pacrain journal article

8/8/2019 PACRAIN Journal Article

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1Department of Geography University of Oklahoma Norman, OK 73019

2Environmental Verification and Analysis Center University of Oklahoma Norman OK 73019

3School of Meteorology University of Oklahoma Norman OK 73019

The Comprehensive Pacific Rainfall Database:

An enhanced tool for research and education

J. Scott Greene1,2, Michael Klatt2, Mark. Morrissey2,3, and Susan Postawko2,3

Corresponding Author:

Scott Greene

Department of Geography

University of Oklahoma

Norman, OK 73019

[email protected]

Submitted to Theoretical and Applied Climatology


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ABSTRACT

It is the intention of this paper to introduce and describe the Comprehensive Pacific Rainfall

Database (PACRAIN). The PACRAIN database contains daily and monthly precipitation records

from the tropical Pacific basin. The database is collection of observations from a variety of sources,

including one, the Schools of the Pacific Rainfall Climate Experiment (SPaRCE), that is unique to

PACRAIN. SPaRCE is a cooperative field project and involves schools from various Pacific island

and atoll nations. Students and teachers from elementary school, middle school, high school,

college, and trade school collect precipitation (and other environmental) data that becomes part of

the PACRAIN database.

Recent enhancements to the database, including improved quality control, observation and

data entry standardization, expansion of the network, increased collaboration with local

meteorological directors, and enhanced high-resolution data (e.g., on hourly or minute time scales),

are discussed. This paper also outlines some of the internal data, computer programming, and web-

based access specifics of the database. To illustrate the potential usefulness of the data, two

examples of research using the PACRAIN database are provided and discussed. The first is an

analysis of temporal changes in the extreme event characteristics of daily precipitation. The second

is an illustration of how the PACRAIN database is currently being used to validate satellite-based

precipitation algorithms. These two examples are not intended to be exhaustive; rather, they are

included to illustrate the range of research opportunities that are available to researchers who choose

to download and use the PACRAIN Database.

Keywords: Pacific Rainfall, Precipitation data, science and education


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I. Introduction

The severity and occurrence of precipitation events is an important component for a range

of environmental and hydrological parameters. A significant potential consequence of a changing

climate is a change in these events. This is especially true in the Tropical Pacific because of the

ocean-atmosphere links in heat energy, and the resultant release of this energy through phenomenon

such as the El Nio/Southern Oscillation. Changes in precipitation patterns are important since

research shows that there have been significant shifts in temporal patterns in the tropical Pacific.

For example, research has suggested that climate change may be producing a multi-decadal scale

change in the Pacific regional hydrologic cycle (e.g., Morrissey and Graham, 1996; Greene, et al.,

2006). Thus, to examine the potential long-term future trends, as well as to model interannual and

longer-term climate change, tropical rainfall measurements are essential.

Many climate models require spatial and temporal densities of measurements that can only

be obtained by using satellite data. However, without surface island raingauge observations, the

satellite rainfall estimates are of unknown accuracy. Even radar measurements, due to its indirect

inference of rainfall from active microwave reception, require raingauge measurements to assess

their accuracy. Thus, programs such as Global Precipitation Climatology Project (Adler, et al.,

2003), which estimates global precipitation through a merging of infrared and microwave satellite

estimates of precipitation with rain gauge data, must have their rainfall algorithms validated in order

to understand the validity of the underlying physics associated with the construction of these

algorithms. While island-based raingauges cannot measure open ocean conditions directly, gauges

located on atolls have been shown to be fairly representative of open ocean conditions.

Given that the Pacific Ocean represents one-third of the global surface area, the relative lack

of continuous observations presents a tremendous gap in the global observational network. This

deficiency has been recognized by the international community, and as a consequence the Global

Climate Observing System (GCOS) and in particular the Pacific Island Global Climate Observing

System (PI-GCOS) programs have been put together to help maintain and enhance the observing

capabilities of the area. The research and data collection described here follow the GCOS plan,

which states that, given the critical importance of the Pacific in global climate, and also given


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sparseness of data from this region, a strong regional program in support of GCOS is a benefit to the

global climate observing effort (see: http://pi-gcos.org/for more specific details).

