional patterns of thunderstorm rainfall distribution
over the longer period in the Sydney region.
6.8 Summary and Conclusion
The purpose of this chapter was to focus attention on the patterns of the spatial variation
and distribution of thunderstorm rainfall during the thundery months of the year (October
to March). According to the applied criteria, the amount of rainfall recorded during a
thunderstorm-day event was derived for 191 stations in the Sydney region by season for a
34-year period (from 1960 to 1993).
The probability distribution of thunderstorm rainfall amounts was described using the
gamma distribution. This distribution provided two measures which describe the patterns
of thunderstorm rainfall in the Sydney region well. The g a m m a distribution was therefore
found to be a suitable technique for characterising the distribution of thunderstorm rainfall
amounts at individual observation sites (rainfall stations). This method also allows
specification of the probability distribution of rainfall amounts and has the potential to be a
predictive tool. As a result, the g a m m a distribution, as a summarising function, could be
regarded a basis tool for defining a thunderstorm rainfall climatology in the Sydney region.
To compare these values with actual rainfalls, a GIS method was used to characterise the
spatial distribution of thunderstorm rainfall patterns over the Sydney region.
The geographical representation of alpha and beta values, including the mean rainfall
values from thunderstorms, indicated that there is considerable spatial variability in rainfall
related to Sydney's physical environment. It seems that while the spatial distribution of
thunderstorm rainfall follows a gradient between inland and coastal areas, it is also
CHAPTER SIX Spatial Variation and Distribution of Thunderstorm Rainfall 153
influenced by the topography of the region. Two major topographic features of the region,
such as the Illawarra Plateau and the Blue Mountains, are seen as areas with high rainfall.
Although the development of thunderstorms over different areas suggests that there are
synoptic-scale processes which cause such thunderstorm rainfalls, these processes are most
likely not solely responsible for the resulting variation and distribution patterns. More
detailed analysis of thunderstorm rainfall amounts are required to explain the relationship
between amounts and such parameters as proximity to sea, topography and landuse
patterns. The next chapter pursues these relationships.
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters ISA
CHAPTER 7
RELATIONSHIPS BETWEEN THUNDERSTORM
RAINFALL AND PHYSIOGRAPHIC PARAMETERS
7.1 Introduction
The spatial analysis of thunderstorm rainfall has indicated that the distribution of
thunderstorm rainfalls is highly variable over the Sydney region. This should be apparent
from the results of the previous chapter, particularly in the case of widespread
thunderstorm rainfall events. It was also argued that despite the role of the different
synoptic air patterns, the distribution of thunderstorm rainfall in the region would be
largely a function of physiographic parameters such as elevation, aspect, proximity to the
sea and landuse patterns of the study area. The influence of each of these factors upon the
spatial distribution of thunderstorm rainfall is examined in more detail throughout this
chapter.
In sections 2 and 3 respectively, the data and methods used are described. In section 4, the
elevation and aspects throughout the study area are considered first, as they are the most
important physiographic parameters affecting the distribution of the thunderstorm rainfall
event. Then, the relationship between the proximity to the sea and thunderstorm rainfall is
analysed in section 5. After that, section 6 examines the possible spatial relationship
between the landuse patterns and the distribution of rainfall from thunderstorms over the
study area. In section 7, the areas affected by high thunderstorm rainfalls are highlighted
firstly by using an overlay modelling technique of GIS. Then, a 'stepwise multiple
regression' technique is applied to explore the statistical relationships amongst these
variables. Finally, in section 8, the results obtained are discussed.
7.2 Data Used
In this chapter, some of the Geographical Information Systems (GIS) techniques and also
simple-to-complex statistical methods (for example, a t-test or regression techniques) were
used in order to describe the spatial nature of the data, and to explore the possible
associations between thunderstorm rainfall amounts and several of the important
physiographic parameters of the Sydney region.
Firstly, thunderstorm rainfall data sets, from the previous chapter, have been reassembled.
Then, the average thunderstorm rainfall map was constructed by using data from the six
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 151
largest thunderstorm rainfall events which were taken for each thundery month (October
to March, 1975 to 1993) (see Figure 7.1). These largest thunderstorm rainfall events were
selected because they are quite important in the region (Colquhoun and Shephred, 1985;
Bryant, 1991). There are several reasons for this criterion.
In the literature, the idea has been well established, that the importance of topography in
enhancing the variations in thunderstorm rainfall distribution for each of the weather types
(for example, frontal systems or air masses), is not the same (Osborn, 1982). Some
researchers, for example Amanatidis et al., (1991) think that the relative importance of
each of the topographic factors - such as elevation and aspect to the wind direction, or
facing the sun - in generating thunderstorms may change from day to day, as weather
conditions change. Climatologists such as Chuan and Lockwood (1974) and Passarelli and
Boehme (1983) and Smith (1989) have pointed out that because of the localised nature of
thunderstorms, topography does not always appear to have the same effect upon the
distribution of thunderstorm rainfall over long and short time-spans. Analysis of the
relationships between the average daily thunderstorm rainfall amounts (as they have been
shown in Chapter 6) and physiographic parameters would be helpful in the understanding
of the role of the Sydney's physical environment upon the rainfall distribution pattern.
However, they are not ends in themselves. They are intended to provide a start for the
analysis of individual widespread thunderstorms which are the soundest basis for
determining topographic effects on precipitation deduced from such thunderstorms. This
last aim is within the scope and content of the current chapter.
More importantly, the impact of large thunderstorm rainfall events - from the point of view
of flooding processes which take place within the Sydney region from time to time - is a
very serious concern, because of these disastrous consequences (Riley et al., 1985; Speer
and Geerts, 1994). Therefore, the purpose of this chapter is to discover the relationships
between physiographic parameters and the average of large thunderstorm rainfall events
likely to be recorded on thunder-days with flash-flooding in the region (all thunder-days
and associated floods have been mentioned already in Chapter 6). This procedure will,
therefore, provide opportunities by which the relative effect of each physiographic
parameter upon thunderstorm rainfall data, of a widespread nature, could be characterised.
156
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CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 157
Other data sets and their sources which were used in this study, such as proximity to sea or
landuse data are shown in Table 7.1.
Table 7.1 Origin of the data that used in Chapter 7.
Variables Data Sources and Origin
Thunderstorm rainfall data a) Maps See Chapter 6 b) Point data-sets See Appendix D (Table 7.2)
Elevation Map Digital Elevation Model (DEM)
Aspect Map Created from D E M
Proximity Map Created by GIS Internal Functions
Landuse Map Using Landsat, Hard-Copy Maps and GIS Functions
7.3 Techniques Employed
To analysis the above-mentioned data sets, a GIS method and a set of statistical techniques
were used in the study of thunderstorm rainfall in the region.
7.3.1 GIS Techniques Applied
Increasingly, GIS systems are currently becoming popular in many academic centres, for
example, in universities, for storing, integrating, analysing and displaying various types of
climatic data (Sajecki, 1991; Brignall, et al., 1991). There is a large amount of GIS
software being used for spatial analysis purposes by geographers. In the first phase of data
analysis, GIS techniques were, therefore, applied to explore the spatial characteristics of
the variables.
The SPatial ANalysis System (SPANS), which is a microcomputer-based geographic
information system, was used to analyse data in relation to the Sydney region. S P A N S GIS
is a software product of Tydac Technologies (now Intera-Tydac). The S P A N S 5.3.1
version, using Operating System 2 (OS2) version Warp, is currently being used by the
Geosciences School at the University of Wollongong. S P A N S works with point, vector,
raster and quadtree files (for more details see Chapter 5). In applying the S P A N S GIS, for
the purposes of this study, several steps had to be taken before using the other advanced
GIS functions.
As a first step, the study area - the boundaries of the Sydney region - had to be
established in a S P A N S directory. This was done by means of a set-up menu in S P A N S
GIS. Using S P A N S projection function, a projection (from the master projection list) for
Thunderstorm Rainfall and Physiographic Parameters
the Sydney region was defined. The geographic location of the Sydney region, located in
the Southern Hemisphere, was assigned according to the Equator and the Prime Meridian.
The term "Study Area" is used in SPANS to define the location and description of the
current project. Both S P A N S and E - R M S , which was used for digitizing the basemap and
landuse maps of the study area, require that the geographic co-ordinates of a square or
rectangular boundary be defined for the study area / database (Table 7.3). These co
ordinates define the rectangle in the projection plane containing the study area / database.
Both systems - S P A N S and E - R M S - however allow for irregular areas to be defined in
the study area / database as a study site for analysis and modelling purposes. These are
called the "Basemaps" and domain in S P A N S and E - R M S respectively.
Table 7.3 Limits of the study area / database.
Geographical Coordinating of the Study Area
Min. Max.
Easting 247529 * Easting
Longitude 150° 15' Longitude
Northing 6289469 Northing
Latitude -33° 30' Latitude
* Easting and Northing in meters
The second step, was the preparing and formatting of data sets on thunderstorm
rainfall in S P A N S formats for the average daily thunderstorm rainfall from the six largest
rainfall events (the biggest event of each thundery month was taken from October to
March, from 1975 to 1993) using data from 152 rainfall stations located by latitudes and
longitudes in Table 7.2 (see Appendix D ) . The results of this phase of data analysis have
already been applied to create the thunderstorm rainfall maps, described in Chapter 6.
Also, the digital elevation data ( D E M ) set was added at this stage to the S P A N S GIS
system. D E M data consisted of height digitised at 20 m contour intervals from 1:100,000
scale maps. Over 20741 points were used to describe the topography over the study area,
with 80 m 2 resolution in S P A N S raster format.
At the third step, the SPANS contour module was used to produce raster-based
maps. A n analysis was made by means of a Triangular Irregular Network (TIN) grid
between sets of data points. Interpolation could be linear or non-linear. A non-linear
interpolation usually provides smoother rounded contours which are more visually
appealing. However, there is a high possibility that with a non-linear interpolation, an
isohyet line, for example, may be projected to an unrealistic value exceeding the observed
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CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 161
values. For this reason, linear interpolation was used to interpolate rainfall values on a
raster-based format.
At the fourth step, the "proximity to the sea" of each rainfall station was created
using automatic compilation of buffer zones. For this study, 10 kilometre wide zones were
subjectively defined away from the coastline (see Figure 7.2). Also, using the digital
elevation model ( D E M ) , an aspect map was automatically produced in the S P A N S GIS
environment. In S P A N S GIS, a map of the aspect, which is measured in azimuth degrees,
is computed from a grid elevation map. The orientation of the steepest slope clockwise
with respect to North is computed (Figure 7.3). A slope facing the sky has an aspect of 0°,
facing East, 90°, facing South 180°. If it is facing the west, it has the value 270° (see Table
7.4).
Table 7.4 Aspect classes derived from the DEM model, using SPANS GIS
No. of
Classes
1
2
3
4
5
The fifth
Aspect
Flat
North
East
South
West
Class Degree °
(None)
(>337.5 to 22.5)
(>22.5 to 157.5)
(>157.5 to 202.5)
(>202.5 to 237.5)
Digital Numbers
Lower Limit
255
239
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112
143
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Upper Limit
255
254
15
111
142
238
Sydney region to tl
GIS model. However, no landuse map exists that is primarily concerned with
climatological aspects.
7.3.1.1 Landuse Map of the Sydney Region
According to the literature (for example, Atkinson, 1969 and 1977) different landuse
patterns create different meso-climates. Large urban areas, with their characteristically
warmer urban climates and other thermodynamic effects, are able to enhance convectional
rainfalls (Changnon, 1978; Landsberg, 1981). Therefore, the following procedures were
taken to establish a 'tailer-made landuse map' for the Sydney region:
1) A set of satellite images were used to find the boundaries of different landuse
patterns. 'Landsat' satellite images of the Sydney region cover the whole of the study area,
and were recorded by U S A (NASA) satellites on December 1972, October 1986 and
November 1990. They use false colour composites of bands having different wavelengths.
Such false colour images indicate variations in vegetation types and vigour and they have a
Rainfall and
ground resolution of 30 metres and measure the reflectance of light from the surface at 7
different wavelengths or band-passes.
2) The impact of man on the natural terrain of the Sydney region is clearly shown on
these images. Urban development which is encroaching upon rural areas and natural
wilderness reserves can easily be recognised using these reflectance colours; water appears
as black to very dark-blue, depending on the amount of sediment and depth of water;
vegetation is highly reflective in the infra-red band and shows as red. Natural bushland, in
and around Sydney, appears red-brown. The areas of cultivation, especially along the
Nepean - Hawkesbury river, are light-red. It was possible to delineate the textural
variations and the fine detail of residential development in contrast to adjacent larger
paddocks and cultivated lands.
Table 7.5 Description of landuse types in the Sydney region.
Description _
Type Landuse Patterns Use / Structure Area
Km2
1 Central Business District Metropolitan natural 7.9 0.09
(CBD) very dense built-up areas
with skyscrapers
2 Industrial areas Airports, factories, 120.17 1.36
(IND) refineries
3 Urban-Residential, Barren Compact residential 807.75 9.13
area (URB) with separated treed areas
4 Urban-Residential, Treed Dispersed residential 784.48 8.87
area (URT) with intense treed area
5 Rural / Semi-Urban area Agricultural rural and 885.57 10.01
(RUS) light urbanisation
6 Rural / Open areas Agricultural rural 1292.87 14.61
(RUO) Grass, trees
7 Treed area, National / Dense vegetated areas 4947.67 55.93
Urban parks (TNP) (forest, grass lands)
Dams, lakes and rivers of the study area have not been regarded as part of the landuse map.
3) Digital data produced for the urban development program by the GIS
cartographic section of the Urban Planning Centre (1993) was used to refine the
boundaries of forests and the existing urban and non-urban areas.
163
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CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 161
4) The Sydney region outline plans, produced by the State Planning Authority of
N S W for 1970 and 1994, were used to locate the main industrial areas.
5) These landuse maps were combined and digitised as impute data, using the E-
R M S computer program. Then, the landuse data was imported into the S P A N S GIS
program and converted into a raster format. Seven classes, as shown in Table 7.5 and
Figure 7.4, were delineated for this final map.
Using the landuse map which was created for this study, the Central Business District
(CBD) was known as landuse 'type 1'. The central portion of the City is the most
developed, containing most of the major commercial developments. The centre of Sydney,
with very dense built-up areas and a rough surface topography with tall skylines (see Plate
7.1). There are some small C B D nodes, for example Parramatta's C B D . Because of the
scale of model used in this study, these areas could not be shown in the landuse map of the
Sydney region.
Plate 7.1 Closeup view of heavy commercial landuse showing the part of CBD.
All major industries, factories and airports (type 2) have been categorised as 'Industrial
areas (IND). Plate 7.2 gives an example. The term 'Urban-Residential Barren' (URB) area
has been assigned to that area encompassed by the out-lying boundary of the dense
residential areas (type 3) generally with less tree coverage (see Plate 7.3).
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters Ml
Plate 7.2 Closeup view of heavy industrial landuse (type 2).
Plate 7.3 View of compact residential landuse (type 3) in the Sydney region.
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters J66
In contrast, the term 'Urban-Residential Treed' (URT) was applied to those areas (Plate
7.4) with less dense residential area and much more tree cover (type 4). It should also be
pointed out that there are a few small recreational parks and cemeteries throughout the
Metropolitan area.
Plate 7.4 View of light-moderate residential landuse (type 4).
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters J61
Plate 7.5 View of normal rural / semi-urban area (type 5).
Plate 7.6 Shows example of rural / open areas (type 6).
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 168
Plate 7.7 Closeup view of compact vegetated land cover (type 7) in the Sydney region.
If rural areas are being encroached upon by urban development, they were categorised as
type 5 and they were simply called 'Rural / Semi-Urban' areas (RUS) (see Plate 7.5). But,
cultivated areas or productive rural areas (type 6) with considerably more natural reserves
were classified as the 'Rural / Open' areas (RUO). A n example of this kind of landuse is
shown by Plate 7.6.
Finally, all areas with considerable natural vegetation, including State forests, National
parks, urban water catchments, and many major parks located in the region (type 7), were
traced and defined as 'Treed National and Urban parks' (TNP) (see Plate 7.7).
As a result, seven landuse types comprise the major portion of the Sydney region which
can be seen in Figure 7.4 and the associated Table 7.5. Also, plates 7.1 to 7.7, as
representative of all landuse classes, illustrate a close-up view of different landuse patterns
from the region
7.3.1.2 Advanced SPANS GIS Functions Used
The following analytical functions of the SPANS GIS were utilised to analysis the data and
to produce the final products.
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 162
1) A GIS allows simple statistical comparisons between maps by arranging the data in a contingency table (Bonham-Carter, 1994). In the GIS, a contingency table was,
therefore, first used to test the hypothesis that the area-based distribution of rainfall in the
various categories of one map are independent of, or dependent on, the classes of the
physiographic maps (as independent variables). Statistically, chi-square coefficients are
used to be the measures of the degree of correlation, association or dependence of a
thunderstorm rainfall map to the physiographic maps. In practice, because the contingency table varies in dimension, S P A N S GIS uses three measures of association - such as
Contingency coefficient, Tschuprow's T and Cramer coefficient - to measure the degree of
correlation between two map layers as follows:
First, the contingency coefficient, C , is estimated by
ifo^+ril( GFX2 + n
where <P2 is the goodness-of-fit chi square GFX , and n is the sample size
( o 2 = G F X 2 /n).
Theoretically C lies between 0 and 1 but does not always reach 1, even when the variable
seem completely associated. In square tables (that is, I = J), for instance, its maximum value is (1 -X)lI.
Then, Tschuprow's T can be estimated by SPANS GIS. This estimator varies between 0
(for independence) and 1.0 (dependence), but it can only attain its maximum in square
tables. T is calculated by
• O2 J GFX2
T"^|)(I-IXJ-I)""H(I-IXJ-I)
Also, the Cramer coefficient (V) corrects for some of the deficiencies of the contingency
coefficient C and Tschuprow's T in that it achieves its maximum in asymmetric arrays
(Intera Tydac, 1993). v varies between 0 (no correlation between maps) to a maximum
value of 1 and, then, it is determined by
~&i GFX2
nm (3)
Where m equals the number of classes in column and it is the smaller of (I - 1 or ( J -1),
while n is the number of classes in row.
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 170
In fact, the degrees of freedom for a two-sample test are given by the number of rows in
the contingency table minus one multiplied by the number of columns minus one (Ebdon,
1985). In this study, the above-mentioned three measures of association were found to be
effective analytical techniques in comparing the spatial distribution of thunderstorm rainfall
amount (as a dependent variable) by the number of physiographic parameters (as
independent variables).
2) In the second stage, the 'Map Analyse' functions of GIS were used to calculate
the average areal distribution of rainfall from point measurement (rainfall stations). The
averaging technique was found to be extremely useful when averaging rainfall values over
fixed physiographic units such as topographic, the proximity zones or landuse classes. The
results produced by this function display area-based mean thunderstorm rainfall values for
each class of physiographic maps, for example, the landuse map of the Sydney region.
Such procedures can also help to find new statistics (such as attribute means or totals)
derived from the areal distribution of thunderstorm rainfall based on different classes of
each physiographic parameter.
3) In the third stage, GIS internal reclassification methods and a multi-overlaying
technique were used to analyses all maps (rainfall and physiographic maps) in the GIS
environment. B y using GIS reclassification techniques, it was first determined which of
these areas in the Sydney region, had the highest amounts of rainfall distribution, (more
than 120 millimetres). This rate indicates the medium class on the thunderstorm rainfall
map, and it was subjectively selected (see Figure 7.1). This arbitrary rainfall value was
selected because in Chapter 6, the study of the 6 largest thunderstorm rainfall events
indicated that there is not a definite rainfall value for the onset of floods in the region. It is
possible that the conditions, by which these events occur, differ from one weather system
(with different rainfall intensities and associated amounts) to another, and possibly, it also
depends on the ground conditions at the time of flooding. Armstrong and Colquhoun
(1976) defined a daily heavy thunderstorm rainfall with more than 100 m m isohyet
extended over the Sydney region in March 1975. Therefore, the current study supposed
that areas in the region which have rainfall amounts above the average values of 120 m m ,
are more prone to floods and, as a result, they must be visualised.
Then, the SPANS GIS modelling language, as a powerful analytical mapping tool, was
employed to produce new maps which overlayed the rainfall map and all the independent
physiographic models, simultaneously. This was made possible by writing equations to be
understood by the S P A N S modelling language system (see Table 7.6 located in Appendix
E). The final productions are new maps each with a specific aim using the visualisation
capabilities of GIS techniques. By this, it was possible to show areas, for example on a
landuse map, subject to the highest rainfall from thunderstorms (see Figure 7.5 (a-d)).
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Thunderstorm Rainfall and Physiographic Parameters
4) Finally, the 'Visualise' menu of SPANS which is an important tool in GIS, was
used to annotate all maps and slides it produced. Rainfall maps and topographic features
have been annotated with complete captions, titles, legends and coordinates with desired
pallets. Colours were usually considered for each map as an advantage of using S P A N S
GIS. For numerical and interval values the accuracy of the data were tested using a query
module.
7.3.2 Statistical Techniques Used
In the second phase of data analysis, statistical analysis techniques, consisting of the t-test
and simple regression coefficient techniques, were used to find possible associations
between thunderstorm rainfall amounts and physiographic parameters, individually. In
addition, a stepwise multiple regression (Hauser, 1974; Bryant, 1985a) was used to
construct a statistical model explaining the average thunderstorm rainfall as a relative
function of elevation, aspect, proximity to sea and landuse pattern. These analytical
methods were not only applied to find possible associations between physiographic factors
and thunderstorm rainfall, but also to test the results obtained from the GIS overlaying
map techniques.
