sensitivity of meteorological high-resolution numerical...

Sensitivity of meteorological high-resolution numerical

simulations of the biggest floods occurred over the Arno river

basin, Italy, in the 20th century

Francesco Meneguzzoa,*, Massimiliano Pasquia, Giovanni Mendunib,1,Gianni Messeric,2, Bernardo Gozzinia, Daniele Grifonia, Matteo Rossic,2,

Giampiero Maracchia

aInstitute of Biometeorology, National Research Council (IBIMET-CNR), Via Caproni 8, I-50145 Firenze, ItalybArno River Basin Authority, Firenze, Italy

cLaboratory for Meteorology and Environmental Modeling (LaMMA-Regione Toscana), Firenze, Italy

Accepted 20 November 2003

Abstract

During recent years quantitative precipitation forecasts (QPFs), based on NWP models, were continuously increasing their

performances and accuracy especially for light and moderate precipitation thresholds; deterministic verification suggests that

for high thresholds there wasn’t a similar improvement. High thresholds and rare events are particularly difficult to handle and

this is a strong limitation for operational activities, particularly for flood forecasting. Where rainfall occurs, when it happens and

how much it will be, are information that depends on how the used numerical model is able to determine the size, scale and the

evolution of atmospheric systems involved. In this study numerical meteorological simulations of the most important floods

occurred along the Arno river basin, Italy, in the 20th century, performed by the Regional Atmospheric Modeling System

(RAMS), are analysed with regard to the sensitivity to geometrical and initial conditions.

RAMS is presently used to produce operational QPFs in an integrated hydro-meteorological forecasting system for the Arno

river basin. The flood events occurred in November 1966 (100-years estimated recurrence, which caused several deaths and

catastrophic damages to public and private goods and to the unique artistic and cultural heritage) and in October 1992 (30-years

recurrence flood, with extensive damage). The advanced modules for the parameterisation of surface–water–atmosphere

exchanges and for cloud and precipitation microphysical processes, included in RAMS, allow the explicit and likely

representation (triggering and evolution) of the cloud and precipitation systems, which make this model a good candidate for

such sensitivity analyses. The simulations of the floods occurred over the Arno river basin were initialised by means of the

NCEP/NCAR global reanalysis data, and the sensitivity of the forecasts to the spatial horizontal and vertical resolution, the

representation of the sea surface temperature and the initialisation time is verified against ground data.

q 2003 Elsevier B.V. All rights reserved.

Keywords: Numerical weather prediction; Quantitative precipitation forecasts; Forecasts verification; Sea surface temperature; Atmospheric

reanalyses; Floods

0022-1694/$ - see front matter q 2003 Elsevier B.V. All rights reserved.

doi:10.1016/j.jhydrol.2003.11.032

Journal of Hydrology 288 (2004) 37–56

www.elsevier.com/locate/jhydrol

1 Fax: þ39-55-26743250.2 Fax: þ39-55-8969521.

* Corresponding author. Fax: þ39-55-308910.

E-mail addresses: [email protected] (F. Meneguzzo), [email protected] (G. Menduni).

http://www.elsevier.com/locate/jhydrol

1. Introduction

Early flash flood warnings are needed to improve

the effectiveness of civil protection efforts to mitigate

damages and save lives. This is especially true for

small to medium size basins and sub-basins where the

response times to the rainfall inputs are of the order of

few hours or less and even marginal anticipation is

important and needed.

This is the case of the Arno river basin, Italy,

whose size is approximately 9200 km2 (Fig. 1) and

several relevant sub-basins along the main course

and tributaries (Fig. 3a), whose areas are less than

1000 km2.

As part of the effort to produce continuous

accurate flood forecasts along the Arno river and

its tributaries, the Arno River Basin Authority has

sponsored since year 2000 the verification, improve-

ment and operational application of the Regional

Atmospheric Modeling System (RAMS) as an

effective quantitative precipitation forecast (QPF)

tool to feed hydrological (flood) models in the frame

of the ARTU (Arno–Tuscany and Umbria Regions)

project (http://www.arno.autoritadibacino.it).

The ARTU project consists of a multi-agency

approach to flood forecasting on the basis of extensive

real-time meteorological (precipitation, temperature)

and hydrometric (water level, discharge) monitoring,

meteorological quantitative forecasts (and expert

assessments) and hydrological models (a rainfall-

threshold nomogram and a distributed hydrological-

hydraulic prediction), integrated in a homogeneous

Geographical Information System. Fig. 2 shows the

home page of the ARTU, while a comprehensive

description of the system can be found in Castelli et al.

(2002), and Mazzetti et al. (2003).

The authors have developed and refined the

numerical atmospheric forecasting techniques based

on model RAMS also during two EU Projects:

satellite data assimilation in EURAINSAT (‘Euro-

pean satellite rainfall analysis and monitoring at the

geostationary scale’, an EC research project co-

funded by the Energy, Environment and Sustainable

Development Programme within the topic

Fig. 1. Location of the Arno basin (dark green boundaries) in southern Europe.