Thus, the goal of this paper is to outline a newly-enhanced resource that will allow

researchers to more effectively examine questions of patterns and variability in tropical pacific

rainfall. The Environmental Verification and Analysis Center (EVAC) at the University of

Oklahoma currently houses the Comprehensive Pacific Rainfall Database (PACRAIN). The

PACRAIN database is the most extensive Pacific island raingauge data base in the world. Data have

been collected from hundreds of Pacific island stations, with some records going back as far as the

1800's. It is currently available to scientists on-line (http://pacrain.evac.ou.edu). Data analysis and

quality control of these data is essential and is considered an operational task since data collection

in the Pacific is a variable and sometimes inconsistent process. The goal of the PACRAIN database

is to satisfy the need for a single source of easily-accessible and homogeneous tropical Pacific

rainfall data. This paper outlines recent advances and enhancements to the PACRAIN database,

explains the database and computer components, describes some examples of data quality control,

and provides two illustrative examples of its applicability as a research tool.

II. Data Description

The PACRAIN database is made up of information from a combination of government

agencies, historical archives, and resources unique to EVAC. Table 1 shows that the primary

government agencies are the National Institute for Water and Atmospheric Research (NIWA) in

New Zealand, the National Climatic Data Center (NCDC) in the United States, and Mto-France

in French Polynesia. Figure 1 shows the spatial pattern of the station locations. Additional datasets

include observations from the local meteorological services of the Pacific, the Atlas of Pacific Island

Rainfall (Taylor, 1973), observations from Japanese possessions prior to World War II, and the

Schools of the Pacific Rainfall Climate Experiment. The database currently contains over 1.3

million daily observations from 653 sites, extending from 1971 to the present. There are more than

40 thousand monthly observations from 201 sites, extending from 1874 through 1970. There are

54 sites with both daily and monthly values, giving them very long periods of record. Daily data


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are organized into files by observation site, and monthly data are organized into files by month.

There is also an interactive query form, which allows the user to select data based on criteria like

date and location. The data are publicly available via the internet at the PACRAIN web site:

http://pacrain.evac.ou.edu.

Table 1 summarizes the characteristics of the PACRAIN data. It includes the source, terrain,

and temporal frequency of the data. The source data illustrates the major sources of the data,

described above. One source of data unique to the PACRAIN dataset, responsible for approximately

5% of the overall data, is the Schools of the Pacific Rainfall and Climate Experiment (SPaRCE;

Postawko et al., 1994). The SPaRCE program will be described below. Table 1 also divides the

data into temporal frequency. The daily data refer to all data obtained after 1971 from the sources

listed above. The monthly data prior to 1971 are digitized versions of the Atlas of Pacific Island

Rainfall (Taylor, 1973) which contains monthly data extending back to the 1870s. Monthly data

summarized from the available daily data can also be generated from the PACRAIN webpage. The

final data division shown in Table 1 is the separation of the data into different terrain characteristics.

This information is available for those users that require a specific terrain classification for

specialized studies. For example, the PACRAIN database has been used in the past to validate

satellite-derived precipitation estimates. To accomplish this and remove any land bias, the

PACRAIN database is used to approximate open-ocean precipitation. Thus, the stations selected

are those that come from atolls only to insure that there was no orographicly-derived precipitation

biases in the data analysis. Approximately one-third of stations are from atolls, which have been

shown (Lavioe, 1963; Morrissey, et al., 1994) to provide an adequate approximation of open ocean

conditions. Any potential PACRAIN user can select the data divided up by any category or

categories desired (e.g., one can download only daily, atoll data from New Zealand sites).

IIA. The Schools of the Pacific Rainfall Climate Experiment (SPaRCE)


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As mentioned above, one of the unique datasets available in the PACRAIN database is the

Schools of the Pacific Rainfall Climate Experiment (SpaRCE). The Schools of the Pacific Rainfall

Climate Experiment (SPaRCE) involves teachers and students from various Pacific island and atoll

nations in making and analyzing environmental measurements vital to the understanding of global

climate and climate change. Students and teachers from elementary school, middle school, high

school, college, and trade school participate in collecting and analyzing the data. There are many

educational benefits to participants from involvement in the SPaRCE program. It allows teachers

to increase the quality of their science education programs by bringing up-to-date information on

climate and climate change into the classroom. The SPaRCE program allows students the first-hand

experience of collecting and analyzing data, i.e., it involves them in a real research program. The

SPaRCE program not only exposes students to real-world meteorological concepts and problems,

it also empowers students by helping to develop the knowledge and techniques required to make

practical use of climate data.