7.4. Topography and Rainfall from Thunderstorms
To investigate any possible associations between the topography of the region and
thunnderstorm rainfall, it is necessary to describe the main topographic features of the
Sydney region in detail.
7.4.1 Description of Major Topographic Units
Generally, the Sydney region can be characterised by five topographic units. Most of the
Sydney region is spread out along the gently undulating Cumberland Plain, on average,
less than 100 meters above sea level. This basin is surrounded by four other topographic
units rising to elevations up to 1200 m in height. Sydney covers part of the Hornsby
Plateau in the north-east of the study area, rising to nearly 250 meters above sea level. The
Blue Mountains is located to the west and north-west of the region with elevations higher
than 1200 meters. To a lesser extent the Illawarra Plateau to the south-east of the region
has an average elevation of 350 to 450 meters with a relatively sharp escarpment facing the
Tasman Sea. Finally, the south-western part of the study area can be characterised by a
relatively flat landscape which is a part of the Southern Tablelands. This flat relief lies
approximately 700 meters in elevation on average, above sea level. The elevation map for
the study area contains elevations that range from sea level to 1200 meters at the top of
the Blue Mountains (see Figure 7.6). Also Table 7.8 summaries the area based on
topographic characteristics of the Sydney region.
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CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters L7A
The major river network in the region is the Hawkesbury - Nepean River which flows
through the study area south to north. In the east, the coastline is crossed by rivers such as
the Parramatta and George Rivers. At the coast these rivers form significant estuaries such
as Broken Bay in the north and Botany Bay in the south. Zigzag coastline which is a
boundary between the Sydney region and the Tasman Sea, cuts the region in the east.
7.4.2 Association Between Elevation and Thunderstorm Rainfall
In the literature (reviewed in Chapter 2), a positive correlation of increasing thunderstorm
activity with altitude is well documented (Spreen, 1947 and Reid, 1973). The effect of
mountains on increasing thunderstorm activity is most clearly seen on thunder-days and
rainfall maps (Court, 1960; Duckstein, et al., 1973). Results from many parts of the world
also confirm that thunderstorm rainfall increases with relief (Cheong and Tay, 1982). The
association noted here is broadly true for N S W and whole of Australia (Hobbs, 1972). In
instance, for Hunter Valley ( N S W ) Hutchinson and Bischof (1983) used a new method
(Laplacian Smoothing Spline Function) for estimating the spatial distribution of mean
seasonal and annual rainfalls. The rainfall maps show that the areas with higher elevations
(for example, the Barrington Tops) received much more rainfall than to the low-lands (the
Goulburn River). The stronger influnce was evident in summer reflecting the inflow of
warm saturated air of equatorial origin from the norttheast.
The thunderstorm rainfall-elevation relationships over the Sydney region were defined
using an area cross-tabulation technique between the digital elevation map (Figure 7.6) and
the thunderstorm rainfall map (Figure 7.1). The GIS technique indicated that there is a
spatially significant association between the topography of the region and the rainfall
distribution map. Chi-square coefficients are given in Table 7.7.
Table 7.7 Area cross tabulation results between the topography map of the region and thunderstorm rainfall map.
Thunderstorm
Rainfall M a p
Topographic
Map
Contingency
Coefficient
0.429*
Tschuprow's T
value
0.179
Cramer's V
value
0.213*
* significant at 0.05 level.
The chi-square value is at a significant level of 0.05 which can confirm the existence of a
correlation between variables. However, it does not give any information about the effect
of the topography of the region upon the rainfall distribution pattern.
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 175
The areal based distribution of mean rainfall which was calculated using a GIS technique,
indicates that the distribution is not the same for all topographic classes and the area
distribution itself varies according to the topographic units. It seems that the highest
amounts of rainfall from thunderstorms (more than 120 millimetres) is limited to the 400 -
500 elevation classes.
Table 7.8 shows the areal distribution of thunderstorm rainfall amounts for each
topographic interval. The advantage of this kind of calculation is that it shows simply the
average area of each topographic class from the total area of the region. At the same time,
this estimation gives the rainfall amounts (in percentage) for each individual corresponded
topographic classes.
Table 7.8 The areal distribution of thunderstorm rainfall by topographic classes in per cent Thunderstorm Rainfall Classes in m m
Topographic 35-60 61-90 91-120 121-150 151-180 181-200 Total Area Classes in m K m sq Per cent
1802.7 19.7
1313.2 14.3
942.0 10.3
588.6 6.4
1060 11.6
959.5 10.5
715.9 7.8
590.5 6.4
871.7 9.5
251.5 2.7
74.1 0.8
9170.4 100 19.3 4.1 0.4
* The area of each topographic class in per cent ** The areal distribution of thunderstorm rainfall for each topographic interval in per cent.
Because the initial analysis of thunderstorm rainfall distribution over the study area
indicated that local spatial variations could be influenced by small-scale topographic
features, an attempt was made to look at the thunderstorm rainfall - elevation relationships
using statistical techniques. T o do this, the Sydney region has been divided into four sub-
regions according to the main topographic units of the region: namely, the Blue
Mountains, Hornsby Plateau, Southern Tablelands and the Illawarra Plateau (see Figure
0-50
51 -100
101 - 150
151 - 200
200 - 300
301-400
401 - 500
501 - 600
601 - 800
801 -1000
above 1000
Total km^ %
3.1* 7.7** 0.8 1.4 4.3 5.7 7.6 6.3 10.0 14.9 7.0 9.6 5.9 6.0 8.9 7.4 32.0 39.2 5.6 2.0 0.0 0.0 711.8 7.8
36.3 18.2 60.8 22.2 44.8 11.8 31.6 5.2 36.8 10.9 28.9 7.7 37.2 7.4 51.3 8.5 33.2 8.0 0.3 0.02 0.0 0.0 3588.0 39.1
27.2 18.2 19.6 9.5 26.6 9.3 36.9 8.0 34.0 13.4 25.9 9.2 32.8 8.7 29.8 6.5 22.3 7.2 83.6 7.8 77.5 2.1 2697.8 29.3
24.5 25.3 15.6 11.6 22.5 12.0 23.3 7.8 15.8 9.5 26.4 14.3 20.2 8.2 9.6 3.2 11.7 5.8 10.1 1.4 22.0 0.9 1766.1 19.3
7.4 35.3 2.8 9.6 1.9 4.7 0.6 0.9 3.4 9.7 11.7 29.7 3.9 7.3 0.4 0.6 0.8 1.9 0.4 0.2 0.4 0.07 376.2 4.1
1.3 76.2 0.6 23. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 30.7 0.4
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 126
7.6). The relationships between thunderstorm rainfall amounts and the elevation in each of
these four topographic units were analysed using simple linear regression (Table 7.9).
Table 7.9 A linear regression analysis between thunderstorm rainfall amount and elevation of rainfall stations located in the four topographic units of the Sydney region.
Topographic Units n F value
Blue Mountains = A
Hornsby Plateau = B
Southern Tablelands = C
Illawarra Plateau = D
26
74
21
31
.82
0.002
-.42
.67
.67
.004
.17
.45
49.6
0.33
4.06
30.2
.0001
.0.56*
.05
.0001
* Not significant at 0.05 level
The liner regression analysis indicates that there is a close relationship between average
thunderstorm rainfall and elevation. Over the Blue Mountains and Illawarra Plateau, the
relationship between elevation and rainfall amounts for the average of the largest
thunderstorm rainfall are generally high. However, over the Southern Tablelands,
correlation coefficients between rainfall and elevation are negative. Also, over the Hornsby
Plateau, the effect of elevation upon rainfall amounts is not at the significant level of 0.05.
These results imply that the relationship may be masked by other controlling parameters.
7.4.3 Association between Aspect Classes and Rainfall
The gradient of thunderstorm rainfall with elevation depends not only on the height of
station, but also its aspect (Reid, 1973). In fact, slope, in relation to prevailing wind
direction, provides a basis for the identification of zones of potential relative thunderstorm
rainfall in the region (Sims, 1981; McCutchan and Fox, 1986).
To find an association between the aspect map - as an important topographic feature - and
the distribution of the thunderstorm rainfall map (Figure 7.1) firstly the aspect map of the
region was derived from the D E M model (Figure 7.3). There are 5 main aspect classes: 1)
aspects facing the sky (flat), 2) north, 3) east, 4) south and 5) west directions (see Table
7.4). This procedure helped to analyse and identify the spatial correlation between the
thunderstorm rainfall map and the exposure of each rainfall station to one of the main
aspect classes.
An area cross tabulation technique, was therefore, introduced between above-mentioned
maps. The results are shown in Table 7.10. The association between maps is significant at
0.05 level.
SEVEN Thunderstorm Rainfall and Physiographic Parameters 177
Table 7.10 Area cross tabulation results between the aspect map of the region and thunderstorm rainfall map.
Thunderstorm
Rainfall Map
Aspect
Map
Contingency
Coefficient
0.52*
Tschuprow's T
value
0.37
Cramer's V
value
0.41*
*significant at 0.05 level.
Accordingly, the position of each rainfall station with respect to these aspect classes was
found using the query function of the S P A N S GIS. The average distribution of
thunderstorm rainfall amounts for each individual aspect class was then found using all
rainfall stations data located in the region. A s it is clear from Figure 7.7, the distribution of
thunderstorm rainfall amount, based upon aspect classes, is not the same. There is a
considerable difference between the different aspect classes in obtaining rainfall from
thunderstorms. It seems that stations which are exposed to the west and east receive the
most rainfall from thunderstorms.
West Flat North South East
Aspect Classes
Figure 7.7 The distribution of thunderstorm rainfall in the Sydney region based upon aspect classes.
In order to prove statistically the association which was found by GIS technique, and to
calculate the different distribution pattern which is graphically shown in Figure 7.7, a
multiple regression method was introduced between all aspect classes (as nominal
independent variables) and thunderstorm rainfall amount as a dependent variable (Table
7.11). In this statistical procedure the aspect of west was entered into the model as a
constant variable.
E E
e '3
IB U <u •a B S
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 128
Table 7.11 A multiple regression analysis between aspect classes and thunderstorm rainfall amount. n= 152
Aspect Items
East-West
South-West
Flat-West
North-West
Beta value
(Std. Coef.)
0.08
-0.25
-0.37
-0.22
t-value
0.66
2.45
3.34
2.74
P
0.51*
0.01
0.0009
0.007
* not significant at 0.05 level, r2 = 0.22
In Chapter 2, it was generally argued that the study area can be affected by two main
weather systems associated with thunderstorms. First, air masses thunderstorms which are
created mainly over the elevated terrain of the Sydney region and, then, move from west to
east (Matthews 1993). Second, some weather systems, for instance, Tasman Sea lows and
associated weather features, occasionally, move from the sea over the Sydney region and
causing wide-spread thunderstorm activity. As Figure 7.7 shows, stations facing the west
and east were more exposed to rain-bearing thunderstorm systems. So, using a multiple
regression method (with some nominal variables), just one of these geographic directions
could be keept as a constant parameter, in the analyse of thunderstorm rainfalls.
Clearly, the relationship between west and east-facing aspects is not significant. Both
aspects are subject to the same amount of thunderstorm rain. The Beta value (Std. Coef. in
Table 7.11) and the associated t-value confirm the above-hypothesis. Also, this statistical
technique indicates that there are significant differences between stations which face the
west and stations which are exposed to other aspects, such as the south and north. Flat
topography is of minor importance in controlling thunderstorm rainfall. In total, aspect
classes can explain about 22 per cent of variance in the distribution of thunderstorm
rainfall in the region.
7.5 Proximity to the Sea and Thunderstorm Rainfall Distribution
As a physiographic parameter, proximity to the sea is also known to be a very important
factor in controlling thunderstorm rainfall amounts, especially near the coast (Merva et al.
1976; Berndtsson, 1989). For the Sydney region, it was suggested by several researchers,
for example James (1992), that proximity to coastal areas can increase the amount of
thunderstorm rainfall considerably. In this study, two methods were introduced to find the
possible relationships between proximity to the sea and thunderstorm rainfall distribution.
First, a GIS technique was produced to show the spatial correlation between thunderstorm
rainfall and proximity maps. Then a simple regression method was applied to assess the
statistical significance of the relationship.
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 122
As a first step, using a GIS buffering technique, a proximity map was constructed. Then,
an area cross tabulation was performed between the proximity and thunderstorm rainfall
maps. Results are shown in Table 7.12 and statistically indicate that, in the Sydney region,
there are high positive associations between the spatial distribution of rainfalls from
thunderstorms and the proximity to the coast.
Table 7.12 Area cross tabulation results between the proximity to sea map of the region and thunderstorm rainfall map.
Thunderstorm Rainfall Contingency Tschuprow's T Cramer's V
Maps Coefficient value value
Distance M a p 0.723* 0.394 0.468*
* significant at 0.05 level
Table 7.13 The areal distribution of thunderstorm rainfall by proximity classes in per cent.
Proximity Classes i Km 1-10
11- 20
21- 30
31- 40
41-50
51- 60
61- 70
71- 80
81- 90
91- 100
101- 110
Total km^ %
35-60
0.0* 0.0**
0.0 0.0
0.0 0.0
1.4 2.4
13.5 23.5
36.4 62.6
9.4 11.5
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
711.6 7.8
Thunderstorm Rainfall Classes
61-90
0.0 0.0
2.1 0.7
26.5 9.1
81.3 28.0
78.2 27.0
58.7 20.0
60.9 14.8
2.2 0.4
0.0 0.0
0.0 0.0
0.0 0.0
359.0 39.1
91-120
14.4 5.5
34.6 15.3
49.0 22.4
16.7 7.6
8.3 3.8
4.9 2.2
29.7 9.6
80.7 17.5
72.2 10.2
87.0 5.8
100.0 0.2
2699.3 29.4
121-150
59.1 34.5
53.1 35.8
24.3 17.0
0.6 0.4
0.0 0.0
0.0 0.0
0.0 0.0
17.0 5.6
25.4 5.5
12.4 1.3
0.0 0.0
1766.5 19.3
in m m
151-180
23.5 64.4
10.1 31.9
0.3 0.8
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
2.5 2.5
0.5 0.3
0.0 0.0
376.2 4.1
181-200
3.0 100.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
30.7 0.3
Total Area K m sq Per cent
1030.7
1188.5
1235.7
1234.1
1241.3
1223.4
871.1
584.2
380.0
179.8
3.3
9172.4
11.2
13.0
13.5
13.4
13.6
13.3
9.5
6.4
4.1
2.0
0.04
100
* The area of each proximity class in per cent ** The areal distribution of thunderstorm rainfall for each proximity interval in per cent.
In addition, areal distribution of thunderstorm rainfall amounts, which were calculated
based on the classes shown on the proximity map, are summarised in Table 7.13.
According to these tables, all rainfall in the highest class (180-200 m m ) was distributed
within 10 k m of the coast (zones 1). Also, more than 95 per cent of rainfall (in class 151-
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 1M
180) fell on zones within 20 k m of the sea. Except for parts of the Blue Mountains, the
percentage of high rainfall decreased sharply westwards from the coast.
To verify these results statistically, a simple regression model was applied to the data to
find out the possible relationships. A computer program was first written to calculate the
shortest distance of each station to the average coastline (see the computer program 5,
Appendix A ) . The results of this phase of the analysis are summarised in Table 7.14.
Table 7.14 Correlation coefficients between the proximity to the sea (in Km) and thunderstorm rainfall (average of the biggest thunderstorm rainfall events).
Distance
(proximity to sea)
n
152
r
-0.61
r2
0.37
F-test
87.6
Probability
0.0001
As it is evident from Table 7.14, the proximity to the sea, as a physiographic parameter, is
negatively correlated with thunderstorm rainfall amounts (r2 = 0.37) at 0.0001 significant
level. There is a high possibility that the addition of other independent variables such as,
distance from the mountain ranges, might substantially improve the results of this kind of
analysis. The GIS map also shows small variations in thunderstorm rainfall along the coast
implying the influence of other factors.
7.6 Landuse Patterns and Thunderstorm Rainfall
The purpose of this section is to find and describe possible relationships between urban-
rural landuse patterns and maximum thunderstorm rainfall distribution in the Sydney
region. Recently, a great deal has been written about the influence of urban areas on
climatic factors (Auer, 1978; Henry et al., 1985; Bradshaw and Weaver, 1993). All
suggested that many climatic factors, for example temperature and rainfall patterns, can be
affected by city environments. Other investigations in urban climatology such as those by
Changnon (1973), Changnon and Huff.(1973), Landsberg (1981) and Houghton (1985),
have indicated that surface conditions such as the heat island effect or physical features of
a city, which influence most weather elements, also affect the subsequent rainfall,
especially in the case of meso-scale convective precipitation (see literature chapter).
In the Sydney region, where about 3.5 million people live (Department of Planning, 1995),
the ground surface is covered by houses, paved roads, factories, warehouses and tall office
and apartment blocks. These structures contrast with the ground cover of the surrounding
rural areas, such as forests and open rural areas, and they may produce local differences in
Sydney's climatic environment. In such an environment, land cover may affect, to some
degree, the distribution of thunderstorm rainfall patterns throughout the region. T o
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 181
correlate the distribution of thunderstorm rainfall over the region with landuse patterns,
both G I S methods and statistical techniques were again used.
Firstly, an area cross-tabulation was used to find the correlation between the landuse and
thunderstorm rainfall maps. It was found that chi-square coefficients are significant at 0.05
level.
Table 7.15 Area cross tabulation results between the landuse map of the region and thunderstorm rainfall map.
Thunderstorm Rainfall Contingency Tschuprow's T Cramer's V
M a p Coefficient value value
Landuse M a p 0.58* 0.30 0.32*
* significant at 0.05 level
Secondly, a GIS technique was used to calculate the details of the areal distribution of
thunderstorm rainfall based on different landuse classes. For more details in the areal
distribution of thunderstorm rainfall based on landuse classes see Table 7.16.
Table 7.16 The areal distribution of thunderstorm rainfall by landuse classes in per cent. Thunderstorm Rainfall Classes in nun
Landuse 35-60 61-90 91-120 121-150 151-180 181-200 Total Area Classes K m sq Per cent
7.9 0.09
120.17 1.36
807.8 9.1
784.5 8.9
885.6 10.0
1292.9 14.6
4947.7 55.9
8846.4 100 19.1 4.0 0.3
* The area of each landuse class in per cent.
** The areal distribution of thunderstorm rainfall for each landuse interval in per cent.
The comparison of the rainfall amounts in the different landuse classes indicates that these
stations which are located in built-up areas have more rainfall from thunderstorms, on
average. The highest thunderstorm rainfall amounts are measured in the centre of the
Sydney (CBD).
CBD
IND
URB
URT
RUS
RUO
TNP
Total km^
%
0.0* 0.0**
0.0 0.0 0.0 0.0 1.5 1.8 9.5 12.7 10.5 20.3 8.8 65.2 665.4 7.5
0.0 0.0
39.5 1.4 44.0 10.2 15.5 3.5 88.3 22.3 63.4 23.4 27.9 39.4
3507.6 39.7
0.0 0.0
18.0 0.8 21.9 6.8 25.4 7.7 2.1 0.7 21.4 10.6 38.5 73.3
2597.5 29.4
0.0 0.0
35.6 2.5 20.0 9.6 49.7 23.0 0.0 0.0 4.6 3.5 21.0 61.4 1691.3 19.1
13.1 0.3
6.9 2.3 12.2 27.6 7.5 16.5 0.0 0.0 0.2 0.7 3.8 52.5 356.0 4.0
86.9 24.0
0.0 0.0 1.8 50.7 0.5 12.9 0.0 0.0 0.0 0.0 0.1 12.44 28.5 0.3
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 182
E E
OS
E t-
o • * *
(A L. Ol •O
e 3
200
180
1 60
140
1 20
1 00
80
60
40
20
0 •ttffll i l a a a • Ivffifl • E::::::l • Eaal |
CBD IND URB URT RUO RUS TNP
Landuse Classes
Figure 7.8 Distribution of thunderstorm rainfall in the Sydney region
based upon landuse classes.
Also, the average rainfalls in residential (barren and treed areas) were higher than other
landuse classes. The uneven distribution pattern of thunderstorm rainfall is clearly shown
by Figure 7.8.
Table 7.17 The result of a t-test for rainfall distribution in different landuse classes. Landuse C B D Classes
IND URB URT RUS RUO TNP
CBD
IND 6.7*
URB
URT
RUS
RUO
TNP
4.3*
4.65*
25.7*
9.29*
4.24*
-1.18
-2.68*
4.26*
2.65*
-0.61
-1.67*
4.61*
4.68*
0.73
7.25*
7.34*
2.4*
-1.25
-3.65* -3.60*
Significant Difference at 0.05 level
Statistically, to examine any possible difference among the means of the rainfall stations,
located in different landuse classes, a t-test technique was applied (Shaw, Wheeler, 1985).
The null hypothesis is that there is no difference in the mean thunderstorm rainfall
population between each paired sets of landuse classes, H0:X = Y. The alternative
hypothesis is that mean rainfall differs by a degree that is too great to be attributed to
random sampling variations from a c o m m o n thunderstorm rainfall population. Thus, the
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 183
mean for the stations located, for example in the CBD, should be higher than for those
stations located, for example in the industrial areas, HX:X>Y Thunderstorm rainfall
data were considered for the average of the biggest rainfall events.
The results are shown in Table 7.17. A significant level of 0.05 was set as the criteria for
accepting or rejecting the null hypothesis. The results of the t-test which was introduced
among all landuse classes adds support for the presence of significant differences between
different landuse classes concerning thunderstorm rainfall distribution in the Sydney
region.