F. Meneguzzo et al. / Journal of Hydrology 288 (2004) 37–5638

http://www.arno.autoritadibacino.it

‘Development of generic Earth observation technol-

ogies’, Contract number EVG1-2000-00030. Web

site: http://www.isac.cnr.it/~eurainsat), and connec-

tion with hydrologic predictions in MUSIC (‘Multi-

sensor precipitation measurements integration, cali-

bration and flood forecasting’, a research project

also co-funded by the EC under the Energy,

Environment and Sustainable Development Pro-

gramme within the topic ‘Development of generic

Earth observation technologies’, Contract number

EVK1-CT-2000-00058. Web site: http://www.

geomin.unibo.it/orgv/hydro/music/).

In both these Projects and other Italian national

projects, extensive use has been made of data

collected during the Mesoscale Alpine Programme

Intensive Observing Observation Period in autumn

1999 (Bougeault et al., 2001).

In this work the identification of the best

configuration of the RAMS model is described with

respect to the predictability of the rainfall associated

with the two main floods occurred along the Arno

river in the 20th century.

It is also found that the predictability depends

critically on the horizontal and vertical resolution of

the model and that, in particular, very high horizontal

resolution is needed. A strong dependence arises also

with regard the representation of the sea surface

temperature (SST). The need to export such findings

to the operational forecasting chain in regional

forecasting centres has led to the use of low cost,

high performance computer systems.

2. The RAMS model

The RAMS, version 4.4, is used for the operational

mesoscale weather forecasts at the Regional Meteor-

ological Service of Tuscany, Italy (Meneguzzo et al.,

2002; see also: http://www.lamma.rete.toscana.it/

rams-web/e_index.html), and has been used for all

the case studies described in the following.

RAMS was originally developed in the early 1970s

essentially as a research tool; now it is widely used both

for research and operational forecast purposes in many

operational centres around the world. Since the early

1990s a large number of improvements have been

introduced both as regards the physics (mainly moist

and land surface processes) and the computational

point design (new numerical schemes and parallel

computing). A general description of the model can be

found in Pielke et al. (1992), while the current status and

future perspectives of RAMS can be found in Cotton

et al. (2003).

RAMS today represents the state-of-the-art in

atmospheric numerical modeling being continuously

improved on the basis of multi-disciplinary work both

at Colorado State University and at several other

research and operational centres worldwide. Numeri-

cal modellers, meteorologists, computer experts,

remote sensing experts, geographers, agronomists,

forest experts and geologists participate to the

development of the various components of RAMS.

2.1. Initialisation; objective analysis

RAMS needs a set of atmospheric data analysed

over the domain grid and at its boundaries both at the

initial time of the simulation and at future times.

Several data types can be combined and processed by

an isentropic analysis package. The isentropic co-

ordinates have several advantages as regards to other

co-ordinate systems; since the synoptic scale atmos-

pheric flow is to a first approximation adiabatic, an

objective analysis is performed over an isentropic

surface, which will ‘pack’ in frontal areas, thus

providing a higher resolution description of the

discontinuities.

The Barnes (1973) objective analysis scheme is

used for wind, pressure and relative humidity. The

local surface observation data over land and water can

be assimilated and blended with other upper air

gridded data and observations according to the

vertical atmospheric stability.

2.2. Basic equations

The basic equations of RAMS are three dimen-

sional, non-hydrostatic, compressible and time-split in

an horizontal rotated polar-stereographic transform-

ation and vertical terrain following height. The non-

hydrostatic formulation and the large number of terms

retained in the RAMS equations allow in principle any

spatial horizontal resolution, which has made appli-

cations from planetary climate to tunnel wind possible.

F. Meneguzzo et al. / Journal of Hydrology 288 (2004) 37–56 39

http://www.isac.cnr.it/~eurainsat

http://www.geomin.unibo.it/orgv/hydro/music/

http://www.geomin.unibo.it/orgv/hydro/music/

http://www.lamma.rete.toscana.it/rams-web/e_index.html

http://www.lamma.rete.toscana.it/rams-web/e_index.html

2.3. Microphysics

The representation of cloud and precipitation

microphysics in RAMS includes the treatment of

each water species (cloud water, rain, pristine ice,

snow, aggregates, graupel, hail) as a generalised

Gamma distribution. The scheme allows hail to

contain liquid water and contains the description of

the homogeneous and heterogeneous ice nucleation,

and the ice size change by means of vapour deposition

and sublimation (Walko et al., 1995).