Generally, small islands and atolls have only one rain gauge sited and maintained by

government weather services. Satellite (and radar)-rainfall validation techniques require information

on the spatial structure of tropical rainfall, which is known to be quite small (


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One advantage of the PACRAIN database is the high level of metadata information

available, as well as the rigorous quality control that the data has undergone (Greene and Morrissey,

2000; Morrissey, et al., 1995). In addition to the traditional quality control approaches as proper

siting and reliable metadata, a key component of the PACRAIN database is the examination of more

sophisticated aspects of quality control analysis. For example, examination of the different sources

of the rainfall data revealed that the time and date of observation varies from country to country.

For example, the US stations (obtained from the National Climatic Data Center) use the end of the

accumulation period (i.e. observation date) while the New Zealand-affiliated stations (obtained from

the National Institute for Water and the Atmosphere) use the beginning of the accumulation period.

Recent updates to the PACRAIN database include synchronizing the date of record stamp to be the

beginning of the accumulation period as necessary. Using the beginning of the accumulation period

seems more intuitive than using the observation date, especially for lay users. This convention is

also superior for storing records of arbitrary frequency. More recently, all records were given a

complete time stamp, which is essential for daily and higher-frequency data, and all time stamps

were converted to UTC where possible, a change that is especially important for a domain that

straddles the Date Line. Finally, it should be noted that the identification and correction of

inaccuracies in the data is an ongoing effort. This review process is being applied to site records as

well as rainfall records.

IIIA. Example Quality Control Examination: Day of week Bias

In a desire to provide the user community with more information regarding the quality of the

PACRAIN database, a series of research efforts into the overall quality characteristics of the data

have been undertaken. The example shown here is one such test, which analyzed the percentage of

no precipitation and the average daily rainfall per day of the week (e.g., Monday, Tuesday, etc.).

The purpose of this test was to potentially uncover the human element in the recording of data; for

example, perhaps an employee reports erroneous values for the weekends when he/she returns on

Monday. It was discovered in this examination that one of the most dominant error patterns was not

indicating rainfall as an accumulated value while incorrectly recording zero values prior to the


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accumulation. A clear example of this error pattern is illustrated in Table 2. As this table shows,

for the selected location, the number of days with zero recorded precipitation jumps from about 40%

during the week to over 70% during the weekend. Since the data was recorded as zero, rather than

missing, this has a significant impact on the estimate of the mean daily rainfall for each day during

the week. The mean values thus range from 135-170 mm from Tuesday to Friday, drop to 80mm

during the weekend, and spike to over 300mm/day on Mondays. Clearly, the explanation for this

pattern is not due to a physical or dynamical process in the region, but rather that the Monday values

represent a three-day accumulation of precipitation, rather than a one-day value (this also explains

the low value of days with zero recorded precipitation on Monday). Thus, it is clear that the

raingauge was allowed to collect precipitation over the weekend, and then the three-day

accumulation was recorded. However, the precipitation was recorded as missing on Saturday and

Sunday, rather than accumulations. Stations that exhibit this type of bias have been noted and

flagged as part of the PACRAIN quality control efforts.

Several other types of analysis similar to the day of week bias have also been performed.

As a summary of how well each station performed on the quality control tests as a whole, a final

summary was prepared for each station. These techniques have been applied to examine the

accuracy of the data, and to determine potential problems. The use of the data is, of course,

ultimately left up to the individual user. The details, as well as a description of the methods and

results are not included in this paper, but the full information can be found at:

http://pacrain.evac.ou.edu/qcpage.html

IV. Database Internals

This section provides a brief overview of the computer programming and database

management details that underpin the PACRAIN database, and allow for the user-friendly simple


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web-based interface. The purpose of any database is to preserve and provide access to data.

Preservation involves not only data storage but ensuring data integrity. The most basic type of

database is the flat file database, which is tabular data stored as one or more text files. Flat file

databases are most appropriate for small, uncomplicated datasets. A flat file database can be

maintained with the built-in file-handling capabilities of the operating system and a text editor.

More complicated file manipulation requires a database management system (DBMS). It provides

all of the basic database functionality, such as data manipulation, retrieval, and security. The

PACRAIN database began as a static dataset that was distributed by hard copy or floppy disk

(Morrissey and Greene, 1992). Within a few years it had expanded in scope, with monthly updates

and Internet distribution. During that time a flat file database was used. As the limitations of a flat

file database became more problematic the dataset was moved to a true DBMS (see. Codd, 1990

for specifics on the theory behind the relational database management model). PostgreSQL was

chosen as the DBMS. PostgreSQL provides an implementation of Structured Query Language

(SQL); . PostgreSQL also offers a scripting language and support for user-defined functions and

data types. PostgreSQL transactions abide by the traditional database supports both relational and

object-oriented data models (see: www.postgresql.org).