Table 7.18 A multiple regression analysis between landuse classes and thunderstorm rainfall, r2 = 0.39
Landuse
Classes
IND-CBD
URB-CBD
URT-CBD
RUO-CBD
RUS-CBD
TNP-CBD
Beta value
(Std. Coef.)
-0.56
-0.84
-0.72
-0.1.03
-0.82
-0.91
t-value
4.92
4.70
3.97
6.97
7.01
5.09
P
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
Clearly, stations located in the C B D and urban-residential areas receive much more
thunderstorm rainfall amounts. To test this idea and to see the total effect of landuse
classes upon the distribution of thunderstorm rainfall, a multiple regression technique was
again introduced between all landuse classes (as nominal independent variables) and
thunderstorm rainfall amount as a dependent variable (see Table 7.18).
Statistically, it was found that there is a significant difference between the centre of Sydney
(CBD) and other landuse classes in obtaining rainfall from thunderstorms. Also, the r2
value indicates that landuse pattern of the region is a very important parameter in
explaining about 39 per cent of variance (in total) in terms of the biggest monthly
thunderstorm rainfall events from 1975 to 1993.
7.7 Overlay Modelling / Multiple Relations
So far, by using the SPANS GIS software, which was employed for pre-processing of
two-map layers analysis, some initial associations among different variables with a spatial
nature, have been established. At the same time, some simple statistical techniques were
used to analyse and find correlations between data sets with a spatial context. In the final
stage, more procedures would be employed to consider the associations among
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters ISA
physiographic parameters (as independent variables) and maximum thunderstorm rainfall
(as a dependent variable) synchronically. In fact, this is the main problem, and there are
two possible approaches to its solution.
The GIS overlaying techniques and the multiple stepwise regression method were applied
to find possible associations between all the variables. These techniques were also used to
consider three important questions (with a climatic content). These were:
1) where are the locations most likely in the region to be subject to the highest
rainfall from thunderstorms?
2) what are the physical characteristics of these locations?
3) and finally what are the statistically significant relationships between the
physiographic parameters and rainfall amounts?
7.7.1 GIS Overlay Modelling
At the first stage in the GIS environment, an overlay modelling technique was introduced
to visualise the area's different physiographic characteristics, for example, topography or
landuse with the highest thunderstorm rainfall. The main aim, was to derive a set of new
maps by imposing the areas of high rainfall (more than 120 m ) over all the independent
maps, in a process known as overlay modelling. For this work, from the rainfall map the
highest rainfall classes were first differentiated using GIS reclassification techniques. These
classes were then spatially imposed over all the physiographic maps of the study area by
writing some equations in the S P A N S GIS system (see Appendix E, Table 7.6). The
resulted Figure 7.5 (a to d) can easily be used to achieve the following information:
1) the amount of the highest thunderstorm rainfall at a specific distance from the
average coast-line (distance zones);
2) topographic classes in which the highest rainfall occurred;
3) variation of rainfall amount with aspect classes;
4) character of a landuse pattern in relation to the highest rainfall amounts;
5) and finally all classes from the physiographic maps exposed to the highest
thunderstorm rainfall amounts which can be visualised simultaneously on the
computer screen.
Results show that areas closer to the sea (zones 1 to 3), about 30 kilometres from the
average coastal line, had high rainfalls. Also, it is evident from Figure 7.5 (a) that zones 8,
9 and even 10, which were located in the west over part of the Blue Mountains, had been
subject to the highest thunderstorm rainfalls. Closer examinations of distant zones from the
coastal areas indicates that there is a considerable variation in rainfall distribution.
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 181
Clearly, high elevations near the coast, for example, the Illawarra Plateau located in the
south-east of the region, and parts of the Hornsby Plateau located in the north-east of the
study area, received the maximum amount of rainfall from thunderstorms. Also, parts of
the Blue Mountains located in the west of the Sydney region, were also susceptible to the
highest rainfall amounts from thunderstorms. However, Figure 7.5 (b) indicates that the
Southern Tablelands in the south-west of the region have not been susceptible to the
highest thunderstorm rainfalls.
A close examination of the aspect classes of the Sydney region shows that generally, in
those areas which were subject to the highest thunderstorm rainfall amounts, aspects
facing the east and west are dominant, except for areas with flat topography which are
located in the east near the coast.(Figure 7.5 (c) ). Exact view of the landuse map of the
region (Figure 7.3) indicates that these areas are used for urban purposes.
Finally, it is clear that built-up areas, for example residential areas, and in particular, areas
which are located in the centre of the City ( C B D ) and east of the Metropolitan area were
much more subject to the highest thunderstorm rainfall amounts. It can be also seen from
Figure 7.5 (d) that some areas in the National parks had higher thunderstorm rainfalls.
7.7.2 Multiple Relations Among Variables
The initial results of GIS and also the statistical techniques have indicated that there are
statistically significant associations among variables explaining the thunderstorm rainfall of
the region. Certainly, a stepwise multi-correlation regression technique and then a Z score
method are needed to verify the results obtained by GIS and statistical techniques to
determine the relative effect of each individual physiographic parameter upon the spatial
distribution of maximum thunderstorm rainfall in the model.
7.7.2.1 Stepwise Multi-Correlation Regression Technique
Before introducing a stepwise multi-correlation regression technique, a correlation matrix
between items of each scale of independent variables and dependent variable was applied.
A correlation matrix is therefore found to be a statistical technique to see the
interrelationships among all items.
The results show that the relationships between many physiographic items and
thunderstorm rainfall are significant at 0.05 level (shown by asterisk). In general, the
interrelations between all items presented in Table 7.19 confirm the reliability of data used.
Accordingly, it can be concluded that most physiographic items which have been used in
SEVEN Thunderstorm Rainfall and Physiographic Parameters 1 M
this study are relatively homogeneous items and therefore they have a c o m m o n
relationship to thunderstorm rainfall.
Table 7.19 Interrelations matrix among physiographic parameters and thunderstorm rainfall (n=152).
1
-.32*
-.14
-.14
-.21*
-.31*
.20*
1
-.14
-.16*
-.23*
-.32*
.27*
1
-.07
-.1
-.14
-.06
1
-.10
-.15
-.31*
1
-.22*
-.36*
1
-.03 1
Variables *1 X2 X3 X4 X; X{ X7 X8 X9 X10 X n X12 X13 X1 4 X15
X\ = Proximity to Sea \
X2 = Elevation 53* \
X3 = Aspect Flat 07 .13 j
X4= East .24* ..05 -.49* 1
X5= South ,12 ..05 -.29* -.35* 1
Xg= North 04 .08 -.13 -.16* -.09 1
X7 = West .06 .25* -.24* -.26* -.18* -.08 1
X8 = Landuse CBD . 15* ..JQ ..10 .04 .14 -.03 -.06 1
Xo= URB ..19* ..35* .32* ..04 -.08 -.11 -.21* .09
Xl0= URT ..23* -.08 -.26* .21* .06 -.04 .0 -.09
Xtl= IND ..os ..17* .21* -.14 .03 -.05 -.1 -.04
Xi2= RUS .23* ..14 .19* ..21* .09 -.05 -.03 -.04
Xi3= RUO .25* .21* .02 -.06 -.13 .22* .08 -.06
X M = TNP 16* 48* ..25* .05 -.01 .05 .24* -.09 -.31*
x15 = Rainfall -.61* .16* -.31* .38* -.14 -.17* .17* .35
* significant at 0.05 level
Independent variables which have been entered into a stepwise regression equation were -
proximity to the sea (in K m ) and the spot elevation (in m ) as interval variables. Other
variables (aspect and landuse classes) were entered on a nominal scale. These two last
variables were transformed into d u m m y variables, and then applied with thunderstorm
rainfall amounts as the dependent variables. Because there were five levels of aspect
classes as nominal scale variables, four dummies were required in the regression model. In
terms of landuse classes, there were seven levels. In general, L-l dummies were required,
where L is the number of levels of the variable to be represented by them (Zar, 1984).
Each independent variable was entered into the regression equation in order to determine
its unique contribution in relation to the other variables. The order in which the
independent variables were entered into the equation has no impact on the outcome
because each variable is treated as though it were the last variable to be entered. The
stepwise regression procedure selects the strongest independent variable in the first stage
and at each new stage, the next most significant variable is added to the equation. The
results of the stepwise regression are presented in Table 7.20.
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 181
Table 7.20 Presents the result of stepwise multiple regression analysis for the average of the biggest thunderstorm rainfall amounts (n=152 ).
Step Multiple R Variance Number of
Numbe Predictor Variable R Square Added in F Value Variable in the
%
1
2
3
4
Total
Proximity to Sea
Landuse
Aspect
Elevation
0.607
0.735
0.810
0.835
0.369
0.54
0.656
0.697
37
17
12
4
70
87.62
42.3
24.5
22.3
1
2
3
4
* All F values are significant at 0.0001 level
The stepwise regression procedure automatically selected the strongest independent
variable (proximity to sea). At the first stage, and at each subsequent stage a new variable
was added to the equation in the order in which they increased variance (up to 70 per cent)
in thunderstorm rainfall amounts. Using a F test, statistically significant variables were
determined at less than 0.001 level of significance.
The rank ordering of variables in terms of their predictive strength for thunderstorm
rainfall is: distance to the sea; landuse pattern of the region; aspect classes; and the
elevation of rainfall stations. Generally, the following results were found in this analysis:
1) Despite the presence of variations in maximum thunderstorm rainfall amounts in
coastal areas (see Figure 7.5 (a)), the correlation coefficient between thunderstorm rainfall
and the distance from the sea is quite high (r2 = 0.37), indicating that proximity to the sea
is the main predictor of thunderstorm rainfall distribution in the region.
2) Although the urban area does not appear to be the best predictor of thunderstorm
rainfall amounts, it increases the variance added in the model significantly, (17 per cent).
This suggests that there is a strong difference between urban ( C B D , U R B and U R T ) and
non-urban ( R U O , R U S and T N P ) areas in the amount of rainfall during major
thunderstorms.
3) Exposure to rain-bearing winds (particularly the east and west aspects), appears
to be the most important topographic factor in explaining statistically some of the
distribution of thunderstorm rainfall in the region. It increases about 12 per cent of
variance in the model.
4) The elevation of the study area is the final important physiographic parameter in
explaining the spatial distribution of thunderstorm rainfall amount (about 4 per cent). It
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 188
can therefore be concluded that the spot elevation does not appear to be the only
topographic feature affecting thunderstorm rainfall distribution over all of the region and
more likely the amount of thunderstorm rainfall does not increase with elevation, as it was
shown in section 7.3.
In brief, these findings support the effects of different independent variables upon the
spatial distribution of thunderstorm rainfall (as a dependent variable) and together explain
about 70 per cent of the variance in thunderstorm rain. The remaining 30 per cent
unexplained variance may be attributed to the other parameters.
7.7.2.2 The Spatial Distribution of 2 Scores Over Sydney
The preceding stepwise analysis provides only summary statements for the entire study
area. Statistically, it summaries the relationships between thunderstorm rainfall and the
four above-mentioned independent variables. Therefore, it is impossible to distinguish
where, in the Sydney region, these relationships hold best.
However, Z scores, derived from the stepwise regression model, provide a means by which
the appropriateness of the model can be assessed spatially. (Berry and Marble, 1968; Shaw
and Wheeler, 1985). Z scores were calculated using the following formula:
x - x z =
SD
where x = observed thunderstorm rainfall values
x = mean of variable x
S D = standard deviation of variable x
z = obtained Z scores
A map was then prepared using GIS interpolation techniques to visualise the Z scores
spatially. The more positive the Z score the better the model fits for that site. For
generalisation purposes, five class intervals were used ranging from > -2 Z to < +2 (see
Figure 7.9).
The spatial distribution of Z scores suggests there are a considerable number of areas
above the mean (Z > 0). As Figure 7.9 shows, over the elevated areas, such as the Blue
Mountains, Illawarra Plateau and over a small part of the Southern Tablelands, Z values
are highly positive. Likewise, Z scores over the Metropolitan area, north of the Parramatta
River and over small areas in the centre of the Sydney region (one located between
Camden and Picton and another located in the northwest of Hornsby Plateau), show
positive spatial variations. These patterns of Z values may indicate that distribution of
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 182
thunderstorm rainfall is, in general, highly correlated positively to many of the parameters
in the stepwise regression.
The remainder of the study area, particularly the Hawkesbury - Nepean Valley and some
small pockets located in coastal areas, have negative Z values (Z < 0). These areas do not
fit the model well for one reason or another. Consequently, it is possible to examine only
those variations in the spatial pattern of potential thunderstorm rainfalls which have a close
relationship with physiographic parameters considered in this study. M o r e importantly, the
produced m a p may also provide valuable insights as follows:
1) establishing and modifying regional boundaries of thunderstorm rainfall
distribution in the Sydney region,
2) selecting unit areas in which to conduct field work,
3) and identifying additional independent variables to be included in future
investigations.
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CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 191
7.8 Discussion
To date, most studies in the Greater Sydney region (for example: Colquhoun and
Shepherd, 1985; Speer and Geerts, 1994) have concentrated on thunderstorm activity over
a few days, examining the synoptic weather patterns which caused the thunderstorm
rainfalls. Although none have concentrated exclusively on the effect of the local
physiographic parameters upon the distribution of thunderstorm rainfall, they have already
acknowledged the importance of the Sydney 's climatic-environmental factors.
The present chapter has addressed this approach by examining the detailed spatial analyses
of thunderstorm rainfall (for the 6 biggest short-term events) with respect to important
physiographic parameters (elevation, aspect, proximity to the sea) and landuse patterns of
the region. These parameters were chosen according to the results of the spatial analyses in
Chapter 6. They were also selected because studies elsewhere indicated they might be
important in controlling the spatial distribution of thunderstorm rainfalls (see Chapter 2).
T w o different methods; GIS techniques, and statistical procedures have been used, one
following the other, to analyse the spatial data across the Sydney region. The information
provided using these techniques confirm and extent the results which have been found by
other researchers.
7.8.1 The Role of Coastal Area
It is evident from the results of this chapter that the coastal areas in the east of the Sydney
region receive much higher thunderstorm rainfall amounts than those located inland in
nearby high relief areas. Over the coastal areas, the following mechanisms are supposed to
be more importance in the controlling of the spatial distribution of thunderstorm rainfall.
It is possible that, meso-scale circulations in the lower troposphere over the coastal areas
develop in response to differential surface heating. This mechanism, in particular, between
the land and the adjacent sea, can cause convectional activity in response to differential
solar radiation during the day, depending on the geographic characteristics of each place
and on weather conditions. Gentry and Moore (1954) and L'hermitte (1974) stressed
mechanisms by which thunderstorm rainfall can increase along Florida's coastal areas. For
the N S W coasts, intense locally thunderstorm activity reflecting coastal influences has been
emphasised by several researchers.
Hobbs (1971), using harmonic analysis, investigated some spatial characteristics of the
rainfall regimes in north eastern N S W . The results indicated that there are four major
terrestrial determinants of the spatial variations in rainfall over the study area. The four
factors concerned were: distance from the coast; distance from the scarp; relief (the scarp)
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 122
and latitude. It appeared that from the four factors mentioned above, the most influential
was distance from the coast. Hobbs (1971) also mentioned that in summer - when
thunderstorms are the most important source of rainfall - although thunderstorms are
experienced over the Tablelands, they are more likely to be accentuated in coastal areas,
particularly when orographic influences operate on air streams with strong easterly
components. Also, Sumner (1983a) highlighted the importance of the coastal plains with
reference to both local convection and the role of sea-breeze fronts in the generation and
enhancement of meso-scale systems such as thunderstorms.
An alternative mechanism is the possibility that occasionally, some weather systems, for
example lows, are accompanied by convection activity enhanced by nearby seas or
evaporative heating processes over by advection at the coastline. In such circumstances,
onshore winds can supply the moisture to the thunderstorm systems evaporated from the
Tasman Sea (Speer and Geerts, 1994). It was also found by Reeder and Smith (1992) that
the coastal areas acts as a stationary convergence zone causing longer duration
thunderstorms. These thunderstorm systems can extend over the coastal areas with intense
rainfalls (James, 1992). However, it was found in the current research that, even when
rainfall from thunderstorms is general over the coastal areas there are still isolated centres
of high rainfall with sharp isohyet gradients.
In addition, thunderstorms which most often develop over the relatively high topography
west of the Sydney region (over the Blue Mountains) can move towards the east of the
region over the coastal areas (Matthews, 1993). Speer and Geerts (1994) supported the
findings of Matthews (see Chapter 2, section 2.6.2). They also found that, in a close
relation with synoptic systems (for example quasi-stationary or eastward moving troughs),
convective systems typically start around midday to the west of Sydney and reach the east
of Sydney in the afternoon to evening. It is therefore more likely that these systems follow
the sources of moisture, available mostly over the coast and nearby Tasman Sea which aids
the convective systems to produce higher rainfall totals over the coastal zones.
7.8.2 Impact of Topographic Factors
The results in this chapter also indicated that both aspect and elevation influence the
amounts of thunderstorm rainfall in the study area, particularly in high relief areas over the
Blue Mountains and the Illawarra Plateau. This occurs mainly because both factors have a
strong effect on the initiation of thunderstorms. The effect of Sydney's high lands on the
distribution of thunderstorm rainfall amount is most clearly seen on maps (figures 6.7, 6.8
and 7.1) showing the relationship between rainfall patterns and terrain height. There are
three possibilities to explain how the region's terrain is able to influence thunderstorm
rainfall so considerably.
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 121
Firstly, one of the most regular and predictable types of thunderstorm activity can occur in
warm seasons over the Blue Mountains (Gentilli, 1971). The daily heating of the hillsides
generates warm up-slope winds which continue rising after reaching the top of the ridges
and trigger deep vertical convection (Maine, 1962). The thunderstorm rainfall patterns
over the Blue Mountains throughout the warm months may be dominated by this
mechanism. More recently, using data recorded by the Lightning Position Tracking System
(see Chapter 2) Laudet et al., (1994) found that the spatial distribution of lightning
(associated with thunderstorms) is closely related to the topography of the region. Their
results support the concept that elevation is a very important physiographic parameter in
controlling thunderstorm occurrence. The relatively high rainfall over parts of the Blue
Mountains from thunderstorms which was clearly shown in the results in Chapter 6 and the
current chapter, may be explained by the above-mentioned mechanism.
Secondly, convection systems can occasionally be developed over the Tasman Sea (Bureau
of Meteorology, 1989) during unstable conditions. These systems may move toward the
west of the region, and as a result, they may be cut off by the elevated terrains due to an
air-mass modification effect. Low-level air may be scavenged of its water by drops falling
from a seeder cloud above. It is likely that as the air descends beyond the high elevated
areas it is dry and cannot restore its water vapour by evaporation from the Tasman Sea,
therefore, without the low-level moisture, convective precipitation would be suppressed.
In fact, this mechanism may explain some of the thunderstorms with high rainfalls over
parts of the Blue Mountains (for example, Katoomba), which is another sign of orographic
control upon the distribution of thunderstorm rainfalls over the region.
Finally, topographic units which are located near the coast have an extra influence upon
thunderstorm rainfall amounts. For example, places along the Illawarra Plateau or Hornsby
Plateau experience very high rainfalls from thunderstorms, illustrating the effect of
elevation and exposure on wind directions. It is more likely that the height and exposures
of these topographic units to the Tasman Sea acts as a barrier to thunderstorms moving
from the east, and isolates coastal areas from those to the west of the plateau. For
example, Shepherd and Colquhoun (1985) studied the meteorological aspects of an
extraordinary flash flood event (17-19 February 1984) near Dapto just south of
Wollongong. They found that a trough had moved slowly from east to west over the
Illawarra area and formed several convective cells. These systems produced maximised
rainfalls during the event. In such a situation, the orographic lifting mechanism clearly
contributed to the short-duration heavy rainfalls along the escarpment. While an
extraordinary amount of rain (in excess of 200 m m ) fell over the region in a band from
Stanwell Tops to Jervis Bay, the heaviest point rainfall recorded was 803 m m at
Wongawilli located along the escarpment facing the east. This topographic effect causes
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters L9A
different rainfall distribution patterns in the region, as is clearly evident from the results of rthe current chapter. The Illawarra Plateau provides an ideal example of thunderstorms
producing more rain parallel to the inland and coast-lines.
How important the above-mentioned mechanisms are, however, can not be clearly
identified. This is because in mountainous areas, for example over the Blue Mountains,
thunderstorm rainfall distribution patterns are very complicated, showing strong
differences over short distances. In contrast with the Blue Mountains or the Illawarra
Plateau, over the Southern Tablelands (located on the south-west of the study area) the
results showed that there is a general decrease in the vigour of thunderstorm rainfalls at all
seasons. This occurs because, a large number of local factors may reduce the amounts of
rainfall received from thunderstorms, and most of these local factors vary greatly with
relief. They include elevation, steepness of slope, relief forms which cause convergence or
divergence of air streams, and aspects, that is, exposure to the rain-bringing winds. A s it is
evident from the aspect map (Figure 7.3), over the Southern Tablelands - where a
relatively flat aspect dominates - topography may not strongly affect the distribution of
thunderstorm rainfall in the region.
7.8.3 Effect of Landuse on Rainfall Distribution
Results of the various analyses may finally lead to the conclusion that different landuse
patterns, for example, residential or C B D areas, are able to affect the spatial distribution of
thunderstorm rainfalls. Evidence of a localised increase in total rainfall from thunderstorms
in spring and summer, over the C B D and generally over the Metropolitan area, can be seen
from figures 6.7 and 6.8 in Chapter 6. This increase was found stronger for the biggest
thunderstorm rainfall events (Figure 7.1). Statistical tests, consisting of t-tests and a
stepwise analysis - which were applied for the point data sets - confirmed the assumption
that in the Sydney region, the effect of 'built-up areas' upon the distribution of
thunderstorm rainfall is real.