The initial number concentration Nt of cloud

droplets and ice crystals is computed in RAMS as a

function of the number concentration of cloud

condensation nuclei (CCN) and ice freezing nuclei

(IFN), respectively, and other environmental physical

quantities. The fraction of nucleated CCN (i.e. the

concentration of cloud droplets) is obtained from a

lookup table as a function of the CCN number

concentration, the vertical velocity and the tempera-

ture, and the current local values in RAMS; the lookup

table has been generated offline from a detailed

parcel-bin model. The ice crystal nucleation is

obtained from simple formulas of deposition nuclea-

tion and condensation freezing, where the nuclei to be

activated are the IFN or CCN according to the

environmental temperature; further relevant processes

for ice crystal nucleation are the contact nucleation

(IFN colliding with cloud droplets at temperatures

below freezing) and the homogeneous freezing of

cloud droplets at temperatures below 230 8C.

The most important quantity to be defined at the

initial stage of the simulation is the number

concentration of CCN, especially in the relatively

warm environments of the case studies analysed in

this work.

The number concentration of the CCN was defined

horizontally homogenous at 3 £ 108 m23, such value

being representative of a marine environment with

some continental contamination (Rosenfeld and

Khain, 2003); no attempt was made to provide

a horizontal distribution of the number concentration

of CCN.

A very efficient solution technique is available for

the stochastic collection equation and a new technique

for the prediction of sedimentation or precipitation of

hydrometeors, allowing the definition of the fall

velocity on the basis of the gamma size distribution

(Walko et al., 1995).

2.4. Representation of cumulus convection

The cumulus convection parameterisation in

RAMS (Tremback, 1990), which is still to be applied

to coarse grids (over 15 km horizontal spatial resol-

ution) is relatively simple. This reflects on one side the

greater effort spent towards the explicit representation

of convective processes at very high resolution, and on

the other the consideration that simple cumulus

convection schemes allow more straightforward

assimilation of non-standard data such as cloud and

precipitation observations (diabatic initialisation).

The scheme implemented in RAMS is a general-

ised form (Molinari, 1985) of the Kuo (1974)

equilibrium scheme.

The equilibrium schemes assume that the convec-

tion is fed by the consumption of the conditional

instability, with a speed equal to its production by the

processes occurring at the grid mesh size scale.

Convection can be generated by a large number of

different mechanisms, comprising frontal and oro-

graphic forcing, surface excessive heating, surface

heterogeneities and gravity waves, and others, often

after their interaction with pre-existing atmospheric

boundary layers (either surface based or elevated). A

comprehensive reference to atmospheric convection

is provided by Emanuel (1994), while a discussion of

the role of gravity waves to stimulate cumulus

convection can be found in Lac et al. (2002).

The cumulus convection schemes deal with the

convection dynamics as a sub-grid process, i.e. not

explicitly solved by the equations of motion.

This imposes a lower limit to the model resolution

enhancement, since at high resolutions a convective

process might span more grid nodes, thus under-

mining the sub-grid hypothesis. The cumulus con-

vection parameterisation is turned off in the very high-

resolution grids.

2.5. Representation of land surface processes

The surface heterogeneities connected to the

vegetation cover and the land use are assimilated

and represented in great detail in RAMS by means of


the LEAF-2 (Land Ecosystem Atmosphere Feedback)

model.

LEAF-2 (Walko et al., 2000) represents the vertical

exchange of water and heat in several soil layers,

including the effects of freezing and melting, the

temporary water and snow cover, the vegetation and

the canopy air. The surface domain meshes are further

sub-divided into patches, each identified by a separate

vegetation cover and land use, soil type and initial soil

moisture.

The balance equations for soil energy and

moisture, surface water, vegetation and canopy air,

and exchange with the free atmosphere, are solved

separately for each patch. A hydrological model

based on the Darcy law for the lateral downslope

water transport exchanges the moisture in the sub-

surface saturated layers and the surface runoff. The

advantages of the two-way coupling of a land

surface model, whose hydrology is similar to LEAF-

2 and to TOPMODEL (Beven and Kirkby, 1979),

with a mesoscale atmospheric model, has been

recently discussed by Seuffert et al. (2002). They

showed a distinct beneficial impact of the two-way

coupling on the boundary layer quantities and the

precipitation.

LEAF-2 assimilates standard land use data sets to

define the prevailing land cover in each grid mesh

and possibly the patches, then parameterises the

vegetation effects by means of biophysical

quantities.

2.6. Computational system and computing

performances

Real-time numerical weather prediction models

are amongst the computationally most demanding

systems in existence so far, considering the time

limits imposed to their execution. Using a complete

set of Navier–Stokes equations along with complex

numerical schemes for soil–vegetation–air inter-

actions and microphysical precipitation description

enlarge the NWP computational demands. However,

since few years, parallel computational systems

based on the Linux operating system are becoming

popular and today indeed represent ‘high perform-

ance–low cost’ systems. Such parallel architectures

are also an alternative to massive parallel systems

which are characterised by very high costs.

The fundamental scheme is called Beowulf-like

and it comprises a number of PCs, called also

Nodes, connected through a switch together within a

local area network.