The PACRAIN database takes advantage an object-oriented data model. One such concept

is inheritance, which PostgreSQL implements. Inheritance allows a hierarchy of classes to be

created. Within this hierarchy, a given class will define the common characteristics that are shared

by all classes that are derived from it. All PACRAIN database records have metadata associated

with them. These metadata fields are placed in a table (class) from which other database tables are

derived. The derived tables will then automatically contain the metadata fields, and any changes

made to the parent table will be reflected in the derived tables. Also, a reference to a parent table

in a query can collectively refer to all of its descendants as well.

In addition to the PostgreSQL DBMS, the PACRAIN database uses a variety of custom

applications. Custom interfaces have been created which allow C++ applications to interact with the

PACRAIN database. These interfaces aid the development of database applications in several ways.

For example, database tasks such as inserting new records, modifying existing records, and

performing queries are abstracted by the interface. All database-specific code is confined to the

interfaces rather than being scattered throughout the client applications. Changes to the interface


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can be made without requiring modifications of the client programs. The C++ interface is composed

of three layers, all of which use an object-oriented design. The first layer interacts directly with the

DBMS, and is built on top of the PostgreSQL C API. The second layer uses SQL to provide generic

relational database functionality for performing queries and modifying database records. The third

layer is a collection of PACRAIN data objects, such as observation sites and rainfall records.

Finally, there is an updated web-based user interfaces for retrieving PACRAIN data. This uses basic

HTML forms in conjunction with CGI (Common Gateway Interface), which minimizes the

requirements for the client.

V. Enhancement of the Pacific Raingauge Database

In addition to the enhancements to the user interface and database management system of

the PACRAIN database described above, the number of records within the database continues to

expand. This includes, of course, more observations over time, but also includes additional values

through a continual addition of an expanded network of stations. This is accomplished with respect

to historical data by working with the local meteorological offices to examine archived paper records

in an attempt to find additional locations not digitally recorded. This data archeology continues

to expand the historical number of stations available in the database. The PACRAIN database has

also continued to expand and enhance the existing precipitation recording networks within the

pacific. For example, a recent collaborative effort between PACRAIN personnel and the

meteorological director of Niue Island in the south Pacific has resulted in the placement of five new

manual read gauges spanning the island. In addition, ten raingauges and related material have been

sent to the meteorological director of the country of Tuvalu. More recently, six raingauges have

been sent to the Cook Islands Ministry of Education to expand their network. A collaboration with

the government of Kiribati has resulted in a network of 15 new raingauges located on 15 atolls

managed by the Kiribati Meteorological Service, and a similar effort has resulted in the recent

placement of 100 raingauges in Vanuatu. This last collaboration is a direct result of efforts made

by the Schools of the Pacific Rainfall Climatology Experiment (SPaRCE) program.

Beyond the list of raingauges used to expand the daily observing capacity of the pacific

rainfall network, there have also been a series of efforts to expand the network to include more high


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time resolution gauges across the islands of the Pacific. Thus, ten tipping bucket rain gauge sites

have been set up in various Pacific countries and data from these countries are currently being

received. These gauges have the capacity to record rainfall down to a temporal resolution of tip time

(e.g., sub-minute resolutions). Finally, 55 new tipping buckets gauges have been donated by EVAC

for an expansion of the PI-GCOS instrumentation project. These instruments will be sent out to the

various meteorological directors of the islands, and the observations will be recorded and stored in

the PACRAIN database.

VI. Illustrative Applications

In order to show the potential usefulness of the PACRAIN database, two examples of

current research currently being undertaken are included here. These examples are an analysis of

trends in extreme-event precipitation, and an exercise in validation of a satellite-derived rainfall

estimation product. The results are not intended to represent complete research manuscripts by

themselves, but are included to provide results of recent scientific research, and to illustrate the

potential research applications of the PACRAIN database.

VIA. Extreme Event Trend Analysis

As mentioned above, extremes in precipitation have important impacts on vital aspects of

environment and our society, including crop yields, power consumption and production, and human

health. In addition to the long-term trends identified and described by the Intergovernmental Panel

on Climate Change (Houghten, et al., 2001) and others, there is also the need to examine the trend

in not only the mean, but also the variability in extreme events. The first illustrative example shown

here uses the PACRAIN database, along with recent standard methodology (described below) to

illustrate the overall changes in extreme precipitation events across a wide region in the tropical

pacific.