More recently, Speer and Geerts (1994) gave examples of high rainfall from thunderstorms
in the study of flash-floods. Radar images, taken from the three storm events (namely for
the 9 March 1989; 10th February 1990; and 2nd April 1992 at the time of the flash-flood
rainfall), indicated that maximum rainfall amounts of more than 100 m m occurred mostly
over the Metropolitan area. This study found that such storms are most c o m m o n in the
summer months in the Sydney Metropolitan area, during the afternoon or evening hours.
O n the basis of these findings, some of the physical-environmental elements of the urban
area appear to be important factors in affecting the amount of thunderstorm rainfall.
SEVEN Thunderstorm Rainfall and Physiographic Parameters 121
It is more likely that urban areas can affect incoming solar radiation changing albedo rates
and heating processes. This happens because the materials used in the City environment,
such as paved surfaces and the multi-faceted nature of the rough urban surface, not only
increase the absorption of heat energy, but also increase heat storage. The results of
investigators such as McGrath (1971) and K e m p and Armstrong (1972) indicate that
generally there is a considerable difference in temperatures between the City and the
outlying rural areas in the Sydney region. The development of a 'heat island' may enhance
vertical motion of air over the City and, as a result, enhance the subsequent convectional
thunderstorm rainfalls. Although no measured data for Sydney's heat island exists,
particularly for over a long time-span, experimental studies by Fitzpatrick and Armstrong
(1973) and Kalma et al. (1973) confirmed that, in the Sydney region, there is a great
difference between urban and non-urban areas in producing artificial energy. The high
spatial variation in artificial heat generation during the day in summer may help the
production of a heat island over the City. This effect can be increased by the high density
of buildings within the City centre ( C B D ) and can create a heat island with greater
cloudiness and higher rainfall.
It is also possible that increased suspended particles in Sydney's atmosphere which cause
pollution, indirectly increase rainfall amounts during thunderstorms. Linacre and Edgar
(1972) give evidence on the atmospheric pollution of Sydney which can be caused by
urban development. There are different sources of pollutants emitted into Sydney's
atmosphere. Industrial and commercial activities, including motor vehicles are important
sources of particle emission (Carras and Johnson, 1982). It is likely that, under calm
weather conditions, urban aerosols such as chemical materials (for example, nitrogen
oxides and hydrocarbons) may act as nuclei or ice nuclei materials and therefore, help to
induce cloud condensation in the atmosphere of Sydney. Despite intensive efforts devoted
to the understanding of Sydney's atmospheric environment in the past (for example,
Taylor, 1992), the role of pollutants upon thunderstorm rainfall remains uncertain.
However, the present thesis shows that such a relationship may exist.
Moreover, the surface roughness of the CBD by way of tall buildings interspersed with
roadways may modify the thunderstorm rainfall distribution to some degree. These
structures contrast with the ground cover of surrounding rural areas, such as forests
(TNP) and open rural areas (RUO), and they can produce local differences in Sydney's
thunderstorm rainfall distribution patterns. Also, the aerodynamic roughness of Sydney's
Metropolitan structure may enhance the development of thunderstorm activity. Generally,
the peak in maximum thunderstorm rainfall over the central part of Sydney is evidence of
the marked impact of the urban area upon rainfall processes. This mechanism is responsible
for 17 per cent of variance in thunderstorm rainfall (Table 7.19).
CHAPTER SEVEN Thunderstorm Rainfall and Physiographic Parameters 196
7.9 Summary and Conclusion
A "climatologically oriented" GIS was utilised to store, manipulate and display topo-
climate data related to Sydney's physical environment. The GIS provided an effective way
of displaying and manipulating the spatial data. Research presented in this chapter also
investigated alternative ways in which the GIS can provide visual support to the analysis of
rainfall distribution in relation to the physiographic parameters of the Sydney region. The
rainfall data (averaged from the six biggest thunderstorm rainfall events) was, therefore,
the fundamental skeleton over which other information, such as physiographic data, has
been performed.
The GIS technique was then followed by a statistical procedure to verify correlations
found between thunderstorm rainfall distribution and all independent variables. The results
obtained can be closely linked to the major physiographic parameters of the Sydney region
which were considered in this study. Analysing the available data, the following
conclusions can be deduced.
1) Increases can be seen in thunderstorm rainfall when the distance from the coast
decreases. Clearly, rainfall increases with proximity to the sea, but there are considerable
variations in rainfall distribution along the coastal areas.
2) The urban area can affect or enhance the development of thunderstorms
particularly over the Metropolitan area. As a result, it can increase thunderstorm rainfall
amounts. The urban maximum results from increases in temperature, humidity, turbulence
and the number of condensation and ice-nuclei substances. Such a physical environment
can considerably influence the spatial distribution of thunderstorm rainfall amounts, hence,
indicating the reality of the urban effects. The strongest effect of urbanisation can be seen
over the C B D and the eastern part of the City near the coast.
3) Rainfall increases with elevation, but exposure to rain-bearing directions (east
and west aspects) also seems to be important factors. Despite the effects of aspect, high-
elevated areas such as the Blue Mountains, the Illawarra Plateau and less importantly the
Hornsby Plateau, are seen to be the more significant topographic units affecting the
distribution of rainfall patterns. These areas are thus found to be subject to the highest
thunderstorm rainfall amounts.
As the percentages of variance from the step-wise regression method has indicated, there
is still considerable unexplained variance (about 30 per cent) which suggests that new
independent variables need to be incorporated in the models predicting the distribution of
thunderstorm rainfall. In fact, the existence of this unexplained variance illustrates that
Thunderstorm Rainfall and Physiographic Parameters 197
factors, other than those under consideration, may contribute to the thunderstorm rainfall
distribution over the Sydney region.
To sum up, although synoptic weather patterns, such as those which were highlighted in
the literature review, can introduce the relatively big and widespread thunderstorm rainfall
events in the Sydney region, most often, as the results of the current chapter have
indicated, there are more than one or two physical-environmental factors which
simultaneously control the spatial distribution of the maximum thunderstorm rainfall
amounts quite significantly.
CHAPTER FIGHT Conclusions 128
CHAPTER 8
CONCLUSIONS
8.1 Introduction
This chapter summarises the findings and outcomes of this thesis. The results, given in
detail in chapters 3 to 7, are presented briefly in the next section. The limitations of the
data and techniques are outlined in section 3. In section 4 the relevant implications of the
research are presented. Suggestions for future work are outlined in section 5.
8.2 Major Conclusions of the Thesis
Historically, the organisation of data concerning thunderstorm rainfall in time and space
has been an important objective for many climatologists. During the last few decades, the
Sydney region has experienced some of the heaviest rainfalls on record from
thunderstorms. The consequent flooding has caused considerable property damage and the
loss of human life (Riley et al., 1985; Colquhoun and Shepherd, 1985; Colls, 1991). The
structure and causes of Sydney's thunderstorms has been well studied (Colquhoun, 1972
and 1994; Speer and Geerts, 1994). Severe thunderstorms can be generated by active
fronts, troughs or squall-lines (Morgan, 1979b), by local disturbances controlled by
climatic-environmental factors (Linacre and Hobbs, 1977), or by supercells (Dickins,
1994). Often thunderstorms originate in response to the daily heating of hill slopes
(Gentilli, 1971). The mountain ranges in the west of the Sydney area can set off
thunderstorms in potentially unstable airflows, and these thunderstorms can then drift over
the adjacent lowlands and coast (Foreman and Rigby, 1990). Local convection in the
Sydney region as a result of surface heating and within warm, humid and unstable air
masses can generate thunderstorms that produce light precipitation (Mitchell and Griffiths,
1993; Batt, 1994). These thunderstorms are common and comprise nearly 95 per cent of
all thunderstorms in the region. O n average they generally produce less than 11 m m of
rainfall. Although these kinds of thunderstorms are normally small in size, they can contain
vigorous parcels of rising and descending air occasionally accompanied by intense rainfalls
(Morgan, 1979a). In coastal areas the presence of the Tasman Sea has a great influence on
the occurrence of precipitation, because it can furnish and sustain a plentiful supply of
moisture. Sea-breeze circulation (Linacre, 1992) along with surface heating of land can
enhance convective activity over coastal areas (Clarke, 1955 and 1960; Drake, 1982; Abbs
CHAPTER EIGHT Conclusions 122
and Physick, 1992). Individual events linked to these factors and producing heavy rainfalls
have been described in the thesis.
Thunderstorm rainfalls are thus neither spatially nor temporally uniform. The prime
objective of this thesis was to examine this variability and relate it to the main climatic
characteristics and physiography of the Sydney region. Four sets of results were obtained.
First, the general behaviour of thunderstorm rainfall (frequency and amount) were
characterised over time (at yearly, seasonal, monthly and diurnal levels) using measures of
central tendency and dispersion. Results, presented in Chapter 3, indicate that
thunderstorms have marked diurnal and seasonal variations, and are most frequent in the
spring (October to November) and summer (January to March) during the late afternoon
and early evening. Thunderstorms are most frequent over the west of the region and least
frequent over the lowland interior. Stations which are located near the ocean receive more
thunderstorm rainfall than those located inland, even near the Blue Mountains. Second
more detailed associations exist between thunderstorm rainfall and climatic factors such as
air and sea temperatures, and air humidity. Results presented in Chapter 4 indicate that
there are casual relationships between these climatic variables and thunderstorms,
particularly for coastal stations. This association becomes weaker as one moves inland.
Specifically, the amount of thunderstorm rainfall is affected by sea-surface temperature,
the effects of unequal heating of land surfaces and the availability of moisture in the
atmosphere. Third, the patterns of spatial variation and distribution of thunderstorm
rainfall during the thundery months of the year (October to March) were examined.
Results are presented in Chapter 6 using data from 191 stations in the Sydney region, for
the 34-year period 1960 to 1993. The probability distribution of thunderstorm rainfall
amounts was shown to be described well using the g a m m a distribution. This technique
provided two measures (beta and alpha values) which described the patterns of
thunderstorm rainfall in the Sydney region. In addition, a GIS method was used to
characterise the spatial pattern of thunderstorm rainfall over the Sydney region. The
distribution of mean thunderstorm rainfall in the Sydney region, reflects topographic,
coastal and urban effects. Thunderstorm rainfall increases with proximity to the ocean, in
the vicinity of elevated topography over the Illawarra Plateau and Blue Mountains, and
over built-up metropolitan areas, especially the C B D and eastern suburbs of the city. In the
latter case, rainfall may be enhanced by urban heating, increased surface roughness and air
pollution. These urban areas are more subject to flash flooding. Finally, because the
physical environment affects the spatial distribution of thunderstorm rainfall, more detailed
analysis was undertaken using a "climatologically oriented" GIS in conjunction with a
stepwise regression technique. These results are presented in Chapter 7. While synoptic
conditions initiate thunderstorm weather systems, physiographic parameters considerably
influence the spatial distribution of the resulting rainfall amounts.
CHAPTER EIGHT Conclusions 200
8.3 Limitations of the Study
8.3 1 Limitations of Data Used
During the research considerable time was spent in collecting data from different relevant
sources and then summarising these. The main limitations of these data can be summarised
as follows:
1) The distribution of the stations recording thunder reflects the distribution of
major population clusters, older suburbs, dams, post offices and railway stations. This
sampling network is spatially uneven and forms only a first approximation of the
distribution of any rainfall. In addition, some records were sporadic over time. To
overcome this limitation, only the best and longest records were initially chosen to study
the generalised distribution of thunderstorm rainfall over time.
2) The number of daily thunderstorm observations for each particular station was
not the same, because some stations reported every three hours and some only once or
twice a day. To overcome this problem, thunderstorm occurrence was studied on a daily
basis. If a station recorded at least one observation of thunder on a particular day, then all
of the rainfall for that day was considered as being thunderstorm derived. All rainfall
values used in this study should thus be considered maximum amounts.
3) Some rainfall stations did not have a complete record for the 34 years under
consideration. Therefore, the period of study may not be represented adequately for some
parts of the region. While no temporal constraints appear to be defined in the literature for
rainfall records (Alexander, 1945; Longley, 1952 and 1974), in this study, only records of
ten years or more were utilised.
4) Some problems were encountered with missing data. Generally, both thunder-
recording and rainfall stations with extensive missing records have been removed from the
data base. N o attempt was made to compensate for missing values in the remaining
records. There is, therefore, a possibility of error being introduced into the data set
because of missing data.
5) The sea-surface temperature data were recorded weekly. Therefore, it was not
possible to calculate and analyse the associations between thunderstorm data and sea
surface temperature on a daily basis.
6) Generally, the north-east and south-west areas of the Sydney region suffer from a
poor coverage of rain-gauges, leading to an incomplete picture of thunderstorm rainfall.
CHAPTER EIGHT Conclusions 201
This uneven distribution of rainfall stations, particularly in mountainous areas, together
with the equally problematic issue of missing data, should be satisfactorily dealt with in
future research in order to understand accurately the distribution of thunderstorm rainfall.
One of the best ways to overcome this problem is to increase the density of the rain-gauge
network in the Greater Sydney Region.
8.3.2 Limitations of Techniques Applied
GIS was found to be a useful tool for data analysis and display of climatic variables. GIS
has many advantages. For example, it has the capability of combining large amounts of
spatial data in varied formats. Furthermore, GIS was very helpful in extracting the various
study area characteristics, derived from topographic and landuse maps. While the simple
functions of GIS provided the means for handling massive data files, GIS also allowed
multivariate analyses of the rainfall data and associated physiographic variables of both a
spatial and non-spatial nature. However, there are some disadvantages in using the S P A N S
GIS technique for climatic purposes:
1) The major limitation to GIS, in climatic modelling, is the current inability of the
S P A N S system to incorporate temporal change. The application of dynamic GIS in this
field requires specialised analytical tools. The author hopes to carry out further study on
this topic.
2) The raster-based GIS is limited by the fact that the minimum resolution of a
raster map has to be decided when the map is first created. This resolution has to be set
small enough in order not to lose spatial information; however this sometimes leads to very
large grids where interpolation of variables may occur without much underlying data
control. Currently the interpolation functions in S P A N S GIS are primitive. A broader
range of interpolation algorithms is required to overcome this problem. For example, a
very flexible TIN function is needed to create smoother isohyets.
3) Given that our future climatic data and our future modelling requirements will
become more complex, it is necessary that the S P A N S GIS can include new functions
which will permit improved transfer of data, more efficient storage, and more flexibility
and faster modelling capabilities. If these improvements are made, the S P A N S GIS can be
more widely used in research of climatic data.
8.4 Advantages and Implications of the Study
While researchers have examined many different aspects of thunderstorm activity in the
Sydney region over the last four decades, three points have not been thoroughly
considered. These are:
CHAPTER EIGHT Conclusions 202
1) the general characteristics of the thunderstorm rainfall distribution over time;
2) the causes of variation in thunderstorm rainfalls across the region; and
3) the spatial variability and distribution of thunderstorm rainfall over time.
These aspects of thunderstorm rainfall climatology of the Sydney region were examined in
the present thesis.
8.4.1 Advantages of the Study
The results of the above mentioned aims are presented in chapters 3 to 7. Thunderstorm
activity can be viewed not only in terms of climatic variables, but also in terms of
physiographic parameters which control the variation and distribution of thunderstorm
rainfall. Three important techniques used in defining these relationships may have wider
applicability in climate studies:
1) First, and of great importance, the gamma method offers summarised
mathematical information about the variability of rainfall amounts from thunderstorms.
This provides a convenient means of estimating the probabilities of receiving rainfall based
on point observations, showing consistent spatial patterns. These patterns can be
realistically compared with the real rainfall data which can then be linked to the
mechanisms creating and controlling thunderstorms, such as synoptic systems and
physiographic parameters. The spatial distribution of alpha and beta values help in broad-
scale environmental planning and in establishing climatic regions where further detailed
analyses, such as time series, can be performed.
2) Second, GIS computer technology offers an excellent means of analysing
multivariate climatic-physiographic relationships. GIS can play a useful role in the analysis
of the spatial distribution of rainfalls from thunderstorms. GIS also offers modelling
capability of data from up to 19 map-layers. The application of GIS to the modelling of
thunderstorm rainfall potential based upon physiographic features is one of the important
outcomes of this thesis (see Chapter 5). M a n y climatologists (for example, Sajecki, 1991;
Brignall et al., 1991) believe that the S P A N S GIS system provides the logical framework
for complex analysis and mapping of such climatic data.
3) Finally, the stepwise multiple regression technique is a useful technique for
defining the relative importance of important climatic and physiographic factors influencing
thunderstorm rainfall. This procedure was used twice in the study. The most important
advantage of this technique is that it can be used to obtain the maximum degree of
explanation of a dependent variable from a composite of independent variables.
CHAPTER EIGHT Conclusions 201
8.4.2 Implications of the Study
Information obtained about the distribution of thunderstorm rainfall in the Sydney region
in this thesis has some important management consequences as follows:
First, the results of this study explain, simply, the times when thunderstorm rainfall
should occur in different zones of the Sydney region. This was highlighted in Chapter 3
where the temporal distribution of thunderstorm rainfall was discussed. Such information
can be used by the State Emergency Service to narrow down the time of day when a
response to heavy precipitation during thunderstorms is most likely to be required.
Second, spatial fluctuations in thunderstorms impact differently on human activity
and development across the Sydney region. While isohyet maps contain very
comprehensive information about the distribution of rainfall over the region, the gamma
estimators (beta and alpha values) facilitate easy reading of the displayed probability values
of thunderstorm rainfall at any point in the study area. Urbanisation is progressively
increasing the amount of run-off during short rainfall events and leading to flash flooding.
Areas where this is most common can be targeted by planners and engineers using these
gamma maps and their associated Z-Score values.
Third, results from the study should make planners aware of the increased risk of
heavy rain from thunderstorms induced by heavy industrial, residential and commercial
development. The State Planning Authority of N S W (1970 and 1994) believes that within
25 years, more than 70 per cent of the people of N S W will live in the Greater Sydney
Region. This will only exacerbate the chance and impact of heavy thunderstorm rainfall. It
is imperative that detailed data on the urban heat island be acquired in order to understand
how intense urbanisation affects the rainfall process. Perhaps a project like M E T R O M E X
(Changnon et al., 1971) should be conducted in the Sydney region or the Metropolitan Air
Quality Study ( M A Q S ) extended in scope to cover the flooding risk from intense
thunderstorms.
Fourth, the spatial variations in urban rainfall are more important than those in rural
areas, simply because more people will be affected. Adequate information on thunderstorm
rainfall within Sydney can be of major importance in the planning and design of drainage
systems, outdoor social and recreational activities, intra-city transport systems, watershed
protection, and finally flood prevention. It is hoped that the recognition of climatological
landuse types defined in the thesis can be of assistance in this planning. Urban
climatologists should work closely with the landuse planners, zoning authorities,
architects, and hydrologists to ensure that heat islands are reduced as much as possible;
CHAPTER EIGHT Conclusions 20A
that vegetative areas are interspersed throughout the Metropolitan area; that air pollution
is reduced at its source, particularly over the C B D and nearby coast; and that hydrologic
problems are reduced by increasing absorptive areas, providing ample storage in streams
and lakes, and by constructing more channels to handle the rate of run-off. This planning is
needed particularly in those areas which have been shown by this study to have a high
potential for heavy amounts of thunderstorm rainfall and subsequent flash flooding.
8.5 Suggestions for Future Studies
This thesis is not inclusive. Important areas of further research should include the
following:
1) the regional development of synoptic weather maps for the Greater Sydney
Region that can be linked to thunderstorm development. This would allow rainfall
predictions to be made in advance of the progress of a thunderstorm;
2) the application of the gamma distribution to other types of synoptic patterns
generating heavy rainfall in the Sydney region. The present study did not include all
rainfalls produced from east coast lows which are a major factor in generating heavy
rainfall in the Sydney region (Bryant, 1991);
3) the techniques used in the study should be applied to a wider area of the New
South Wales coast where other heavy thunderstorm rainfall events are know to occur; and
4) sophisticated time series models should be applied to the data to better
characterise temporal change in thunderstorm rainfalls. The application of advanced
models, such as the Auto-Regressive Integrated Moving Averages ( A R T M A ) technique,
may help to construct better thunderstorm rainfall prediction models over time.
8.6 Concluding Remarks
Although the problems caused by thunderstorms are many and varied and the solutions do
not seem simple, this current thesis has been built upon solid research provided over the
last 25 years by meteorologists and climatologists (for example Hobbs, 1971, 1972, 1995;
Colquhoun et al., 1985; Bryant, 1991; Williams, 1991; Linacre, 1992; Griffiths et al.,
1993; Matthews, 1993; Colquhoun, 1994; Laudet et al., 1994; Speer and Geerts, 1994;
Batt et al., 1995; and Matthews and Geerts, 1995). It is hoped that information and models
contained within this thesis can also contribute to this understanding of thunderstorm
rainfall. In terms of the spatial variation and distribution of thunderstorm rainfalls, most
specifically from the analyses of the available data, the following conclusions can also be
deduced:
CHAPTER EIGHT Conclusions 201
1) The distribution of thunderstorms over the Sydney region varies from year-to-
year and reflects the overall impacts of synoptic patterns and physiographic parameters. In
general, the probability of receiving heavy precipitation (gamma estimators) from
thunderstorms is greatest in the east of the Sydney region, particularly over the
Metropolitan area, in spring and summer. This rainfall decreases towards the western
suburbs and then increases again over the Blue Mountains. The decrease is most marked
over the Southern Tableland.