The number of operations needed, during an

atmospheric simulation, is distributed among avail-

able cluster nodes, thus reducing the total amount of

overall time needed. A more detailed description of

the system in use can be found in Soderman et al.

(2003).

3. Data and methods

The RAMS model is used today worldwide for

research and operational forecast services; in

particular, in the Mediterranean it is executed for

regular mesoscale high-resolution atmospheric pre-

dictions, initial and boundary conditions being

provided by the global atmospheric and surface

fields produced operationally by the European

Centre for Medium-Range Weather Forecasts and

the National Center for Environmental Predictions

(NCEP/NOAA).

For the case historic case studies which are the

subject of this work, the NCEP/NCAR reanalyses

global atmospheric and surface fields were used

(Kalnay et al., 1996; Kistler et al., 2001. See also:

http://www.cdc.noaa.gov), which ensure a relative

homogeneity of the quality of initial and boundary

fields due to the same analysis and assimilation

technique used for the reanalysis period since 1948.

Such homogeneity is of course limited by the data

available for assimilation.

The meteorological situations occurred during

the flood events of 3–4 November 1966 and 30–31

October 1992 are simulated and quantitatively

analysed in terms of QPFs over the Arno

river basin, located in central Italy (Fig. 1) and

area approximately 9200 km2. The verification of

the RAMS’ QPF is performed over the whole Arno

river basin and relevant sub-basins, of area

1000 km2 or less (Fig. 3a). The thresholds of the

mean areal QPF for floods to occur over few sub-

basins of the Arno river basin, are listed in Table 1

for three different antecedent soil moisture con-

ditions (as defined in McCuen, 1998), together other


http://www.cdc.noaa.gov

relevant features (size, maximum discharge), as

derived by Mancini et al. (2002).

The distribution of the rain gauges used for the

QPF verification in the November 1966 case is shown

in Fig. 3b; the same distribution for the October 1992

case study in shown in Fig. 3c.

Two to four grids were used for the RAMS

simulations, whose horizontal resolution assumes

the values 80, 16, 3.2, 1.6 km, and are shown, together

the respective model orography, in Fig. 4. Each grid is

two-way nested in the horizontal into the next larger

grid, which means that there is a continuous data

exchange at the lateral boundaries of the smaller grid.

The integration time steps increase with mesh size,

and the data exchange happens at the frequency

defined by the larger time step; dynamic, thermodyn-

amic and water quantities are downscaled and

upscaled in a seamless environment, preventing any

relevant edge effects at the boundaries of the nested

domains. The convective parameterisation is turned

on in the two coarser grids (resolutions 80 and 16 km)

and is switched off in the finer grids (resolutions 3.2

and 1.6 km).

Table 1

Relevant features and rainfall thresholds for few sub-basins of the

Arno river basin

Duration (h) Basin features Sieve Casentino Arno

(upstream

florence)

Maximum

discharge (m3/s)

550 600 1500

Size (km2) 831 738 3900

Soil status Rainfall threshold for flood

(mm)

3 AMC I 90 138 96

AMC II 58 79 59

AMC III 40 49 40

6 AMC I 96 145 97

AMC II 62 84 61

AMC III 44 53 41

12 AMC I 104 157 102

AMC II 69 94 64

AMC III 49 62 44

24 AMC I 123 187 114

AMC II 86 120 75

AMC III 66 85 53

AMC ¼ Antecedent Moisture Condition.

Fig. 2. Home page of the ARTU project, Arno River Basin

Authority.

Fig. 3. (a) Relevant sub-basins of the Arno river basin, where

specific QPF verification was performed; (b) distribution of rain

gauges used for the QPF verification in the November 1966 case

study; (c) distribution of rain gauges used for the QPF verification in

the October 1992 case study. Lat-lon geographical co-ordinates.


The vertical levels coincide with each other

between the domains, implying no vertical nesting.

The levels are defined starting from the lowest one,

lying just under the bottom surface, with a vertical

stretching factor greater than 1, resulting in a finer

description of the boundary layer and a coarser

description of the upper troposphere. Different values

for the thickness of the lowest layer and the stretching

factor are used along with different numbers of levels,

while approximately conserving the depth of the

domain. Three vertical structures are used to

assess the model sensitivity, with 26, 36 and 50

vertical levels, lowest spacing 80, 50 and 40 m,

respectively, and stretching rations 1.2, 1.125

Fig. 4. Horizontal domains of the RAMS simulations of the floods occurred in November 1966 and October 1992. Spatial horizontal resolutions

are: Grid 1 ¼ 80 km; Grid 2 ¼ 16 km; Grid 3 ¼ 3.2 km; Grid 4 ¼ 1.6 km.


and 1.08, respectively. The heights of the vertical

levels are shown for each different configuration (26,

36 and 50 vertical levels) in Fig. 5.