For this analysis, a strict criteria of at least 99% data completeness of daily data for the time

period under examination (the latest climate normal period of 1971-2000) was necessary for the


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station to be included in the analysis. Checks for station and instrument moves, as well as

inconsistencies in the data have also been undertaken. Those stations with complete record but

known problems in inhomogeneities were removed from the analysis. For example, some stations

which had a complete data record had a suspiciously high frequency of zero precipitation during the

weekends, as illustrated above. Finally, additional quality assurance procedures were performed.

For example, some stations showed evidence of highly unrealistic outliers. These outliers would

have dramatically skewed the interpretation of the point-specific and regional patterns in the trends

and were thus eliminated from further analysis.

A key question regarding a changing climate is are climate extremes becoming more

frequent? Recent efforts, have attempted to standardize the variables used in extreme event

analysis (Peterson et al. 2001; Nicholls and Murray 1999). For example, Nicholls and Murray

(1999) recommend a set of indicators which include the following: variations in magnitude of

particular percentile values; percentage of annual precipitation falling on days with rainfall above

a specified percentile (e.g., 95th). Thus, for this example, precipitation proportion at the 95th

percentile was included to examine the relative change in the amount and character of the heaviest

rainfall events for each location. Extreme proportion was determined as follows. First, the amount

of daily rainfall associated with the 95th percentile value for each year at each location was

computed, and then the proportion of total annual rainfall for each year that fell during those days

that exceeded the computed 95th

percentile threshold was calculated. Thus, each year for each

station has a value of the percentage of rainfall that fell during values that exceeded the 95 th

percentile threshold. The linear trend over the 30 year period was then determined and the spatial

patterns are presented in Figure 2. Although the trends are computed on a year-to-year basis,

complete daily data is needed to compute the 95th percentile value, and the associated intensity (for

example) of the days above that threshold for a given year.

Analysis of Figure 2 shows a pronounced spatial pattern to the trends in the proportion of

total rainfall produced during the heaviest rainfall days. Overall thirteen stations show an increase

in this variable, with the greatest values located in the southern portion of the study region. These

results yield a more pronounced significance when compared to analysis of overall precipitation

trends. Previous research (e.g., Griffiths, et al., 2003) has shown an overall decrease in precipitation

in this region. This pattern is likely associated with a displacement of the South Pacific


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Convergence Zone during the analysis period. There are several stations that show a decrease in the

overall rainfall, but a pronounced increase in the percent of overall precipitation that falls during the

heaviest 5% of days. The suggestion of this result is that although the overall precipitation is

decreasing, the amount of overall rainfall during the heaviest rainfall days is increasing, and

illustrates the need to examine such extreme event parameters as the one illustrated here.

VIB. Satellite rainfall Verification

The second research application involves the role of the PACRAIN database in examining

the accuracy of satellite-derived precipitation estimates over the tropical Pacific. This example

focuses on a comparison between areal-averaged estimates obtained from the PACRAIN database,

and estimates of the same locations obtained from the Global Precipitation Climatology Project

(GPCP; Adler, et al., 2003). The Global precipitation climatology project (GPCP) was established

by the World Climate Research Program in 1986 with the initial goal of providing monthly mean

precipitation data on global scale at a 2.5 2.5latitude -longitude resolution. The data now

includes 11 daily data set starting in 1997 in addition to the monthly product. The GPCP

produces its estimates through a merger of infrared and microwave satellite estimates of

precipitation with rain gauge data. However, large uncertainty is associated with satellite

precipitation estimates, stemming from unknown variation in space and time of the physical and

statistical relationships between precipitation and satellite-sensed radiance. To mitigate this, satellite

algorithms must be calibrated and verified using surface precipitation sampled from different climate

regimes and seasons. Thus, data from the PACRAIN database have been compared to the GPCP

merged product. Of the 800+ stations from the PACRAIN database initially analyzed, the first

concern was the potential effect of orography and land-ocean temperature gradients on the

representativeness of island rain gauge measurements of open ocean conditions. Because of the

questionable representation of large-island rainfall measurements, and since it has been shown that

atoll rainfall data can be a good estimate of open-ocean conditions (Lavoie, 1963; Morrissey, et al.,

1994) only data from atolls were used in this study. For this illustrative example, totals for 2.5 x

2.5 boxes were calculated by simply areal averaging all the stations within each box.