2) The proximity to the Tasman Sea is the most important physiographic parameters
controlling the spatial patterns in rainfall from thunderstorms. Generally, areas near the
coast play a major part in the 'turning-on' of thunderstorms producing heavy rainfalls.
3) Spatial rainfall variations are associated with thermodynamic and/or kinematic
characteristics of the landuse patterns. Intense urbanisation results in the heaviest rainfalls.
4) Thunderstorm rainfall intensity is also dependent on aspect and elevation.
Generally areas of higher relief receive more rainfall.
The information obtained here can be used in many areas such as urban planning, design of
rain-gauge network, flash flood control programs and emergency response management.
Hopefully the study will spur other research into the identification and explanation of
thunderstorm rainfall patterns along the east coast of Australia.
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A List of Computer Programs 210.
APPENDIX A
List of Computer Programs
A.l Computer Program Number 1
To find the common thunderstorm-days in the Sydney region the Computer Program
N u m b e r 1 was used (see Chapter 4). This program was written using a C1"4" programming
language.
#include <stdio.h> long lines (FILE *file); main()
long int thun[1000][12]; int nil, nl2, nl3, nl4, nl5, n!6, nl7, nl8; int nl9, nllO, nlll, nll2, il, i2, i3, i4, i5, i6, i7, i8, i9; int ilO, ill, il2, i, j, k, m, n; long int xl, x2, x3, x4, x5, x6, x7, x8, x9, xlO, xll, xl2; FILE *fpl, *fp2, *fp3, *fp4, *fp5, *fp6, *fp7, *fp8, *fp9; FILE*fplO, *fpll, *fpl2; fpl =fopen("a:thul.txt", V ) ; fp2 = fopen("a:thu2.txt", "r"); fp3 =fopen(,,a:thu3.txt", "r"); fp4 = fopen("a:thu4.txt", V ) ; fp5 = fopen("a:thu5.txt", "r"); fp6 = fopen("a:thu6.txt", "r"); fp7 = fopen("a:thu7.txt", "r"); Q)8 = fopen("a:thu8.txt", "r"); fp9 = fopen("a:thu9.txt", "r"); fplO= fopen("a:thul0.txt", "r"); fpll= fopen("a:thull.txt", "r"); fpl2= fopen("a:thul2.txt", "r"); nll = lines(Q)l); n!2 = lines(fi)2); nl3 = lines(Q)3); nl4 = lines(^)4); nl5 = lines(fp5); nl6 = lines(lp6); nl7 = lines(fp7); nl8 = lines(lp8); nl9 = lines(fp9); nllO= lines(fpl0); nlll=lines(fpll); nll2= lines(fpl2); rewind(fpl) rewind(jfp2) rewind(fp3) rewind(Q)4) rewind(jQ)5) rewind(fp6) rewind(fp7)
APPENDIX A List of Computer Programs 211
rewind(fp8); rewind(fp9); rewind(fplO); rewind(fpll); rewind(fpl2); k=0; for(il=0; iKnll; ++il)
{ fscanf(fpl,"%ld\n",&xl); thun[il][0]=xl;
} for (il=nll; iKIOOO; ++il)
wun[il][0]=0; for(i2=0; i2<nl2; ++i2)
{ fscanf(fp2,"%ld\n",&x2); thun[i2][l]=x2;
} for (i2=nl2; i2<1000; ++i2)
thun[i2][l]=0; for(i3=0; i3<nl3; ++i3)
{ fscanf(fp3,"%ld\n",&x3); thun[i3][2]=x3;
} for (i3=nl3; i3<1000; ++i3)
thun[i3][2]=0; for(i4=0; i4<nl4; ++i4)
{ fscanf(fp4,"%ld\n",&x4); thun[i4][3]=x4;
} for (i4=nl4; i4<1000; ++i4)
thun[i4][3]=0; for(i5=0; i5<nl5; ++i5)
{ fscanf(fp5,"%ld\n",&x5); thun[i5][4]=x5;
} for (i5=nl5; i5<1000; ++i5)
thun[i5][4]=0; for(i6=0; 16<nl6; ++i6)
{ fscanf(fp6,"%ld\n",&x6); thun[i6][5]=x6;
} for (i6=nl6; i6<1000; ++i6)
thun[i6][5]=0; for(i7=0; i7<nl7; ++i7)
{ fscanf(fp7,"%ld\n",&x7); thun[i71[6]=x7;
} for(i7=nl7;i7<1000;++i7)
thun[i7][6]=0; for(i8=0; i8<nl8; ++i8)
I
APPENDIX A List of Computer Programs 212.
fscanf(fp8,',%ld\n",&x8); thun[i8][7]=x8;
} for (i8=nl8; i8<1000; ++i8)
thun[i8][7]=0; for(i9=0; i9<nl9; ++i9)
{ fscanf(fp9,"%ld\n",&x9); thun[i9][8]=x9;
} for (i9=nl9; i9<1000; ++i9)
thun[i9][8]=0; for(ilO=0; il0<nll0; -H-ilO)
{ fscanf(fplO,"%ld\n",&xlO); thun[il0][9]=xl0;
} for (ilO=nllO; il0<1000; ++ilO)
thun[il0][9]=0; for(ill=0;ill<nlll;++ill)
{ fscanf(fpl l,"%ld\n",&xl 1); thun[ill][10]=xll;
> for (ill=nlll; ilKlOOO; ++ill)
thun[ill][10]=0; for(il2=0; il2<nll2; ++il2)
{ fscanf(fpl2,"%ld\n",&xl2); thun[il2][ll]=xl2;
} for (il2=nll2; il2<1000; ++il2)
thun[il2][ll]=0; for(i=0; i<12; ++i) for(n=l; n<12; ++n)
{ for(j=0;j<1000;++j) for(m=0; m<1000; ++m)
{ if(thun[j][i] != 0 & & thun[j][i]==thun[m][n]) k=k+l; if(thun[j] [i]<thun[m] [n]) m=1000;
} printf("k%ld,%ld=%d\n",thun[0] [i] ,thun[0] [n],k); k=0;
} return 0;
/* */
long lines (FILE *file) long nl, c; nl = 0; while ((c = getc(file)) != EOF) if(c = V )
++nl; return nl;
DATA EXTRACTION
A.2 Computer Program Number 2 (C1^)
Due to the large volume of thunderstorm rainfall data recorded on a daily basis, it was
very difficult and time consuming to extract the specified data sets. This was due to the data-set
files from more than 350 rainfall stations located in the Sydney region operated by the Bureau of
Meteorology during the last 34 years (1960 to 1993). There were also more than 100 stations
which have been covered by the Sydney Water. These data sets were in different formats, so it
was impossible to extract the data manually, and the possibility of errors were very high. To
overcome these difficulties three computer programs were developed, each for a specific format,
using three different computer programming languages as follows:
This computer program was written to extract the daily rainfall events from the Sydney
Water data sets. Each rainfall station data was in ASCI format.
#include <stdio.h> #include <stdlib.h> #include <math.h> int lines (FILE *file); main(int argc, char *argv[]) {
FILE *fpl, *fp2, *lp3; char c[500], cc; intij, k, kk, nil, nl2;
float x, y, z, ml, m2, m3, m4, m5; if((fpl = fopen(*++argv, "r"))==NULL)
{ printf("File \"%s\" does not exist.\n", *argv); printf("The usage is: Y'Alil inputfile outputfile\"\n"); return 1;
} else if
((fp3 = fopen(*++argv, "w")) == NULL) {
printf("File \"%s\" does not exist.\n", *argv); printffThe usage is: V'Alil inputfile outputfileVW); return 1;
} fp2 = fopen("match.txt", "r"); nil = lines (fpl); nl2 = lines (fp2); rewind (fpl); rewind (fp2); prinrf("nll=%d nl2=%d\n", nll,nl2); for (k=0; k<nl2; k++) {
fscanf (fp2, "%f % f %f\n", &x, &y, &z); /•printf ("%f % f %nn", x, y, z);*/ = = ^ = = = _
for (kk=0; kk<nll; kk++) " {
fscanf (fpl, "%f %f %f %f %f\n", &ml, &m2, &m3, &m4, &m5); /*printf ("%f %f %f %f %f\n", ml, m2, m3, m4, m5);*/ if (m3==z & & m 2 = y & & ml==x) { fprintf (fp3, "%2.0f%2.0f %2.0f %. lf\t %.0f\nM, ml, m2, m3, m4, m5); printf ("%2.0f %2.0f %2.0f %.lf\t%.0f\n", ml, m2, m3, m4, m5); kk = nll;
} > rewind (fpl);
} rewind (fpl);
} /* */
/* This function counts the number of lines in a file */ int lines (FILE *file)
{ int c, nl;
nl = 0; while ((c = getc(file)) != EOF) if(c = V )
++nl; return nl;
A.3 Computer Program Number 3 (Fortran 77)
This computer program was written to extract the daily rainfall events from the Sydney
Water data sets. Each rainfall station data was not in ASCI format. This program is written in a
standard Fortran which can be used on mainframes or PCs. The program has two input files as
follows:
a file named "MATCH"; and
a file named "RAINFALL".
Every record of the MATCH consists of a date on which the value of rainfall is required.
The R A I N F A L L contains dates and their corresponding values of rainfall for several years. First
the program reads a data from the M A T C H file and then searches that date in the RAINFALL.
After finding the date from the RAINFALL, the value of rainfall which corresponds to this date
is also read and both the date and value of rainfall will be written in the output file called
O U T P U T . This procedure is repeated for all records of the M A T C H file.
dimension iday (400), month (400), isal (400) character*5 A A A open (unit =5, file= 'match.txt' ,status= 'old') open (unit = 6, file = 'match.out', status = 'unknown') open (uint = 7 . file = '566020', status = old')
APPENDIX A List of Computer Programs 211
open (uint = 8 , file = 'final', status = unknown) do 1100 i = 1,383 read (5, *) iday (i), month (i), isal (i) isal (i) = isal (i) + 1900 1100 continue
do 12 i = 1, 383 c print *, 'i = ',i
do 120 m = 1, 10 c print *, 'm= *,m
if(m. eq. 1) then nn=3 do 10 i i = 1, nn read (7,*)
c print *, 'i i- ,i i, m 10 continue
end if read (7,11) year
11 format (127X, i4) print *, *year=', year do 151 k = 1,4 read (7,*)
151 continue if (isal (i). eq. year) then do 121 j= 1,31
124 read (7, 122) irooz 122 format (9x, i3)
if (irooz.eq.iday (i)) go to 123 go to 124
123 if (month (i) .eq. 1) then backspace (unit = 7) read (7, 13) A A A
13 format (18x, A5) write (8, *) iday (i), month (i), isal (i),' \ A AA go to 1020 end if if (month (i). eq. 2) then backspace (unit = 7) read (7, 14) A A A
14 format (27x, A5) write (8, *) iday (i), month (i), isal (i),' ', A A A go to 1020 end if if (month (i). eq. 3) then backspace (unit = 7) read (7, 15) A A A
15 format (36x, A5) write (8, *) iday (i) month (i), isal (i),' ', A A A go to 1020 end if if (month (i) .eq. 10) then backspace (unit = 7) read (7, 16) A A A
16 format (lOOx, A5) write (8, *) iday (i), month (i), isal (i),' ', A A A go to 1020 end if if(month(i).eq. 11) then
APPENDIX A List of Computer Programs 216.
backspace (unit = 7) read (7, 17) A A A
17 format (108x,A5) write (8, *) iday (i), month (i), isal (i),' ' A A A go to 1020 end if if (month (i) .eq. 12) then backspace (unit = 7) read (7, 18) A A A
18 format (117X,A5) write (8, *) iday (i), month (i), isal (i),' ';AAA go to 1020 end if
121 continue else dol25j = l, 58 read (7, *)
125 continue end if
120 continue 1020 rewind (unit = 7) 12 continue
stop end
A.4 Computer Program Number 4 (Quick Basic)
This computer program was written to extract the daily rainfall events from the Sydney
Bureau of Meteorology data sets which are in a long ASCI format, in each rainfall station.
CLS 100 REM 110 REM ** initialisation **** 120 DIM nm(12), P(12, 31), PM(12), PMAX(12), NOP(12), dy(500), mon(500), year(500) 130 Y$ = "#######.#": X$ = " ##": V$ = "####.#": W$ = " ##" 140 Z$ =" M ## #### ###.#" 150 DATA 31,28,31 FOR i = 1 TO 3 READ nm(i) NEXTi DATA 31,30,31 FOR i = 10 TO 12 READ nm(i) NEXTi INPUT "ENTER INPUT MATCH FILE NAME:"; 12$ OPEN 12$ FOR INPUT AS #3 WHILE NOT EOF(3) K = K+1 nmatch = nmatch + 1 INPUT #3, daym, monm, yearm dy(K) = daym mon(K) = monm year(K) = yearm + 1900
APPENDIX A List of Computer Programs 211
' PRINT dy(k), mon(k), year(k) WEND 190 INPUT "ENTER INPUT FILE NAME:"; i$ 200 OPEN i$ FOR INPUT AS #1 INPUT "ENTER OUTPUT FILE NAME:"; 0$ OPEN 0$ FOR OUTPUT AS #2 otyp= 1 330 WHILE NOT EOF(l) 340 INPUT #1,A$ 365 SW=1 370 year = 1000 + VAL(MID$(A$, 1, 6)) 380 IF year MOD 4 = 0 THEN nm(2) = 29 ELSE nm(2) = 28 390 mon = VAL(MID$(A$, 7, 6)) 440 MAXP = -1 450 FOR i = 1 TO nm(mon) '460 P$ = MID$(A$, 13 +1 * 8, 8) P$ = MID$(A$, 5 + i * 8, 8)
470 IFP$ = " " THEN P(mon, i) =-1 ELSE P(mon, i) = VAL(P$) •480 PCD$ = MLD$(A$, 24 +1 * 6, 1) PRINT
490 IF P(mon, i) > MAXP THEN MAXP = P(mon, i) 500 NEXTi 510 PMAX(mon) = MAXP 520 IF otyp = 1 THEN GOSUB 770 ' printing the results in a file 560 WEND 570 CLOSE 580 END 590 REM ** SETTING UNAVAILABLE DATA ** 600 FOR i = mon+1 TO 12 610 PM(i) = -l:NOP(i) = -l:PMAX(i) = -l 620 FORJ=lT0 31 630 P(i, J) = -1 640 NEXT J 650 NEXTi •670 RETURN 770 REM ** PRINTING OUTPUT FILE *** 780 FOR i = 1 TO nm(mon) FOR c = 1 TO nmatch
IF year(c) o year THEN GOTO 999 IF mon(c) o mon THEN GOTO 999 IF dy(c) = i THEN PRINT #2, USING Z$; i; mon; year; P(mon, i) 'c = nmatch PRINT" year = "; year ELSE ENDIF
999 NEXT c 666 PRINT 800 NEXTi PRINT " it is working "; c 810 RETURN
A.5 Computer Program Number 5
This computer program was written in the C1"4" computer programming language
environment. The program calculates the horizontal distance between a rainfall station and the
average coastline in the study area. To run the program, three files should be entered after the
name of the program. One file contains the data of the stations; a second file for coastline data,
and a third file for the output.
#include "include.h" #include "function, h" #include "func.c" main(int argc, char *argv[])
{ int nil, nl2, i, ii; char sn[50]; float x, y, xl, yl, x2, y2, x3, y3, x4, y4, gl, g2, dist; float dxl, dyl, dx2, dy2, m, ml, m2, h; FILE *fl, *f2, *f3; printf ("\n"); printf ("\n"); printf ("This program calculate the distance between the horizontal^"); printf ("line from a station and the coastal line.\n"); printf ("\n"); printf ("\n"); printf ("To run the program three files should be entered after the \n"); printf ("name of the program. File contains the data of the stations,\n"); printf ("file contains the coastline data, and output file.\n"); printf ("\n"); printf ("\n"); if ((fl = fopen (*++argv, "r")) = NULL)
{ printf ("Program can't open file %s as input.\n", *argv); exit (0);
} if ((f2 = fopen (*++argv, "r")) == NULL) {
printf ("Program can't open file %s as input.\n", *argv); exit (0);
} if ((f3 = fopen (*++argv, "w")) = NULL) {
printf ("Program can't open file %s as output.\n", *argv); exit (0);
} printf ("argc = %d\n", argc); printf ("\n"); printf ("\n"); nil = lines (fl); nl2 = lines (f2); rewind (fl); rewind (f2); for (i=0; i<nll; i++)
{
= fscanf (fl, "%s %f %f %f % f %f\n", sn, &xl, &yl, &gl, &g2, &h);
APPENDIX A List of Computer Programs 211
x2 = xl+100; y2 = yi; fscanf (12, "%f %f\n", &x3, &y3); for (ii=0; ii<nl2-l; ii++)
{ fscanf (f2, "%f %f\n", &x4, &y4); dxl =x2-xl;
1 dyl=y2-yl; dx2 = x4-x3; dy2 = y4-y3; m = dxl*dy2-dyl*dx2; if(m!=0)
{ ml m2 x = y =
= xl*dyl-yl*dxl; = x3*dy2-y3*dx2; (dxl*m2-dx2*ml)/m; (dyl*m2-dy2*ml)/m;
if ((x>=x3 & & x<=x4 & & y>=y3 & & y<=y4 ||
{
line is %.3fm\n", sn, dist);
coastal line is %.3fm\n", sn, dist);
} else {
} }
} rewind (f2); } fclose (fl); fclose (f2); fclose (0); return (0);
}
x<=x3 & & x>=x4 & & y>=y3 & & y<=y4 || x>=x3 & & x<=x4 & & y<=y3 & & y>=y4 || x<=x3 & & x>=x4 & & y<=y3 & & y>=y4) & & y<=yl)
dist = distance (xl, yl, x, y); printf ("The distance between Station %s and the coastal
fprintf (f3, "The distance between Station %s and the
ii = nl2-l; rewind (f2);
x3 =x4; y3=y4;
**********************************
rstorm
APPENDIX B
Thunderstorm Rainfall Data
B.l Common thunderstorm-days in the Sydney region between 12 thunder-recording
stations
* The numbers in the table are representative of the thunder-recording stations.
1 = Richmond
2 = Katoomba
3 = Parramatta
4 = Prospect D a m
5 = Sydney Regional Office
6 = Liverpool
7 = Bankstown
8 = Sydney Airport
9 = Lucas Heights
10 = Camden Airport
11 = Wollongong University
12 = Bowral
Date is represented as Year, Month and Day.
Thunderstorm Rainfall Data
Table 4.2 C o m m o n thunderstorm-days in Sydney region.
Row
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
Date
19600103
19600129
19600209
19600214
19600307
19600314
19600322
19600402
19600420
19600421
19600518
19600519
19600520
19600916
19600917
19600924
19601004
19601024
19601025
19601026
19601030
19601031
19601103
19601118
19601121
19601124
19601126
19601203
19601212
19601214
19601215
19601216
19610101
19610112
19610113
19610131
19610205
19610206
19610207
19610208
19610209
19610223
19610228
19610315
19610405
19610413
19610503
19610604
19610608
19610821
19610823
19610824
19610826
19610829
19610905
19611011
19611023
19611101
19611102
19611103
19611104
19611107
19611108
19611114
19611115
19611120
19611121
19611123
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
I
1
1
1
1
1
1
1
1
1
1
1
1
1
5*
5
5
5
5
5
5
5
5
5
5
5
5
5
5
8
8
8
5
5
8
5
5
5
5
5
8
8
5
5
5
5
5
5
8
5
5
5
8
8
8
8
8
8
8
5
5
5
8
5
5
5
5
5
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
! 1
!
1
i
i i
Row
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
Date
19731010
19731011
19731101
19731102
19731104
19731111
19731120
19731121
19731126
19731129
19731206
19731215
19731227
19731228
19740213
19740214
19740218
19740219
19740323
19740328
19740411
19740425
19740601
19740616
19740901
19740902
19740924
19740930
19741017
19741022
19741023
19741028
19741029
19741030
19741031
19741101
19741102
19741126
19741207
19741217
19750102
19750103
19750104
19750108
19750109
19750110
19750117
19750125
19750126
19750227
19750228
19750310
19750311
19750313
19750315
19750328
19750329
19750330
19750414
19750415
19750416
19750419
19750501
19750621
19750622
19750623
19750810
19750923
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
9
5
3
7
8
7
8
5
4
5
5
3
10
5
9
4
5
7
5
5
3
9
5
10
5
5
4
9
4
11
10
8
5
4
5
5
9
9
2
8
10
5
5
5
4
10
5
7
2
7
5
10
2
9
9
7
8
8
9
7
8
7
7
8
5
8
7
4
8
8
9
5
10
7
8
7
8
7
10
10
10
8
7
5
8
9
4
9
7
12
5
8
8
8
11
7
8
6
10
7
8
10
8
12
9
8
6
12
10
5
10
9
9
10
12
8
9
7
11
8
9
10
7
8
8
11
10
10
8
9
10
9
12
Row 1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
Date
19850402
19850403
19850404
19850406
19850407
19850408
19850409
19850425
19850426
19850501
19850506
19850507
19850520
19850606
19850620
19850707
19850901
19850911
19850912
19850913
19850925
19851007
19851011
19851012
19851016
19851017
19851018
19851019
19851023
19851106
19851107
19851108
19851109
19851115
1985112S
19851126
19851127
19851128
19851130
19851201
19851208
19851209
19851210
19851211
19851214
19851216
19851217
19851223
19851224
19860104
19860105
19860108
19860115
19860116
19860117
19860122
19860130
19860131
19860204
19860205
19860206
19860209
19860222
19860223
19860225
19860309
19860318
19860326
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
3
4
12
8
5
7
4
4
5
7
5
5
5
5
10
2
4
5
4
12
8
3
5
12
12
5
8
7
5
7
8
5
5
7
12
12
8
3
5
7
5
7
5
7
5
7
5
7
8
7
5
7
2
5
7
8
5
7
2
5
2
5
5
10
7
8
11
7
8
8
8
11
3
11
7
11
12
7
7
7
8
7
12
8
7
10
10
6
8
8
8
7
8
10
8
8
11
8
11
5
7
8
8
8
6
12
8
4
8
12
8
8
10
8
12
8
7
10
10
8
10
10
10
11
8
8
7
11
8
12
10
11
10
10
8
10
12
12
10
10
8
10
12
12
10
12
11 12
APPENDIX R Thunderstorm Rainfall Data
Table 4.2 cont....