For the purposes of the quantitative verification,

the observed and forecast precipitation over a target

area, either the Arno basin or a sub-basin, is obtained

by means of a three-step technique. First, only the

RAMS’ meshes where at least a rain gauge is located

are retained for the comparison. Second, the hourly

observed precipitation over each of such meshes is

computed as the simple average of the available

observations, which can be compared to the RAMS’

QPF. Third, the comparison is performed over the

average of the hourly observed and forecast precipi-

tation over all the available meshes.

4. Case studies

4.1. 100-year flood: November 1966

At 00 UTC on November 2, 1966 a high-pressure

belt extended over a great part of the Atlantic Ocean,

the British Isles, Scandinavia, and east to Russia (peak

pressure of 1044 hPa).

Over central-southern Europe a low pressure field

dominated, with a relatively organised structure over

the Gulf of Biscay. The main cyclonic westerlies over

the Atlantic were temporarily confined to far northern

latitudes. In the mid-troposphere a wide trough

extended from Scandinavia to the Iberian Peninsula,

associated with very cold air masses and distinct cold

advection over the Mediterranean.

On November 3 the situation evolved to greater

vorticity in the trough, also as a consequence of the

vorticity advection by the jet stream from very

northern latitudes, and the deepening of the surface

vortex. The trough axis started rotating anti-clock-

wise, triggering a strong elevated heat wave over

central Mediterranean and a ridge over Balkans and

eastern Europe. Large scale ascent also developed

west of Italy.

At 00 UTC on November 4, the north Atlantic

very cold air masses reached western Mediterra-

nean, making the (deepening) cyclonic vortex over

central-western Mediterranean baroclinically

unstable. The warm advection over eastern Italy

and further east increased, leading to a fast increase

of the geopotential. A blocking situation rapidly

developed (1032 hPa peak pressure over Balkans).

Starting from early afternoon on November 4, the

surface low weakened after the baroclinic instability

flux diminished. Thermal advection rapidly weakened

too, and an elevated weak ridge grew over western

Mediterranean. Vertical descent developed over

western Italy during November 4. The atmospheric

synoptic evolution is displayed in Fig. 6.

The precipitation which fell during the event was

very high, reaching more than 300 mm in Tuscany

(central-western Italy) and more than 500 mm in

north-eastern Italy; the rain volumes over large areas

were also extraordinary. Over the Arno basin, the

highest precipitation fell during November 3, after-

noon, and cumulated precipitation averaged to about

120 mm in the period 12 UTC November 3–12 UTC

November 4 (Fig. 7).

Seven simulations were executed, a ‘control’ one

with three nested grids, starting on November 3, 1966

at 12 UTC, and the other ones starting at the same

time, 12 and 24 h before with different geometrical

settings in the horizontal and in the vertical. The

relevant features are explained in Table 2.

The QPF was compared with the observed

precipitation, as resulting from the interpolation of

observations at about 30 rain gauges, for the period 3-

Nov-1966 12 UTC to 4-Nov-1966 12 UTC. The

observed rainfall field is shown in Fig. 8.

The QPFs fields produced by the RAMS’ simu-

lations, over the respective finest resolution grid, in

the same period of the observations, are shown in

Fig. 9. For simulations started before 3-Nov-1966 12

UTC, only the rainfall forecasts for the comparison

period were considered. The precipitation field

produced over a larger area by the NCEP/NCAR

reanalyses, for the same period, is plotted in Fig. 10.

The QPFs produced by the RAMS’ simulations in

their respective finest resolution grids and averaged

over the Arno river basin and four sub-basins, are

directly compared to the observations in Figs. 11 and

12. For simulations started before 3-Nov-1966 12

UTC, only the respective rainfall forecasts for the

comparison period were considered.

The validation of the QPFs vs the rain

gauges provides some guidance to produce

accurate quantitative forecasts, as shortly summa-

rised below.


Fig. 5. Vertical structures of the RAMS meteorological simulations of the floods occurred in November 1966 and October 1992. The heights of

the vertical levels are shown for each different configuration (26, 36 and 50 vertical levels).

Fig. 6. 500-hPa geopotential heights at 00 UTC on November 3 (a), November 4 (b) and November 5 (c), 1966.


† The November 1966 event was a large scale one,

spanning the Atlantic, central/western Europe,

Mediterranean and northern Africa, and the

precipitation systems covered hundreds of

kilometres.

† The NCEP/NCAR reanalyses QPF (resolution

2.58 lat-lon) shows a relevant eastward shift,

probably due to the model orography, and great

underestimation, since the areal mean over

regions as large as the Arno river basin (about

2 grid meshes of the Reanalyses domain) doesn’t

exceed 35 mm (Fig. 10), against about 120 mm,

as computed from the observations (Fig. 7).

† The simulations starting 3-Nov-1966 at 12 UTC

show, both in the rainfall fields (Fig. 9) and in

the charts for the Arno river basin (Fig. 11) and

sub-basins (Fig. 12), a relevant impact of the

increase of the number of vertical levels from 26

to 36, while no distinct effect is produced when

such number is further increased to 50.