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Figures 3-6 show the results of the satellite verification analysis. Figures 3 and 5 show a

scatterplot of the comparison between monthly values of the GPCP version 2 product and a atoll

raingauge derived average for two different locations. Figure 3 is located at 5 degrees N latitude,

142.5 degrees E longitude (the values represent the southwest corner of the box). The PACRAIN

data for the box consists of combination of data obtained from NCDC (Woleai Atoll), and from the

SPaRCE program (Eaurpik Elementary School; Tagailap Elementary School; and Falalop, Woleai).

This combination of US and SPaRCE data shows how different data sources and types, once

properly normalized, can be use to together to examine research issues. The location for Figure 5

is 5 degrees N latitude, 170 degrees E longitude. The stations for this box were obtained from a

combination of the US sites and NZ sites on Majuro and Arno atolls. Figures 4 and 6 show a

monthly time series of the error and bias for each location. The results illustrate that the satellite

typically overestimates low values of precipitation and underestimates high values. This result is

a common feature in analysis of satellite data in the tropical Pacific (Greene and Morrissey, 2000).

These figures also illustrate the seasonal and interannual difference between the satellite and gauge

estimates of areal-averaged precipitation. For example, Figure 6 shows that error field is typically

highest in January and closer to zero during June-July. A similar pattern is noted for Figure 4.

While the overall month-to-month average difference between the satellite and gauge estimates are

small, the difference in both sign and amount varies significantly across space and time. Further

examination of these fields can be related to ENSO patterns, etc.

VII. Summary and Conclusion

This paper set out to describe and illustrate the usefullness of the Comprehensive Pacific

Rainfall Database (PACRAIN), which contains daily and monthly precipitation records from the

tropical Pacific basin. The database includes data from a range of sources, including, the Schools

of the Pacific Rainfall Climate Experiment (SPaRCE), that is unique to PACRAIN. SPaRCE is a

collaborative effort to engage students and teachers from Pacific Island nations in the collection and

use of precipitation data. Recent enhancements to the PACRAIN database include: improved

quality control and observation and data entry standardization, expansion of the network, increased

collaboration with local meteorological directors, and enhanced high-resolution data. The expansion,


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under the auspices of the Pacific Islands Global Climate Observing System program, is an attempt

to further fill in the gaps in observations, and also to provide capacity building and technology

transfer to Pacific Island nations.

To illustrate the potential usefulness of the data, two examples of research using the

PACRAIN database have been discussed. The first is an analysis of temporal changes in the

extreme event characteristics of daily precipitation, the second is a satellite-raingauge comparison.

The severity and occurrence of precipitation events is an important component when examining the

impact of climate change. Results show that there does appear to be a significant shift in the overall

pattern of extreme events in this paper as illustrated by the analysis of the proportion of total

rainfall produced by the highest 5% of days. These two examples are not intended to be

exhaustive; rather, they are included to illustrate the range of research opportunities that are

available to researchers who choose to download and use the Comprehensive Pacific Rainfall

Database.


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Climate. International Journal of Climatology 21: 1705-1721.

Taylor, R. C., 1973: An atlas of Pacific island rainfall, Dep. of Meteorol. Publ., 25, University ofHawaii, Honolulu.


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List of Tables and Figures

Table 1. Data distribution by frequency, terrain classification, and source

Table 2: Example Quality Control analysis: Day of Week Bias

Figure 1: Location of Stations in Pacrain database

Figure 2: Example of research: Trend analysis of Extreme Events using Daily Data

Figure 3: Example of research: Satellite verification





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Table 1

Source Record

s Sites

Fiji 81105 4French

Polynesia 92986 21Japan 1956 15

New Zealand 734452 188

SPaRCE 89104 166

TA 39032 118

US 891057 320

Terrain Records SitesAtoll 627799 150

Coastal 677524 251

Coastal Orographic 504442 269

Other 119927 162

Frequency Records Sites

Daily 1888704 699

Monthly 40988 133

TABLE 2

Sun Mon Tues Wed Thurs Fri Sat (Number of observations)

759 831 828 826 811 820 754

Sun Mon Tues Wed Thurs Fri Sat (percent of observations that are zero)

0.72 0.26 0.41 0.38 0.45 0.43 0.72

Sun Mon Tues Wed Thurs Fri Sat (Mean rainfall (mm))

78.4 308.9 170.6 159.6 133.5 138.2 80.2


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pacrain journal article

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