Row
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
Date
19611124
19611125
19611130
19611201
19611215
19611217
19611218
19611231
19620104
19620109
19620110
19620111
19620118
19620129
19620131
19620205
19620208
19620209
19620214
19620215
19620220
19620302
19620308
19620318
19620319
19620320
19620429
19620522
19620806
19620915
19620927
19621104
19621111
19621128
19621203
19621204
19621207
19621208
19621214
19621218
19621219
19630102
19630103
19630127
19630129
19630201
19630204
19630218
19630310
19630314
19630409
19630420
19630421
19630424
19630517
19630603
19630604
19630625
19630626
19630710
19630713
19630814
19630822
19630823
19630825
19630829
19630916
19630923
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5
5
5
8
8
5
5
5
5
9
8
5
5
8
9
5
8
8
5
8
5
5
5
5
5
5
5
5
5
5
5
5
8
5
6
8
6
5
8
5
8
5
5
9
5
5
5
5
5
5
5
5
5
5
8
5
8
8
8
9
8
8
8
8
8
8
8
8
6
8
8
8
8
8
8
6
8
8
8
9
i
I ! ! ! I
i
1 i 1 !
i i
I
|
Row
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
Date
19750928
19750930
19751002
19751010
19751011
19751020
19751024
19751031
19751104
19751109
19751116
19751123
19751124
19760109
19760205
19760206
19760219
19760220
19760225
19760226
19760228
19760327
19760328
19760405
19760418
19760615
19760617
19760701
19760813
19760823
19760827
19760828
19760917
19760918
19760920
19760921
19760922
19761001
19761006
19761007
19761014
19761015
19761017
19761018
19761030
19761102
19761103
19761104
19761110
19761111
19761112
19761114
19761115
19761117
19761118
19761120
19761121
19761122
19761123
19761203
19761210
19761215
19761216
19761217
19770101
19770102
19770110
19770113
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
8
5
5
9
4
5
8
4
8
3
8
4
5
2
4
7
2
3
5
4
5
9
3
5
5
5
8
10
10
4
5
2
4
8
5
10
5
7
3
5
3
5
4
5
5
2
2
5
5
8
4
2
5
9
7
5
5
4
5
5
9
5
10
8
8
5
9
7
12
4
3
6
5
8
9
7
7
8
8
3
9
7
7
9
8
4
8
5
8
8
9
3
7
8
9
5
5
7
8
8
8
-7
6
8
12
10
7
10
8
7
4
7
8
9
8
4
10
8
8
5
9
9
11
5
8
6
7
8
12
9
7
8
9
10
5
9
5
9
7
7
11
7
8
9
11
8
10
7
10
8
10
9
8
8
9
9
8
10
10
9 12
10
9
11
11
10
Row
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
Date
19860327
19860412
19860413
19860417
19860418
19860430
19860804
19860805
19860925
19861001
19861003
19861004
19861019
19861028
19861111
19861117
19861118
19861119
19861126
19861127
19861205
19861215
19861216
19861231
19870101
19870103
19870104
19870202
19870210
19870211
19870215
19870221
19870319
19870320
19870326
19870327
19870328
19870329
19870409
19870514
19870515
19870722
19870723
19870728
19870810
19870920
19871016
19871017
19871019
19871023
19871024
19871025
19871108
19871109
19871110
19871111
19871115
19871116
19871117
19871118
19871120
19871201
19871205
19871210
19871211
19871216
19871220
19871221
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
4
5
4
10
4
8
4
4
5
5
3
8
10
5
5
7
5
5
12
7
12
10
10
5
2
7
10
5
2
5
2
8
5
7
8
7
5
7
5
10
5
2
2
5
5
5
2
2
2
6
12
2
6
2
5
8
12
5
8
2 2
11
7
10
7
7
7
7
8
7
8
7
8
8
5
11
7
7
11
7
8
7
7
10
8
6
7
8
6
7
5
7
7
8
10
12 7
1 l\
8
8
8
8
8
8
8
8
10
10
8
8
8
8
8
12
8
8
7
12
8
8
10
10
12
12
10
10
10
10
10
12
12
12
11
11
11
11
12
12
1
APPENDIX R Thunderstorm Rainfall Data 243
Table 4.2 cont....
Row
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
Date
19630930
19631012
19631025
19631029
19631030
19631031
19631111
19631120
19631122
19631129
19631211
19631213
19631215
19631216
19631217
19631223
19631227
19640124
19640126
19640207
19640209
19640215
19640216
19640223
19640301
19640609
19640610
19640702
19640824
19640827
19640828
19640829
19641008
19641023
19641029
19641103
19641109
19641119
19641209
19641223
19641226
19641229
19650111
19650124
19650130
19650216
19650217
19650218
19650219
19650222
19650410
19650622
19650623
19650718
19650802
19650909
19650913
19651024
19651025
19651027
19651103
19651124
19651202
19651203
19651214
19651230
19651231
19660116
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5
5
5
5
5
9
6
8
5
5
8
5
5
8
8
5
5
8
8
5
5
5
5
5
5
5
5
5
5
5
6
5
8
5
5
5
5
5
5
5
6
5
5
5
5
5
5
5
8
5
5
5
5
9
9
5
5
8
5
6
6
6
6
6
6
8
6
8
8
8
8
8
8
8
8
8
8
6
8
8
8
8
8
8
8
8
8
8
6
8
8
8
8
8
8
8
8
8
8
8
8
9
8
9
9
8
9
9
9
9
9
9
|
! 1
I
! 1 1
1 1
!
1
1
! 1
|
Row
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
72S
729
730
731
732
733
734
Date
19770114
19770115
19770116
19770117
19770121
19770122
19770123
19770124
19770128
19770129
19770202
19770204
19770205
19770206
19770218
19770219
19770223
19770225
19770228
19770304
19770305
19770306
19770320
19770321
19770407
19770528
19770608
19770806
19770812
19770927
19770928
19771019
19771029
19771101
19771104
19771114
19771115
19771117
19771118
19771119
19771120
19771130
19771201
19771202
19771210
19771211
19771214
19771215
19771221
19771225
19771226
19780101
19780102
19780104
19780115
19780116
19780118
19780123
19780124
19780125
19780127
19780211
19780219
19780221
19780222
19780225
19780227
19780228
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
10
9
5
7
5
7
11
12
9
5
12
9
5
9
2
2
2
9
8
5
5
5
5
2
7
5
5
3
3
7
8
2
9
5
2
4
8
11
4
5
5
8
5
7
12
8
4
5
5
5
2
5
7
2
3
8
4
3
5
4
10
8
9
8
10
11
7
8
11
12
12
5
7
6
8
7
3
8
7
4
9
3
8
3
10
9
9
8
12
8
7
7
5
8
3
5
10
9
8
7
12
8
8
8
7
8
4
12
5
4
5
12
8
8
10
5
8
—r
9
9
8
9
5
8
5
8
9
9
11
8
11
12
11
10
6
10
7
10
11
11
12
11
7
11
8
11
12
8
11
9 10 11 12
Row
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
Date
19871228
19880101
19880102
19880108
19880109
19880120
19880121
19880122
19880123
19880124
19880131
19880207
19880213
19880214
19880228
19880229
19880301
19880304
19880324
19880325
19880429
19880430
19880521
19880527
19880528
19880615
19880827
19880828
19880917
19880919
19880920
19880927
19880928
19881103
19881110
19881117
19881120
19881121
19881122
19881123
19881124
19881125
19881126
19881127
19881203
19881209
19881210
19881211
19881216
19881220
19881222
19881223
19881226
19890102
19890104
19890105
19890106
19890110
19890117
19890118
19890119
19890207
19890219
19890220
19890226
19890305
19890309
19890310
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
10
2
2
2
2
2
2
2
2
10
2
2
7
6
10
10
2
2
2
10
5
2
8
10
5
5
5
10
5
5
6
5
5
5
2
2
5
5
3
5
7
2
2
2
2
2
2
2
2
6
10
2
2
5
2
8
2
3
2
2
2
6
2
5
3
5
7
2
7
3
7
5
11
10
6
5
8
6
7
8
7
6
6
5
3
6
7
8
8
3
5
3
5
7
6
3
7
5
5
7
8
6
8
10
5
10
5
10
6
7
7
7
8
7
7
8
5
7
8
10
5
8
5
6
10
5
7
7
8
6
7
10
8
8
10
8
8
6
8
6
6
7
7
8
7
8
10
11
10
7
7
7
8
8
8
10
8
8
8
10
10
10
11
10
11
10
11
11
11
11
APPENDIX R Thunderstorm Rainfall Data 244
Table 4.2 cont....
Row
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
Date
19660117
19660128
19660129
19660130
19660204
19660205
19660214
19660215
19660216
19660309
19660310
19660311
19660318
19660321
19660322
19660323
19660324
19660414
19660415
19660519
19660521
19660610
19660817
19660831
19660916
19660921
19661002
19661003
19661006
19661016
19661017
19661018
19661019
19661020
19661026
19661027
19661109
19661110
19661111
19661121
19661123
19661124
19661205
19661214
19661222
19661223
19661226
19661229
19661230
19661231
19670102
19670111
19670116
19670129
19670208
19670213
19670225
19670226
19670306
19670504
19670505
19670806
19670817
19670905
19671011
19671013
19671018
19671028
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
4
5
8
4
4
4
4
5
5
4
5
5
8
4
5
4
12
9
4
5
5
5
4
12
4
5
5
9
8
9
8
9
5
9
8
5
5
5
8
5
9
9
5
5
5
9
12
12
5
5
5
5
5
5
8
5
8
8
8
5
8
8
9
12
9
8
5
5
8
8
5
5
8
6
9
8
8
6
9
12
6
6
8
8
8
8
8
8
9
8
8
12
8
9
8
8
8
8
8
8
9
9
9
1
j 1
1 i i i
1
12l
i
i i
;'
i
'! | : ' 1 !
8 9
| ! ' i
9
!
;
1
—r Row
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
Date
19780303
19780308
19780313
19780321
19780322
19780323
19780327
19780401
19780410
19780518
19780602
19780831
19780912
19780918
19780919
19780922
19781004
19781006
19781018
19781031
19781101
19781107
19781111
19781113
19781117
19781126
19781128
19781129
19781130
19781203
19781212
19781213
19781214
19781215
19781217
19781218
19781222
19781225
19790102
19790103
19790107
19790108
19790211
19790226
19790302
19790303
19790314
19790315
19790321
19790324
19790402
19790403
19790415
19790416
19790418
19790510
19790719
19790720
19790726
19790819
19790911
19790919
19790920
19791004
19791006
19791011
19791016
19791022
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5
12
2
11
10
4
9
12
11
5
8
5
5
2
5
8
5
9
7
2
8
2
8
8
9
5
2
4
5
7
5
4
12
5
4
8
5
5
4
5
5
5
12
11
5
12
9
5
5
4
8
8
5
5
11
10
5
5
5
7
17
5
7
8
5
8
8
9
7
12
8
5
9
9
8
9
8
7
8
5
7
8
8
7
8
8
9
10
7
8
2]
8
8
8
8
10
8
8
10
10
10
9
7
8
8
10
8
9
11
11
10
9
10
11
9
9
11
12
12
10 11
Row
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
Date
19890311
19890312
19890313
19890331
19890406
19890412
19890413
19890421
19890422
19890423
19890426
19890504
19890505
19890506
19890507
19890623
19890624
19890816
19890817
19890820
19890823
19890826
19890926
19891004
19891023
19891025
19891105
19891106
19891107
19891112
19891116
19891117
19891118
19891202
19891203
19891205
19891209
19891210
19891211
19891212
19891214
19891219
19891220
19891221
19900101
19900102
19900106
19900107
19900108
19900112
19900113
19900114
19900119
19900120
19900121
19900205
19900206
19900207
19900208
19900209
19900210
19900211
19900217
19900218
19900222
19900223
19900224
19900225
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
7
7
2
5
6
5
5
7
11
11
4
8
5
5
2
5
3
2
2
2
6
2
3
2
3
5
3
2
2
6
2
2
3
3
2
3
6
5
6
2
2
2
2
2
3
2
3
2
2
2
2
2
3
2
3
2
6
3
11
7
8
8
7
8
6
7
7
5
11
5
5
8
6
3
7
5
5
6
5
5
8
7
3
3
3
5
6
5
6
3
3
3
3
6
5
6
7
5
8
8
8
7
7
7
5
11
8
7
8
11
5
5
8
8
7
5
7
5
5
7
8
7
7
11
8
8
7
6
6
7
6
6
11
11
8
7
7
8
7
7
11
8
8
8
8
11
—
APPENDIX R Thunderstorm Rainfall Dat>
Table 4.2 cont....
Row
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
Date
19671029
19671104
19671105
19671108
19671119
19671120
19671130
19671214
19671215
19671229
19680104
19680117
19680122
19680206
19680207
19680304
19680312
19680316
19680318
19680319
19680320
19680323
19680324
19680325
19680412
19680415
19680416
19680612
19680722
19680815
19680820
19680913
19680916
19681019
19681104
19681110
19681111
19681120
19681209
19681210
19681225
19681226
19681227
19681228
19690102
19690103
19690114
19690122
19690205
19690206
19690207
19690223
19690224
19690225
19690314
19690319
19690327
19690328
19690329
19690330
19690331
19690401
19690415
19690501
19690515
19690609
19690610
19690715
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
9
5
9
5
8
9
8
9
5
3
5
3
5
5
5
8
5
5
5
8
8
8
5
8
5
5
4
3
3
5
3
5
3
4
5
6
4
8
4
5
9
5
8
9
12
5
9
5
5
5
8
8
8
6
8
8
8
8
8
8
8
8
8
9
8
4
4
8
8
5
6
8
8
8
8
8
8
9
9
9
5
5
8
7
9
9
12
I 8
6 8
i 9l
— Row
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
Date
19791023
19791024
19791028
19791104
19791105
19791106
19791111
19791112
19791115
19791116
19791119
19791120
19791123
19791124
19791126
19791204
19791205
19791206
19791208
19791221
19791231
19800106
19800110
19800111
19800112
19800113
19800131
19800201
19800202
19800205
19800206
19800301
19800416
19800417
19800430
19800501
19800528
19800529
19800609
19800610
19800824
19800826
19800918
19801002
19801012
19801013
19801018
19801019
19801020
19801021
19801028
19801107
19801109
19801110
19801203
19801204
19801216
19801229
19801230
19810103
19810106
19810107
19810112
19810121
19810122
19810125
19810126
19810128
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5
9
8
2
7
5
3
5
4
9
5
5
4
5
8
5
7
5
8
2
3
9
5
5
2
4
12
7
2
2
5
8
5
8
5
7
10
8
2
3
4
2
2
2
7
7
7
7
12
5
3
10
2
2
7
7
2
4
2
2
12
8
10
10
5
8
4
8
7
7
8
5
6
8
8
9
4
7
8
5 7
8
8
7
5
3
8
8
5
12
4
7
8
9
12
11
8
10
7
9
9
6
7
10
9
5
8
6
9
9
8
8
4
6
5
10
10
10
10
11
8
10
10
7
8
10
6
8
11
11
9
9
5
7
8
11
12
11
8
9
8
10
12
8
8
10
9
10
9
11
9
10
12
10
11
10
12
10
11
12
Row
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
Date
19900305
19900306
19900307
19900316
19900318
19900319
19900403
19900413
19900416
19900701
19900719
19900724
19900725
19900801
19900802
19900815
19900901
19900913
19900914
19900915
19901002
19901011
19901012
19901015
19901019
19901021
19901101
19901103
19901104
19901109
19901110
1990U15
19901116
19901129
19901130
19901201
19901203
19901204
19901208
19901209
19901210
19901211
19901220
19901221
19901227
19901231
19910101
19910109
19910110
19910U1
19910112
19910113
19910115
19910U6
19910118
19910119
19910120
19910121
19910122
19910125
19910126
19910127
19910205
19910206
19910207
19910215
19910216
19910222
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
3
5
3
2
6
8
11
8
11
11
2
11
2
6
5
3
4
7
2
7
2
5
5
4
2
4
2
2
2
2
2
5
2
3
5
4
7
12
2
2
2
2
2
2
3
2
3
2
7
2
2
3
12
2
3
2
2
3
2
2
2
5
5
6
6
5
7
3
8
8
5
5
8
7
5
7
5
3
4
3
4
8
3
7
12
10
12
5
3
4
8
10
3
3
4
3
4
8
3
4
5
5
12
8
6
7
7
8
4
7
12
8
12
11
8
4
5
7
5
5
8
4
11
10
5
4
6
4
12
4
6
8
7
11
7
8
5
8
11
12
8
8
8
10
7
12
12
7
5
7
5
8
8
12
8
8
11
7
12
12
12
12
10
8
7
10
7
11
11
11
8
12
11
10
8
8
12
11
12
11
10
11
12
11
12
12
APPENDIX R Thunderstorm Rainfall Data 246
Table 4.2 cont...
Row
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
Date
19690721
19690901
19690908
19690918
19690922
19690929
19690930
19691004
19691015
19691021
19691022
19691026
19691029
19691030
19691101
19691106
19691108
19691110
19691114
19691118
19691119
19691128
19691211
19691212
19691222
19691230
19700101
19700102
19700104
19700110
19700111
19700118
19700119
19700121
19700125
19700126
19700210
19700212
19700215
19700216
19700226
19700227
19700228
19700306
19700307
19700316
19700318
19700319
19700320
19700425
19700528
19700603
19700621
19700802
19700831
19700901
19700902
19700909
19700923
19700928
19701019
19701024
19701106
19701108
19701111
19701112
19701115
19701123
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5
8
7
3
5
3
3
5
5
5
4
5
5
4
6
5
5
4
5
3
8
5
9
5
5
5
3
5
4
5
4
7
5
5
5
5
12
12
5
8
5
5
5
8
7
5
7
5
5
5
4
5
9
3
5
7
4
7
8
9
8
7
5
8
5
7
8
7
4
5
5
8
8
8
8
8
8
8
8
8
5
8
8
8
8
8
8
6
12
7
8
8
5
6
8
9
8
9
7
8
9
7
7
12
1
8
12
8
8
12
12
12
i 1
j
i !
i
I
i 1 1
Row
871
I 872 873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
Date
19810205
19810206
19810210
19810211
19810212
19810219
19810302
19810303
19810310
19810405
19810406
19810407
19810424
19810504
19810827
19811011
19811012
19811015
19811021
19811029
19811030
19811104
19811105
19811113
19811114
19811115
19811116
19811121
19811128
19811205
19811212
19811213
19811219
19811220
19811223
19811224
19811225
19811226
19811229
19811230
19820101
19820102
19820107
19820113
19820117
19820124
19820130
19820131
19820226
19820227
19820321
19820324
19820417
19820425
19820426
19820612
19820709
19820816
19820927
19820930
19821008
19821016
19821203
19821207
19821214
19821215
19821216
19821230
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
4
8
11
5
4
11
4
2
4
8
7
5
2
9
5
5
2
8
7
10
4
5
4
2
5
5
4
4
7
2
5
7
5
8
2
2
8
2
2
10
3
7
12
11
2
8
7
2
5
5
8
3
5
2
5
4
10
5
4
8
8
7
4
9
11
8
12
8
8
9
12
10
8
9
8
8
5
7
9
7
3
12
7
8
5
8
8
11
5
10
8
8
7
10
12
9
5
10
8
8
4
8
6
10
10
10
10
6
8
5
8
11
12
11
7
9
8
9
12
8
12
12
10
10
Row
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
Date
19910223
19910310
19910311
19910312
19910320
19910411
19910412
19910426
19910509
19910515
19910522
19910530
19910609
19910612
19910703
19910823
19910926
19910927
19911005
19911007
19911024
19911025
19911026
19911031
19911116
19911117
19911126
19911127
19911130
19911203
19911204
19911210
19911211
19911215
19911221
19911222
19911224
19911227
19911228
19920101
19920103
19920104
19920105
19920106
19920109
19920110
19920121
19920122
19920123
19920124
19920131
19920201
19920202
19920203
19920204
19920205
19920206
19920211
19920212
19920213
19920222
19920223
19920224
19920303
19920304
19920305
19920322
19920328
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
7
12
4
2
5
8
2
5
2
11
8
12
2
3
2
3
2
5
3
11
2
7
5
2
3
8
2
12
3
2
5
2
5
3
12
2
3
5
7
2
2
12
2
2
3
2
2
2
5
2
3
12
3
2
3
2
2
2
11
11
10
5
10
5
3
8
8
3
4
4
5
10
12
5
8
12
12
3
5
7
8
5
5
5
7
5
12
7
5
4
12
8
3
4
4
3
4
3
12
12
7
7
5
4
7
8
8
12
4
7
8
12
7
8
7
8
12
12
8
7
4
8
5
6
4
8
8
7
5
11
5
8
12
8
12
5
12
8
7
11
10
8
7
12
7
8
11
11
11
8
8
10
12
12
10
12
12
—
Thunderstorm Rainfall Data 247
Table 4.2 cont....