† The same simulations show a relevant impact of the

increase of resolution from 16 (second grid) to

3.2 km (third grid), but not from 3.2 to 1.6 km.

† The other simulations show a very limited impact

from the 12-h anticipation of the initialisation,

which produces the largest differences mostly

outside the Arno river basin (Fig. 9), and a dramatic

impact from the 24-h anticipation (simulation

named Lev36Ini(-24)_G2, which should be com-

pared with the other simulations with only two

grids: Lev36_G2).

† Underestimation is apparent in all simulations,

mostly concentrated in the early 6 – 9 h of

the period. The model spin-up has probably a

role for simulations starting 3-Nov-1966 at 12

UTC, but not for the others. It is hard to

distinguish the impacts of spin-up and initialisa-

tion time; for simulations starting 3-Nov-1966 at

12 UTC, the spin-up seems to act during the

forecast range up to þ6 h or at most þ 9 h. The

underestimation is also mostly contributed from

the Casentino and Upper Arno Valley sub-basins

(Figs. 3 and 12), which show the greatest

orographic complexity and presumably the high-

est sensitivity to fine-scale features of the flow.

† Overall, the RAMS’ best QPFs succeed in

representing about 70% of the rainfall over the

Arno river basin, ranging from 50% over Upper

Arno Valley and 80% over Sieve Valley, which

are much higher than the respective values

displayed by the NCEP/NCAR reanalyses

(around 30%).

It is shown elsewhere (Soderman et al., 2003) that

the simulations started 3-Nov-1966 12 UTC allowed

very accurate distributed hydrological flood forecasts

at least six hours before, at the Florence river section

(downstream of sub-basins A–D in Fig. 3a), than

accounting only for the rainfall observations at the

rain gauges.

Recalling the range of rainfall thresholds for few

sub-basins of the Arno river basin, listed in Table 1, and

the charts in Figs. 11 and 12, it appears that the

observed precipitation is everywhere higher than the

rainfall thresholds for floods for any antecedent soil

status, while RAMS’ QPFs, albeit the underestimation,

Table 2

Summary of features of the RAMS simulations for the November 1966 case study

Simulation name Spatial horizontal resolution (km) Number of vertical levels Initialisation

G1 G2 G3 G4

Lev36_G3 80 16 3.2 36 03-Nov-1966, 12 UTC

Lev36_G4 80 16 3.2 1.6 36 03-Nov-1966, 12 UTC

Lev50_G3 80 16 3.2 50 03-Nov-1966, 12 UTC

Lev26_G3 80 16 3.2 26 03-Nov-1966, 12 UTC

Lev36Ini(-24)_G2 80 16 1.6 36 02-Nov-1966, 12 UTC

Lev36_G2 80 16 36 03-Nov-1966, 12 UTC

Lev36Ini(-12)_G3 80 16 3.2 36 03-Nov-1966, 00 UTC

Gn, horizontal resolution of nth grid, in kilometres; Initialisation, initialisation time of the simulation.


are higher than the respective rainfall thresholds at

least for soil as dry as described by the AMC II

condition (McCuen, 1998).

4.2. 30-year flood: October 1992

A sustained south-westerly air flow, running a long

path from southern north-Atlantic, occurred over

Mediterranean and Italy on October 30th, when a

baroclinically and orographically induced low devel-

oped downwind Iberian Peninsula. Cold air penetrated

far western Mediterranean during the 36 h period and

the flux became more cyclonic (warm advection).

Large scale vertical ascent developed over western

Mediterranean and central-northern Italy. Such evol-

ution is displayed in Fig. 13.

Fig. 7. Average precipitation over the Arno river basin (hourly and cumulated) from 12 UTC—3/11/1966 to 12 UTC—4/11/1966.

Fig. 8. Observed rainfall distribution over the Arno river basin in the period November 03, 1966 at 12 UTC to November 04, 1966 at 12 UTC.


Fig. 9. Validation of RAMS QPFs: forecast rainfall distributions in the period November 03, 1966 at 12 UTC to November 04, 1966 at 12 UTC.

(a) Lev36_G2; (b) Lev36Ini(-24)_G2; (c) Lev36_G3; (d) Lev36Ini(-12)_G3; (e) Lev50_G3; (f) Lev50_G3; (g) Lev36_G4 (see Table 2 for

respective features).


The precipitation which fell during the event was

very high, averaging to about 100 mm over the Arno

river basin (Fig. 14), with up to 150 mm in the Upper

Arno Valley sub-basin.

Seven simulations were executed, a ‘control’ one

with three nested grids and climatological SST,

starting on October 30, 1992 at 00 UTC, and

the other ones starting at the same time and 24 h

before, with different geometrical settings in the

horizontal and in the vertical, climatological and

observed SST (both at resolution 18 lat-lon). Every

grid in all simulations had 36 vertical levels. The

relevant features are explained in Table 3.