Row
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
Date
19701124
19701201
19701205
19701211
19701212
19701214
19701215
19701218
19701219
19701222
19701223
19701228
19701229
19710103
19710104
19710113
19710115
19710116
19710117
19710118
19710126
19710128
19710210
19710315
19710322
19710323
19710413
19710520
19710521
19710821
19710916
19711031
19711107
19711109
19711113
19711114
19711128
19711129
19711202
19711206
19711214
19711225
19711226
19711227
19720105
19720118
19720122
19720123
19720126
19720127
19720203
19720214
19720217
19720218
19720219
19720220
19720221
19720303
19720306
19720307
19720422
19720605
19720621
19720623
19720826
19720829
19721004
19721016
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
7
12
9
8
9
5
9
8
3
5
4
8
3
5
4
3
3
5
3
5
5
8
3
3
4
3
5
12
3
3
4
4
5
10
5
5
3
5
3
4
5
5
5
5
5
5
5
8
3
3
8
5
4
8
8
5
9
7
4
8
8
4
8
6
4
5
4
8
5
4
5
5
8
8
8
5
8
8
8
8
8
7
7
4
11
5
7
7
7
7
5
8
9
5
6
9
7
9
8
8
11
7
8
12
8
7
9
8
8
10
8
10
9
11
8
9
8
9
9
10
9
9
12
12
Row
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
Date
19821231
19830102
19830103
19830109
19830121
19830122
19830126
19830201
19830202
19830203
19830204
19830207
19830209
19830210
19830211
19830212
19830213
19830222
19830223
19830225
19830226
19830305
19830306
19830314
19830315
19830730
19830903
19830904
19830906
19830908
19830909
19830915
19830929
19830930
19831003
19831004
19831015
19831019
19831020
19831024
19831127
19831130
19831201
19831207
19831208
19831210
19831211
19831212
19831213
19831214
19840108
19840109
19840110
19840113
19840121
19840204
19840205
19840206
19840207
19840208
19840214
19840215
19840216
19840217
19840218
19840221
19840319
19840325
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
10
8
7
3
5
2
2
10
12
7
8
5
7
10
2
2
7
4
2
10
5
4
7
5
7
5
7
2
7
7
2
7
5
8
5
4
5
4
2
4
4
5
4
5
7
7
4
5
4
4
2
4
2
5
4
2
7
10
7
5
7
7
5
8
7
10
7
8
5
8
5
10
3
10
7
10
8
5
8
7
12
8
7
7
8
10
5
8
5
7
5
7
5
8
12
12
10
8
8
10
8
10
12
8
7
8
5
8
7
12
8
11
7
7
11
8
7
12
11
10
12
12
5
12
10
8
12
10
,
7
12
8 10
Row
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
Date
19920426
19920427
19920428
19920429
19920510
19920518
19920626
19920628
19920804
19920824
19920828
19920919
19921015
19921016
19921018
19921020
19921031
19921101
19921102
19921104
19921105
19921106
19921109
19921110
19921111
19921116
19921117
19921119
19921120
19921121
19921122
19921124
19921125
19921129
19921130
19921204
19921205
19921206
19921213
19921214
19921221
19921222
19921223
19921224
19921225
19921226
19930102
19930104
19930106
19930107
19930111
19930115
19930116
19930118
19930119
19930120
19930121
19930122
19930124
19930125
19930202
19930203
19930204
19930205
19930209
19930210
19930212
19930216
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
8
4
10
4
2
5
8
5
5
5
2
5
3
3
8
2
2
2
5
4
3
5
4
8
12
10
2
2
10
2
4
4
12
3
2
2
2
2
2
2
2
2
4
2
2
5
3
2
12
3
4
12
8
4
2
3
2
4
8
4
2
2
5
6
8
8
6
4
4
5
4
8
10
8
5
4
5
3
5
3
8
4
3
4
4
3
8
4
12
12
5
4
12
12
11
12
12
7
7
8
5
8
8
11
12
8
5
6
5
8
12
8
4
8
8
4
11
11
8
12
8
8
6
12
12
6
8
5
12
10
8
11
11
5
12
12
10
8
8
12
8
11
12
12
12
8
12
11
12
12
12
10
12
11 12
APPENDIX R Thunderstorm Rainfall Data 248
Table 4.2 cont....
Row
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
Date
19721020
19721024
19721025
19721026
19721029
19721101
19721104
19721106
19721109
19721110
19721111
19721116
19721125
19721207
19721208
19721215
19721216
19721221
19721222
19730109
19730113
19730125
19730126
19730127
19730129
19730131
19730201
19730202
19730203
19730204
19730205
19730217
19730221
19730222
19730223
19730226
19730227
19730228
19730301
19730304
19730312
19730406
19730407
19730408
19730409
19730430
19730501
19730502
19730710
19730811
19730825
19730912
19730913
19731004
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
11
12
10
4
5
5
5
5
11
3
5
9
5
5
5
10
5
10
12
10
10
5
4
5
10
7
8
7
12
10
9
12
5
10
5
5
11
7
8
4
5
5
5
7
8
8
8
7
7
5
8
7
8
8
5
7
8
8
8
7
8
8
10
10
9
6
8
10
12
11
8
51
8!
101
RI
9
11
11
12
10
7
9
11
8
10
9
1
10
i
i I 1 | i
9 10 11
Row
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
Date
19840530
19840620
19840806
19840810
19840901
19840902
19840904
19840905
19840908
19840909
19840914
19840915
19840921
19840928
19841013
19841014
19841025
19841026
19841028
19841029
19841103
19841104
19841105
19841106
19841107
19841108
19841109
19841111
19841112
19841113
19841115
19841211
19841212
19841215
19841221
19841225
19841226
19841230
19850102
19850103
19850109
19850116
19850117
19850122
19850123
19850128
19850129
19850207
19850208
19850222
19850319
19850320
19850324
19850325
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
I
1
1
1
5
5
4
5
5
4
5
4
2
7
10
4
11
11
8
4
11
2
4
2
7
5
11
8
3
5
5
7
5
11
2
4
7
11
5
4
7
5
7
11
5
5
8
11
3
10
7
3
2
2
5
7
8
7
7
7
8
7
8
5
5
10
7
4
7
5
8
5
7
7
8
7
5
7
8
7
8
8
8
7
5
12
8
11
8
8
7
8
5
8
6
8
8
11
8
8
11
10
6
12
8
8
12
7
11
10
11
7
8
12
8
10 11
Row 1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
Date
19930217
19930218
19930220
19930221
19930226
19930307
19930308
19930309
19930320
19930321
19930324
19930325
19930326
19930327
19930328
19930329
19930405
19930406
19930428
19930429
19930510
19930523
19930804
19930810
19930825
19930826
19930913
19930914
19930919
19930920
19931004
19931018
19931023
19931024
19931025
19931102
19931113
19931114
19931117
19931118
19931119
19931120
19931124
19931204
19931212
19931213
19931214
19931226
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
12
2
2
3
2
5
3
2
2
2
3
12
3
3
5
5
2
3
3
2
3
2
8
2
12
12
12
ll
5
2
8
2
2
2
2
3
12
2
12
2
8
2
4
5
4
3
8
4
12
12
4
4
8
8
8
4
5
5
3
8
5
3
5
8
3
8
5
6
5
5
11
8
5
8
8
5
8
6
8
12
6
8
8
8
11
8
11
6
12
10
11
12
11
12
ll|
8
12
8
11
12
11
8
12
12
APPENDIX B Thunderstorm Rainfall Data 249
B.2 Monthly Thunderstorm Rainfall Data at Richmond
Table 5.1 Monthly thunderstorm rainfall frequency at Richmond station.
Year
1960 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
Total Average
Jan
1 2 5 2 1 0 3 1 2 3 7 2 5 2 0 5 1 5 8 0 4 5 6 1 3 1 3 2 5 2 6 93 2.74
Feb
6 4 2 0 3 1 1 7 73 2.15
Mar
3 1 0 1 0 0 5 0 4 4 2 2 2 2 1 4 1 2 4 3 0 1 1 2 2 0 0 5 1 0 3 56 1.65
Apr
1 1 0 1 0 0 0 0 1 1 0 0 1 1 1 3 0 1 1 1 0 1 1 0 0 5 0 0 1 1 0 23 0.68
May
0 0 1 1 0 0 0 0 0 1 1 2 0 1 0 1 0 0 0 0 2 0 0 0 0 2 0 0 0 0 0 12
0.35
Jun
0 0 0 0 1 1 0 0 0 1 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 9
0.26
Jul
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 2 5
0.15
Aug
0 0 1 2 1 0 2 0 0 0 0 1 2 0 0 1 0 2 0 0 0 0 0 0 0 0 1 1 0 2 2 18
0.53
Sep
0 0 0 3 0 2 0 0 1 3 2 0 0 1 2 3 1 1 2 1 0 0 0 2 4 2 1 0 3 1 3 38 1.12
Oct
1 2 0 3 0 2 3 2 0 6 1 0 4 2 3 2 0 0 2 2 1 4 1 3 3 4 1 4 0 1 0 57 1.68
Nov
1 7 0 1 2 1 1 1 0 6 4 4 6 4 2 1 7 6 3 5 2 2 0 2 7 6 1 3 6 1 4 96 2.82
Dec
3 0 2 2 2 2 5 0 3 3 7 3 4 1 0 0 2 2 4 3 1 3 4 2 2 5 0 2 4 5 5 81 2.38
Total
11 16 12 18 8 10 20 5 13 31 29 15 29 22 10 22 13 25 26 16 12 17 14 18 25 28 7 21 21 15 32 561 16.50
Table 5.2 Monthly thunderstorm rainfall (in m m ) at Richmond station.
Year
1960 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
Total Average
Jan
2.5 9.5 21 33.3
17.3
0.5 12.7
0.3 5.7 7.5 92.6
11.5
60.2
7.8 0
22.2
8.6 44.2
43.6
0 3.7 34.2
58 3.8 9.8 3.6 28.6
4.6 46.8
14.2
40.2
648.5
19.1
Feb
24.6
8.6 47.3
19 2.5 0.5 0.8 17.3
5 5.1 18.3
12.7
19.6
46 1.2 25 18.2
61.2
0.8 0
22.2
7.4 11.6
51.4
36.2
24.5
0 16 7.4 12
79.8
602.2
17.7
Mar
25 5 0 0.3 0 0
12.3
0 52.2
126 19 13 10 5 8
34.2
25.8
8.2 26.8
20.2
0 23.8
5 4.4 3 0 0
26.8
1.2 0
48.2
503.4
14.8
Apr
2.5 9 0 6.4 0 0 0 0 1.8 15.2
0 0 1.5 26.7
27.4
20.4
0 4 0.2 0.8 0 5.8 17 0 0
33.8
0 0 19 6.4 0
197.9
5.8
May
0 0 4.8 22 0 0 0 0 0 1 3
29.7
0 9 0 8.8 0 0 0 0
14.4
0 0 0 0 3 0 0 0 0 0
957 2.8
Jun
0 0 0 0
257 7.6 0 0 0 8.6 3.6 0 0 0 0 11 0 5.8 27 0 0 0 0 0 0 47 0 0 0 0.8 0
137.1
4.0
Jul
0 0 0 0 0
93.2
0 0 0
. 0
0 0 0 0 0 0 0 0 0 27 0 0 0 0 0 0 0 3.2 0 0 18
141.4
4.2
Aug
0 0 0.5 9 4.8 0 1.8 0 0 0 0
20.3
8 0 0 7.4 0 6 0 0 0 0 0 0 0 0 2 12 0 7.6 75 154.4
4.5
Sep
0 0 0
15.6
0 37 0 0 11 12.5
16.5
0 0
10.2
2.6 23 1.6 9.2 6.7 4.2 0 0 0
24.6
7.8 12.6
0.4 0
20.6
8 64.5
288.6
8.5
Oct
0.8 8.2 0 10.9
0 26.2
7.4 22.3
0 73.5
2.5 0 32 8.1 32.6
22.8
0 0 12 3.2 11.6
20.8
2 15.8
4.6 19.4
7 59.3
0 16.8
0 419.8
12.3
Nov
44.1
59.7
0 0.5 59.2
1.6 23.4
18.5
0 57 70 20.4
21 21.4
3.8 32.2
32.6
28.8
38 19 13.8
3.2 0 10 52.4
81.2
25.2
3.6 26.3
6 31
803.9
23.6
Dec
18.5
0 12.5
8 23.5
1.1 31.2
0 21 4.7 38.3
21.5
9 38.1
0 0 18
25.2
30.8
13 4.3 39.8
17.8
5.4 12.2
28.4
0 12
64.5
63.7
13 575.5
16.9
Total
118 100 86.1 125 133 167.7 89.6 58.4 96.7 311.1 263.8 129.1 161.3 172.3 75.6 207 104.8 192.6 185.9 87.4 70 135 111.4 115.4 126 253.5 63.2 137.5 185.8 135.5 369.7 4568 147.3
APPENDIX B Thunderstorm Rainfall Data
B.3 Monthly Thunderstorm Rainfall Data at Sydney Regional Office
Table 5.3 Monthly thunderstorm frequency at Sydney Regional Office station.
Year
1960
61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
Total
Average
Jan
2 2 3 1 0 3 3 1 0 2 6 2 3 1 0 2 0 4 5 1 3 1 1 3 1 2 3 1 4 2 4 66 1.9
Feb
2 3 2 1 1 3 2 3 1 2 2 0 4 3 1 2 2 2 1 1 0 1 0 1 5 1 3 2 0 2 5 58 1.7
Mar
0 0 3 0 1 0 4 0 6 1 4 1 3 0 1 4 1 1 2 4 0 1 0 0 0 1 1 2 0 1 3 45 1.3
Apr
2 1 1 2 0 1 0 0 0 0 0 1 1 2 1 1 1 1 0 1 0 1 0 0 0 2 1 0 2 2 0 24 0.7
May
3 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 2 0 0 0 0 2 0 0 1 0 0 14
Jun
0 1 0 3 2 2 1 0 0 2 2 0 0 0 2 1 0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 21
0.4 ! 0.6
Jul
0 0 0 2 1 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 9 0.3
Aug
0 0 0 3 3 1 1 2 1 0 1 1 0 1 0 0 2 1 1 0 0 1 0 0 1 0 0 0 1 2 1 24 0.7
Sep
2 0 1 2 0 0 2 1 0 2 1 0 0 2 1 0 2 1 1 1 0 0 2 3 4 0 1 0 4 0 1 34 1.0
Oct
3 1 0 2 3 2 3 1 0 3 2 0 1 1 3 4 4 2 2 4 2 1 1 3 1 2 1 4 0 0 2 58 1.7
Nov
2 9 0 1 2 1 1 2 0 3 4 3 4 3 0 1 9 4 0 7 0 2 0 1 6 3 4 2 6 2 5 87 2.6
Dec
2 2 6 4 2 2 0 1 1 3 2 4 2 1 0 0 2 2 4 2 1 3 2 2 2 3 0 1 3 4 2 65 1.9
Total
18 20 16 22 15 16 18 11 10 20 24 12 18 15 9 15 24 20 16 21 8 11 6 13 21 18 14 13 22 16 23 505 14.9
Table 5.4 Monthly thunderstorm rainfall (in m m ) at Sydney Regional Office station.
Year
1960
61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
Total
Average
Jan
8.7 5.6 8.4 18.3
0 21.4
10.4
0.3 0
29.2
69.2
8.2 23.7
7.4 0 3.6 0
28.2
25.7
7.8 21.5
9.2 5.6 20.7
30.7
5 33 30.6
43.3
50 28.4
554.1
16.3
Feb
18 12.1
38.4
4.8 12.2
3.2 15.8
53 10.4
17.5
29.5
0 47 143 16.4
12 65.5
10.2
2.2 0.4 0
23.6
0 8.6 54 15.3
23 9.3 0 7
85.4
737.8 21.7
Mar
0 0
33.2
0 14 0
52.1
0 45 33 88 25 31.8
0 14.2
240 6
18.4
17.2
16.8
0 25.8
0 0 0 9.4 5 11 0 36 48.4
770.3 22.7
Apr
7.2 2 0.5 5.8 0 2.3 0 0 0 0 0 1 4.3 43 7.5 7 1.8 5.4 0 7.4 0 14.2
0 0 0 12.4
0.6 0
75.5
32.2
0 230.1
6.8
May
38 2.5 0 5.8 0 0
20.1
0 0 1.8 0 0 0
29.5
0 0 0 9.2 0 0 64 0 0 0 0 10 0 0 4 0 0
184.9 5.4
Jun
0 8.1 0
53.8
71 41.6
4.6 0 0 82 14.7
0 0 0
26.8
17.6
0 0.6 0 0 0 0 0 0
21.7
16.4
0 0 2 4 0
364.9
10.7
Jul
0 0 0 16.8
4.6 15 0 0 8.4 3 0 0 0 0 0 0 17.6
0 0 0 0 0 0 0 0 8 0 4.6 0 0 0 78 2.3
Aug
0 0 0
53.8
6 0.8 1.6 37.6
2.3 0 5.8 112.7
0 6.4 0 0 3.4 6.8 18.2
0 0 1.4 0 0 1.8 0 0 0 3.4 35 4 301 8.9
Sep
7.3 0 2.5 7.9 0 0 4
41.4
0 18.3
2 0 0 7.4 5.4 0
25.4
12.1
5.1 6 0 0
27.8
15.7
21 0 0.2 0
26.4
0 34
269.9
7.9
Oct
12 10.7
0 13 10 19 21.2
2 0 10 2.3 0
28.7
3.6 17 41.6
40 1.5 6
27.3
3.6 8.8 2.2 53.5
2.6 13.6
6.6 128.8
0 0 1
486.6
14.3
Nov
21.8
133.8
0 3.6 61.5
2.3 3.3 89.4
0 11.5
68.6
18 8.7 24.3
0 0.7 90.4
15 0
49.1
0 3.2 0 3.6 334 10.3
8.6 8.6 51 12 45 1078
31.7
Dec
22.3
41 29 47.5
16.5
3.8 0 5.8 4.6 20.5
29.7
20 14.8
6.6 0 0 23 12.6
38.6
0.4 23.6
23.6
10.4
22.2
21 16 0 3 69 40.3
4.4 570.2
16.8
Total
135.3
215.8
112 231.1
195.8
109.4
133.1
229.5
70.7
226.8
309.8
184.9
159 271.2
87.3
322.5
273.1
120 113 115.2
112.7
109.8
46 124.3
486.8
116.4
77 195.9
274.6
216.5
250.6
5626
181.5
APPENDIX B Thunderstorm Rainfall Data
B.4 Monthly Thunderstorm Rainfall Data at Sydney Airport
Table 5.5 Monthly thunderstorm rainfall frequency at Sydney Airport station.
Year
1960
61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
Total
Average
Jan
2 2 1 2 0 2 3 0 1 1 5 0 2 1 0 4 0 3 5 1 3 3 2 3 2 3 5 1 4 2 5 68
2.00
Feb
1 2 2 1 4 2 3 3 2 2 1 2 5 3 1 1 2 2 3 1
[___!_ 3 0 1 4 1 4 1 0 1 4 63 1.85
Mar
0 0 4 1 1 0 4 0 5 1 3 1 3 0 1 4 1 2 2 2 0 1 0 1 0 1 2 3 0 2 4 49 1.44
Apr
0 1 0 1 0 1 0 0 0 0 0 1 1 2 0 0 0 1 0 1 2 2 0 0 0 1 2 0 1 3 2 22
0.65
May
1 1 0 0 2 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 3 0 0 0 0 2 0 1 1 1 0 16
0.47
Jun
0 1 0 1 1 2 1 0 1 2 2 0 1 0 2 0 1 1 0 0 0 0 0 0 1 1 0 0 0 1 0 19
0.56
Jul
0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 1 0 1 0 0 0 8
0.24
Aug
0 5 0 1 0 1 1 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 0 2 1 1 1 2 29
0.85
Sep
1 1 0 2 0 1 1 1 0 2 1 1 0 1 2 1 2 2 3 0 0 0 2 2 3 1 1 0 4 0 1 36
1.06
Oct
5 1 0 2 2 2 4 3 0 1 1 0 0 1 2 4 4 1 3 3 2 2 1 3 1 3 2 4 0 0 1 58 1.71
Nov
0 4 0 2 2 1 2 3 1 4 5 3 3 6 1 2 9 4 1 7 1 3 0 2 8 5 4 2 5 2 4 96
2.82
Dec
4 2 4 5 1 3 1 0 1 3 4 2 1 1 1 0 4 3 3 1 2 3 2 2 2 4 1 3 3 2 1 69
2.03
Total
14 20 11 19 13 15 20 11 13 17 23 11 17 17 11 16 25 21 21 20 15 18 8 14 22 23 23 17 19 15 24 533 15.68
Table 5.6 Monthly thunderstorm rainfall (in m m ) at Sydney Airport station.