The QPF was compared with the observed

precipitation, as resulting from the interpolation of

observations at about 30 rain gauges, for the period

30-Oct-1992 00 UTC to 31-Oct-1992 12 UTC. The

observed rainfall field is shown in Fig. 15.

The QPFs fields produced by the RAMS’ simu-

lations, over the respective finest resolution grid, in

the same period of the observations, are shown in

Fig. 16. For simulations started before 30-Oct-1992

00 UTC, only rainfall forecast for the comparison

period was considered. The precipitation field pro-

duced over a larger area by the NCEP/NCAR

reanalyses, for the same period, is plotted in Fig. 17.

The QPFs produced by the RAMS’ simulations in

their respective finest resolution grids and averaged

over the Arno river basin and four sub-basins, are

directly compared to observations in Fig. 18a and b.

For simulations started before 30-Oct-1992 00 UTC,

only rainfall forecasts for the comparison period were

considered.

Fig. 10. QPF from the NCEP/NCAR reanalyses: forecast rainfall

distributions in the period November 03, 1966 at 12 UTC to

November 04, 1966 at 12 UTC.

Fig. 11. Validation of RAMS QPFs: comparison of observed vs forecast average rainfall over the Arno river basin. Obs, observations; other

acronyms in Table 2.


The validation of the QPFs vs the rain gauges

provides additional guidance to produce accurate

quantitative forecasts, as shortly summarised below.

† The event was again a large scale one; no explosive

cyclogenesis occurred, and this is different from to

the 1966 event. Precipitation systems were wide-

spread, just like the vertical velocity pattern.

† In the simulations started 30-Oct-1992 00 UTC

(Fig. 18a), the effect of the increase in resolution

from 3.2 to 1.6 km produces significant and

positive effects in terms of rain volumes and partly

of rain patterns.

† In the same simulations, the use of ‘observed’ SST

instead of climatological ones, both at spatial

resolution 18 lat-lon, impacted positively only at

the highest resolution (1.6 km).

† Moderate to relevant underestimation is apparent

for all simulations started 30-Oct-1992 00 UTC

over the upper southern sub-basins: Casentino,

Upper Arno Valley (not shown); a much better

agreement is shown over the other sub-basins.

Fig. 12. Validation of RAMS QPFs: comparison of observed vs forecast average rainfall over four sub-basins of the Arno river basin: Casentino

(a), Upper Arno Valley (b), Sieve Valley (c) and Medium Arno Valley (d). Obs, observations; other acronyms in Table 2.


Fig. 13. 500-hPa geopotential heights at 00 UTC on October 30 (a) and October 31 (b), 1992.

Fig. 14. Average precipitation over the Arno river basin (hourly and cumulated) from 00 UTC—30/10/1992 to 12 UTC—31/10/1992.

Fig. 15. Observed rainfall distribution over the Arno river basin in the period October 30, 1992 at 00 UTC to October 31, 1992 at 12 UTC.


Only a limited underestimation is left for the

whole Arno river basin, particularly in the best

simulation (SSTobs_G4, Table 3). It is worth

noting that most of missing rainfall occurs in the

early simulation period (up to þ12 h from the

initialisation), while later the QPFs are in good

agreement with observations. The model spin-up

effect certainly plays a role, which seems more

serious and prolonged than in the case of

November 1966.

† The high-resolution simulation [SSTcl_Ini

(-24)_G4, Table 3] started 29-Oct-1992 00

UTC, 24 before the other simulations and before

the beginning of the rainstorm (Fig. 18b),

produced very accurate forecasts in the early

15–18 h of the target period (i.e. between 24 and

39 to 42 h after their initialisation), thus con-

firming the impact of spin-up, after that some

inaccuracies occurred, degrading the quality of

this forecast under the corresponding one, started

24 h later [SSTcl_G4, Table 3], thus showing a

loss of predictability. The final cumulated values

are anyway accurate.

† The sensitivity to spatial horizontal resolution

(Grid 4 at resolution 1.6 km, and Grid 2 at 16 km

resolution) is dramatic in the simulations started

29-Oct-1992 00 UTC, as shown both in the maps

(Fig. 16) and in the graphs (Fig. 18b). The lower

resolution simulations, almost insensitive to

the representation of SST, produce too little

rainfall, while the higher resolution simulation

provides reasonable forecasts.

† The initialisation time of the simulations appears

as a key component, as earlier initialisation

apparently overcomes the spin-up problems

experienced by the later simulations in their

first 12 h, but afterwards looses predictability,

while later initialisation suffers from initial spin-

up but afterwards adjusts to produce comparable

results. This evidence suggests the need to

perform more frequent runs in an operational

setting, e.g. every 12 h, to ensure the availability

of accurate forecasts for a sufficient lead time. Of

course, the operational initialisation by means of

higher resolution global atmospheric fields could

reduce the spin-up experienced by the mesoscale

model. Improving initialisation in the mesoscale

model, e.g. by means of diabatical data assimila-

tion (Soderman et al., 2003) could also have a

positive impact in all simulations.