Year
1960
61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
Total
Average
Jan j Feb
30 5 1.3 26 0 15 12.5
0 2 16.5
24 0 16.5
6.6 0 6.2 0 19.5
47.2
5.4 18.2
13.5
18.5
9.2 15.3
5.2 44 11.5
36.5
12 19
436.6
12.8
9 2
46.5
1.8 5.7 1
15.5
92.5
19 12.5
26 44 56.5
30.5
13.2
42 23 14 0.8 10 40 33.2
0 1.2 27 3.4 30.4
18 0 0
69.3
688 20.2
Mar
0 0 4.1 2.5 8.5 0 102 0 26 22 85 10 51 0 17 202 4.6 14.2
35 10 0 1 0 9.6 0 6.6 2.5 41 0
26.6
32.2
713.4
21.0
Apr
0 15.5
0 2.5 0 2 0 0 0 0 0 49 10.5
54 0 0 0 10 0 3.6 5.5 24 0 0 0 18.2
10 0 70 74 17
365.8
10.8
May
2 0.5 0 0 0 0 0 0 0 0 0 0 0 16 19 0 0 3.6 0 0.2 18.5
0 0 0 0 20 0 1 3 10 0
93.8
2.8
Jun
0 0.3 0
10.2
59.2
33 1.3 0 6.5 26 7.5 0 1.3 0 13 0
29.2
0.2 0 0 0 0 0 0 15 17 0 0 0 7.2 0
226.9
6.7
Jul
0 0 0 2.5 2.8 0 0 0 10 0.5 0 0 0 0 0 0 35 0 0 12 0 0 0 0 0 0.4 0 9 0 0 0
72.2
2.1
Aug
0 13.2
0 33.5
0 0.8 7 24 7 0 8
48.5
6.6 16 0 0 3.6 4.6 24.5
1 0.4 0.2 15.5
0 4 0 111 2 6
11.1
10 358.5
10.5
Sep
3.6 2.8 0 17 0 0.5 1 35 0 4 28 15.5
0 4
20.5
16 13 18 4 0 0 0
21.6
19 15.5
0.4 0.4 0 19 0 41
299.8
8.8
Oct
15.7
11.5
0 13 7 15 7.8 4 0 0.3 1.3 0 0 6.6 13 22.6
67 0.8 56 25.6
17 3.6 1.8 54.2
2.8 13.6
23.5
109 0 0 11
503.7
14.8
Nov
0 67.5
0 1.5 17.3
1.5 5 54 0.3 14.2
95.5
21 12.2
23 0.4 14.5
98 15.4
1.4 51.5
33 20 0
21.5
265.5
62 24 9.5 65.5
11 11 1017
29.9
Dec
55.5
10 30 91.5
8.4 23 9.5 0 6 29 38.6
28.5
10 2.3 5 0 20 4.6 31.5
1.2 14 59.2
6 28.5
46.5
17.6
8 24.5
112.5
33 3
757.4
22.3
Total
115.8
128.3
81.9
202 108.9
91.8
161.6
209.5
76.8
125 313.9
216.5
164.6
159 101.1
303.3
293.4
104.9
200.4
120.5
146.6
154.7
63.4
143.2
391.6
164.4
253.8
225.5
312.5
184.9
213.5
5533
173.4
APPENDIX B Thunderstorm Rainfall Data 252
B.5 List of Rainfall Stations
All stations were sorted by latitudes and longitudes (in decimal) from northwest to south-east of the Sydney region. * The Sydney Water stations are identified by a prefix of the number 5.
Table 6.2 List of stations and the periods from which data were used.
Row Station Number Name of Stations Latitude0 Longitude0;
Elevation in m Period
No. of Events
63057 Mount Wilson 33.51 150.37
63184 [
63246
Blaxland Ridge
Mount Wilson
33.51
33.52
150.90
150.37
1027
177
!1960-78 100
1005
1962-79
1969-86
109
133
63118
63013
Bilpin (Fern Grov.)
Berambing
33.52
33.53
150.50
150.43
610
792
1960-93
1960-93
282
104
63043 Kurrajong Heights 33.53 150.63 495 1960-93 227 63042
-AAJW U U U A J J U U U U U
Kurrajong P.Of. 33.55 150.67 152 1960-91 184 . ,A**J^UUIAJJUUUUUUU«JJUUMU' HII
67090 Arcadia 33.58 151.07 205 1964-93 202 66143 Kuring-Gai Chase 33.58 151.30 170 1969-91 140 63056 Mount Victoria 33.60 150.27 1064 1960-90 159
11 63248 Grose Wold 33.60 150.68 61 1969-93 233 12 67033 Richmond AMO/MO 33.60 150.78 19 1960-93 271
1960-93
66128 Palm Beach (G.C) 151.32 1965-89
17 67021 Richmond (H.A.C.) 33.62 150.75 20 1960-93 268 18 67073 Maralya Boudary RD. 33.62 150.90 49 1963-93 263 19 63009 Blackheathp P.Of. 33.63 150.28 1065 1960-93 198
* 567100; Riverstone ivwvvvwevvvn rr * V W W V W W W * V W V I r t Y**1
33.65 150.83 25 1984-93 120 21 66119 Mount Kuring-Gai 33.65 151.13 215 1964-93 242 22 67002 Castlereagh 33.67 150.68 15 1965-93 216
«*wu M***WWiV^^WI
23 66183 Ingleside Walter Ave. 33.67 151.27 160 1984-93 130 24 66045 Newport B.C. 33.67 151.32 1960-93 213 25 563059 Katoomba 33.68 150.30 950 1984-93 122 26 67086
"h Dural (Old Northern RD) 33.68 151.03 216 1973-93 181
27 566051 Wamewood 33.68 151.30 15 1982-93 135 28 63227 Wentworth Falls 33.70 150.37 900 1967-93 176
150.48 29 563070 Linden W.F.Dam 33.70 520 i1984-93 123 30 63077 Springwood P.Of. 33.70
• rrrr>rr*rrT+rr**
150.57 ;366 1960-93 240 31 567076 Castle Hill 33.70 150.98 65 1984-93 123 32 66028 Hornsby Police STN 33.70 151.10 181 1960-93 110 33 63044 Lawson P.Of. 33.71 150.43 1715 1960-93 193 34 63039 Katoomba Composite 33.72 150.30 1030 1960-93 190
51 36
563067
63045
Wentworth Falls
Leura P.Of™
33.72
33.72
150.38 823 150.43 1975
1982-93
1960-93
101
186
37 66063 WWAMMMArtMNMrVVV -
Wahroonga Reservoir *?_
33.72 151.12
ST. Marys S.T.P. 33.73 205 1960-93
f V A W A V A W A W V A W ^M^^M^'J^^^^V^W^WVt ^VJWIWVMMI^AVWVriJl 188
^^.NW^AV^A^WMVA^VW
38 567087 150.77 20 1984-93 129 67076 Quarkers Hill 33.73 150.88 33 1966-92 184 66158 Turramurra (kiss. PT.RD) 33.73 151.13 160 1960-93 301 66157 Pymble(Canisius College) 33.73 1151.15 1165 1960-93 281 66044 Cromer 33.73 151.27 10 1960-93 172 66182 Frenchs Forest
...•JJUUIIIU'WT' rwowvwwwv»»l»TW> irvvwwvwwwwvww 33.74 151.23 155 1960-93 277
63230 Blaxland western Highway 33.75 150.60 234 1968-80 100
APPENDIX B Thunderstorm Rainfall Data 253
Table 6.2 cont....
45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97
67018
67089
67098
66118
63185
67067
67024
67059
67080
566040
66020
66156
66120
67026
66032
66056
66145
66153
566025
66089
67084
66124
66087
66081
566017
66010
66167
66002
567092
67019
67032
66130
66042
66138
67070
66085
66134
66131
66075
66166
566027
66082
567079
66013
66163
67029
67068
567077
67008 > „..«....—
566022
66017
66062
66006
Penrrith
West Pnnant Hills
Pennant Hills West
Frenchs Forest (F. Av)
Glenbrook B.C.
E m u Plains Gough ST
ST Marys B.C
Blacktown Kidare R D
Winston Hills
West Epping
Epping (Chester St)
Marsfield (Macq.Uni.)
Gordon B.C.
Seven Hills Exp.Farm
West Lindfield
Roseville B.C.
Seaforth (Castle Circuit)
Manly Vale(ManDam)
Manly Dam
North Manly B.C.
Orchard Hill
Parramatta North
Eastwood B.C.
North Ryde Stround St.
Chastswood
Chatswood Council
Northbridge B.C.
Balgowlah
South Prospect
Prospect Dam
Westmead Austral Av.
Northbridge
Mosman (Bapaume R D )
Manly Army North Head
Merrylands Wellsford ST
Auburn/ Granville Composite
Granville Shell Refinery
Riverview Observatory
Waverton B.C.
Cremorne Grasmere R D
Mosman
Concord West Plaster Mills
Guildford
Concord G.C. r Watsons Bay
Wallacia
Badgerys Creek RES STN
Fairfield
Guildford
Home Bush
Fivedock Council DEP
Sydney Regional Office
Sydney Botanic Gardens
33.75
33.75
33.75
33.76
33.77
33.77
33.77
33.77
33.77
33.77
33.77
33.77
33.77
33.78
33.78
33.78
33.78
33.78
33.78
33.78
33.80
33.80
33.80
33.80
33.80
33.80
33.80
33.80
33.82
33.82
33.82
33.82
33.82
33.82
33.83
33.83
33.83
33.83
33.83
33.83
33.83
33.84
33.85
33.85
33.85
33.87
33.87
33.87
33.87
33.87
33.87
33.87
33.87
150.68
151.04
151.05
151.23
150.62
150.65
150.77
150.88
151.00
151.05
151.08
151.12
151.15
150.93
151.15
151.18
151.23
151.25
151.24
151.27
150.72
151.02
151.08
151.13
151.18
151.20
151.22
151.25
150.90
150.92
150.98
151.22
151.24
151.30
150.98
151.02
151.03
151.17
151.20
151.22
151.23
151.08
150.97
151.10
151.28
150.63
150.73
150.95
150.98
151.08
151.12
151.20
151.22
25 120 168 150 183 31 35 58 75 100 92 55 96 55 60 116 85 20 21 5 93 60 78 70 92 96 35 70 65 61 26 80 70 85 45 8 3 23 21 61 85 5 50 15 25 50 65 5 31 10 6 42 15
1960-93
1960-93
1960-93
1964-82
1963-93
1960-93
1960-84
1963-93
1968-93
1980-93
1960-93
1970-92
1960-93
1960-90
1960-92
1960-79
1968-92
1968-93
1963-93
1961-87
1970-92
1965-93
1960-93
1960-79
1963-93
1960-93
1980-92
1960-89
1975-93
1960-93
1960-92
1960-80
1960-93
1968-88
1968-93
1960-93
1960-93
1960-78
1960-93
1963-89
1960-93
1961-82
1972-93
'1960-93
1968-93
1960-93
1960-93
1981-93
1960-77
1969-93
1960-93
,1960-93
1960-93
r*'""v*'"v'~wwww,iMWV
256 187 233 129 229 274 127 256 215 153 273 210 274 224 248 106 208 |
147 201 113 188 273 |
150 101 243 214 128 217 153 283 268 127 280 111 254 242 197 103 258 185 164 121 190 245 138 267 230 138 105 152 224 287 257
B Thunderstorm Rainfall Data
Table 6.2 cont....
98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150
66098
566038
563046
568045
567093
566050
66164
66070
66000
566032
66005
66050
566020
66160
66073
67035
66025
566049
66003
566026
66052
67036
66137
566033
66076
66037
66171
66054
66004
67015
67009
66148
566047
567078
66181
66069
66051
66058
66072
68007
566018
66086
68192
66078
566056
66040
66014
563037
568138
568130
66090
66001
68081
Rose Bay R/S G. C.
Vaucluse B.C.
Mcmahons Loo
Wairagamba
St.Johns Park
Villawood
Rookwood
Strathfield G.C.
Ashfield B.C.
Paddington
Bondi B.C.
Potts Hill (pumping st.)
Enfield
Centennial Park
Randwick Racecourse
Liverpool Council
Warwick Farm
Liverpool
Bankstown (Condell Park)
Marrickville
Randwick B.C.
Austral Eighth Ave
Bankstown A M O
Padstow
Wiley Park
Sydney Airport
MoorebankN.B.
Revesby
Bexley G.C.
Bringelly (Maryland)
Glenfield Composite Macquarie
Peakhust Golf Course
Mortdale B.C.
Glenfield
Oatley (Woronora Parade)
Hurstville Grove
Little Bay (Coast G.C.)
Sans Souci
Kurnell(A.6.R)
Camden (Brownlow Hill)
Cronulla S.T.P.
Cronulla W.P.C.P.
Camden j irport
Lucas Hts (A.A.E.)
Yarrawarrah
Miranda Blackwood ST
Cronulla South B.C.
Barragorang
Oakdale
West Camden
Engadine
Audley National Park Bottom
Campbelltown S.C. MWWWMJWM1MIMI r„..nn^nflJiJuvll
33.87
33.88
33.88
33.88
33.88
33.88
33.88
33.88 ]
33.88
33.88
33.88
33.90
33.90
33.90
33.91 |
33.92
33.92 ]
33.91 !
33.92 ;
33.92
33.92
33.93
33.93 1
33.93
33.93 ^
33.93
33.95
33.95
33.95
33.97
33.97
33.97
33.97
33.98
33.98
33.98
33.98
34.00
34.02
34.03
34.03
34.03
34.05
34.05
34.05
34.05
34.05
34.07
34.07
34.07
34.07
34.07
34.08
151.27
151.27
150.38
150.58
150.88 .
150.98
151.05
151.07
151.13
151.22
151.27
151.03
151.08
151.23
151.23
150.92
150.93
150.93
151.02
151.15
151.24
150.82
150.98
151.02
151.07
151.17
150.95
151.00
151.10
150.72
150.90
151.05 ,
151.07
150.90
151.08
151.10
151.25
151.13
151.22
150.65
151.15
151.17
150.68
150.98
151.02
151.10
151.15
150.40
150.43
150.67
151.02
151.05
150.52
6 75 655 180 35 30 41 21 25 45 15 55 10 38 25 21 5 5 10
5 75 60 9 20 45 6 22 15 10 122 23 39 40 15 42 5 22 9 3 61 10 |10_
70 140 50 40 30 180 410
1979-93
1980-93
1982-93
1982-93
1980-93
1980-93
1974-93
1960-93
1960-93
1961-87
1960-82
1960-93
1983-93
1960-93
1960-93
1962-93
1960-90
1960-93
1960-79
1980-93
1960-93
1964-89
1968-92
1981-93
1960-87
1960-93
1968-80
1960-93
1960-93
1960-91
1960-83
1969-87
1978-93
1984-93
1982-93
1960-81
1962-93
1960-93
11960-93
1960-93
1972-93
;1960-93
!1960-93
1960-93
1983-92
1960-93
1960-93
!1983-93
1984-93
75 j 1983-93
170 j 1962-93
23 f1960-79
75 1960-92
136 159 119 133 144 143 142 191 259 100 120 267 128 225 196 272 189 258 127 |
145 248 158 223 109 146 288 100 164 208 196 132 114 140 125 149 140 216 290 284 269 200 234
i 171
183 112 274 266 132 129 110 128 100 171
APPENDIX B Thunderstorm Rainfall Data
Table 6.2 cont..
151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191
66176
66116
566052
563036
68013
68052
568072
568069
568051
68001
68028
568038
68024
568139
568048
68016
568065
568004
568050
568060
568047
568049
568067
568046
568097
68030
568061
568068
68188
568099
568118
68044
568136
68033
568058
568071
68023
68102
568054
68053
68022
Audley Royal Notional Park
Bundeena Composite
Woronora Dam
Yerranderie
Menangle (JMAI)
Picton Composite
Cobbong
Reverces
Oakdale
Appin (Bulli Road)
'Helensburgh P.Of.
Wollondilly River
Darker Forest (Kintyre)
Buxton P.O.
Cataract Dam(w.B.S)
Cataract Dam
Letle Box Tower
Cordeaux Air.St.
Hill Top
Ironbark
Nepean Dam
Cordeaux Quart
Beth Salem
Avon Dam
Mount Keira
Mittagong
Browns Roard
Upper Cordeaux
Wollongong Uni.
Leicester Park
Wollongong STP
Mittagong Pool
Wollongong
Mittagong (K.O)
Hambridge
Upper Avon
Dapto West
Bowral (P.D)
Mittagong Ma. Cr.
Port Kembla S.ST.
Dapto B.C.
34.08
34.09
34.12
34.13
34.13
34.18
34.18
34.18
34.20
34.20
34.20
34.23
34.23
34.25
34.27
34.27
34.27
34.28
34.30
34.30
34.33
34.33
34.33
34.35
34.37
34.39
34.40
34.40
34.40
34.43
34.43
34.45
34.45
34.47
34.47
34.47
34.47
34.48
34.48
34.48
34.49
151.05
151.15
150.93
150.30
150.73
150.62
150.85
150.92
150.50
150.78
150.98
150.32
150.92
150.53
150.80
150.81
150.87
150.72
150.42
150.67 '
150.60
150.75
150.85 }
150.63 j
150.82
150.30 j
150.70
150.77 >s
150.88 •
150.38
150.90
150.47
150.90
150.50
150.63
150.73
150.77
150.40
150.52
150.90
150.78
120 45 180 298 80 171 280 305 410 230 150 200 370 390 340 340 449 380 580 300 390 335 366 390 430 735 442 330 30 670 5 625 15 625 491 330 42 690 570 11 10
1979-93
1964-78
1983-93
1982-93
1960-92
1960-92
.1984-93
1983-92
1982-93
1960-93
1960-93
1982-93
1960-93
1966-93
1982-93
1960-93
1982-93
1984-93
1981-93
1983-92
1982-93
1984-93
1982-93
1982-93
1982-93
1960-93
1984-93
1982-93
1960-92
1960-93
1980-93
1960-93
1975-93
1960-93
1982-93
1982-93
1960-87
1960-93
1981-93
1960-92
1960-93
156 101 126 139
; 229
215 128 108 148
1 135 182 133 j
276 |
245 133 279 146 122 137 103 118 100 142 135 I
138 245 133 141 224 220 155 187 |
148 244 141 154 143 237 126 138 235
APPENDIX C Synoptic Weather Charts 256
APPENDIX C
Synoptic Weather Charts
This appendix contains the 6 sets of different synoptic charts. During the domination of
these weather systems the biggest thunderstorm rainfall events (for each thundery mounts,
October to March 1975 to 1993) occurred in the Sydney region (see Chapter 6 for more
details). Synoptic charts were taken from the Monthly Weather Review of the N e w South
Wales (Bureau of Meteorology). All attempts have been made to maintain the clarity and
detail of information on the synoptic charts.
Synoptic charts 6.1 from 23th to 25th October 1987.
APPENDIX C Synoptic Weather Charts 257
Synoptic charts 6.2 from 5th to 11th November 1984.
-~"jl,"__**H006* ^»: i
lo)" ~ ^—' / "JOB 'oo*
APPENDIX C Synoptic Weather Charts 258
Synoptic charts 6.3 from 9th to 11th December 1988.
APPENDIX C Synoptic Weather Charts 259
Synoptic charts 6.4 from 19th to 22th January 1991.
,,0T—~+"~~ X ^
\ A T
\ / *?°jj—=»-r~-'
wi2 \ V r \ J 1
\ V \iv Jk s \ \
+ \
18) -10 W-\- Jr
-A F"^^
1 ^^^-W20
^ X + X i
/ '
'• + \
r0W\ J>
T?K ' -U^H -
+ o 1012
jXT~ ' L
+
—'—^?—=—f-ita*
— r ^ +
I 1012
ki 1020 + 1oW \
\ • r " -w^xVi /
V +i t ,AT K
\ / , J E ™ ^ ' J r—Ksr\ x.
J012 \ \ .\ + V T \
M ^w\ y -ioiii40----r"" CX yx. JX-^"
+ \\ A (v \JL
Q
'iooo\ V
1004*"
^—-+^'°^\ \ \ ni
t^ion V. + 10VT ihi T "
' +\ 10 \
+/
+ f
T* * ' m
f +
V"^ <L 1012 M /
f Y+VLi-1 wo / /
/ / I J ° — / — _
*J N-J012
b-1020^_ /
APPENDIX C Synoptic Weather Charts 260
Synoptic charts 6.5 from 7th to 11th February 1990.
APPENDIX C Synoptic. Weather Charts 261
Synoptic charts 6.6 from 10th to 11th March 1975.
APPENDIX D Data Used for GIS and Statistical Models 262
APPENDIX D
Data Used for GIS and Statistical Models
Geographical Location of Rainfall Stations and their Attributes
Landuse classes
CBD = Central Business District URT = Urban-Residential (treed) IND = Industrial areas R U S = Rural / Semi-Urban U R B = Urban-Residential (barren) R U O = Rural / Open areas T N P = Treed (National / Urban parks)
* Indicates the location of a rainfall station located in one of the four sub-topographic regions A, B, C and D. For stations numbers and their names see Appendix B,Table 6.2.
Table 7.2 Geographical location of rainfall stations and their attributes and average rainfall of the 6 biggest monthly thunderstorm rainfall events (October to March - 1975 to 1993).
APPENDIX D Data Used for GIS and Statistical Models 263
Table 7.2 cont....
APPENDIX D Data Used for GIS and Statistical Models 264
Table 7.2 cont.
APPENDIX D Data Used for GIS and Statistical Models 265
Table 7.2 cont.
Equations Used in SPANS GIS
APPENDIX E
Equations
Table 7.6 Equations which were written in SPANS GIS environment.
E Reclass Reclassification of Rainfall Map
: This equation reclassifies the thunderstorm rainfall map having more than 120 mm rainfall
A = { 1 if class (biggrain)>3, 0};
: where 'biggrain' is the name of the rainfall map (average of the biggest thunderstorm rainfall events
: where 1 and 3 are the number of classes on thunderstorm rainfall map
Result (A)
:The resulted map is the study area's map showing the areas having >120 rainfall amount
E Overlay Overlaying of the Reclassified Map
: This equation overlays (imposes) the reclassified rainfall map upon the physiographic maps
A = {class (input map) if class ('reclass map') = 1, 0 or o 0, 0};
: where input map is the name of each physiographic map in the equation. This equation was applied
for all of the physiographic maps such as the proximity, elevation, aspect and landuse maps of the
Sydney region.
Result (A)
:The result of the 'GIS overlaying modelling technique' is shown in Figure 7.5 (a-d).
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