† Overall, the RAMS’ best QPFs succeed in

representing reasonable rainfall over both the

Arno river basin and most of sub-basins, anyway

much better than the QPF produced by the

NCEP/NCAR reanalyses (Fig. 17), which miss

both the location and the volume of the rainfall

field, the shape being only partially reproduced.

5. Discussion and conclusions

This work analyses the sensitivity of numerical

QPFs of the most important floods occurred over

the Arno river basin, Italy, in the 20th century,

produced by means of the RAMS, to few operational

settings of the model.

Given the initialisation of the mesoscale meteor-

ological model by means of low resolution global

Table 3

Summary of features of the RAMS simulations for the October 1992 case study

Simulation name G1 G2 G3 G4 Vert. lev. SST Initialisation

SSTcl_G3 80 16 3.2 36 Clim. 30-Oct-1992, 00 UTC

SSTcl_G4 80 16 3.2 1.6 36 Clim. 30-Oct-1992, 00 UTC

SSTobs_G3 80 16 3.2 30 Obs. 30-Oct-1992, 00 UTC

SSTobs_G4 80 16 3.2 1.6 36 Obs. 30-Oct-1992, 00 UTC

SSTcl_Ini(-24)_G2 80 16 36 Clim. 29-Oct-1992, 00 UTC

SSTcl_Ini(-24)_G4 80 16 3.2 1.6 36 Clim. 29-Oct-1992, 00 UTC

SSTobs_Ini(-24)_G2 80 16 36 Obs. 29-Oct-1992, 00 UTC

Gn, horizontal resolution of nth grid, in kilometres; Vert. lev., number of vertical levels; SST, sea surface temperature: Clim., climatological

average; Obs., observed, both at resolution 18 lat-lon; Initialisation, initialisation time of the simulation.


Fig. 16. Validation of RAMS QPFs: forecast rainfall distributions in the period October 30, 1992 at 00 UTC to October 31, 1992 at 12 UTC.

(a) SSTcl_G3; (b) SSTcl_G4; (c) SSTobs_G3; (d) SSTobs_G4; (e) SSTcl_Ini(-24)_G2; (f) SSTobs_Ini(-24)_G2; (g) SSTcl_Ini(-24)_G4

(see Table 3 for respective features).


fields, the sensitivity of the QPF to the vertical and

horizontal spatial resolution, the representation of the

SST and the initialisation time is analysed by means

of the comparison against ground data, aggregated

over scales of about 800–9200 km2.

Several guidelines are derived to improve the QPF,

and particularly the meteorological input to hydro-

meteorological forecasting systems, which value

could exceed the strict concern of two, even relevant,

case studies:

† The vertical resolution is a key component, in that

more than about 30 vertical levels have to be used,

with a closer spacing in the atmospheric boundary

layer.Fig. 17. QPF from the NCEP/NCAR reanalyses: forecast rainfall

distributions in the period October 30, 1992 at 00 UTC to October

31, 1992 at 12 UTC.

Fig. 18. Validation of RAMS QPFs: comparison of observed vs forecast average rainfall over the Arno river basin, for simulations started 30-

oct-1992 00 UTC (a) and 29-oct-1992 00 UTC (b). Obs, observations; other acronyms in Table 3.


† The horizontal spacing of the meshes of the highest

resolution grids over the target areas (e.g. river

basins) shouldn’t be more than few kilometres,

2 km (or less) arising as the critical grid spacing to

ensure accurate QPF. This statement is indeed

supported by the results of the second case study

(October 1992), while the first case study allows

greater mesh spacing, probably due to the greater

length scale of the mesoscale convective system

acting in November 1966. An operational QPF

system should tune to the most stringent

requirements.

† The SST has a clear impact, here revealed in the

second case study (October 1992) at the finest

resolution used: in particular, observed values

should be used instead of climatological ones. A

further discussion can be found in Soderman et al.

(2003), and an extensive review in Pastor et al.

(2001).

† The initialisation time of the meteorological

numerical forecasts is critical, in that they suffer

from a distinct spin-up, with the associated errors

of the QPF (underestimation), in the first several

hours (about 12 h), and are pretty accurate

thereafter (whenever above prescriptions are

followed), until about 36 h. This imposes a

lower limit of two daily forecast cycles to an

operational forecasting chain, if accurate quanti-

tative forecasts are required at least 12–24 h in

advance.

These results should be confirmed by more

extensive analyses covering at least few seasonal

cycles and other areas, nevertheless they are increas-

ingly confirmed by other studies. Useful references

are Mass et al. (2002) for the spatial resolution

issues and Pastor et al. (2001) for the assimilation of

the SST.

Further work is needed and planned over the same

area, mainly testing the resolution and initialisation

issues when higher resolution global fields are

provided to the mesoscale meteorological model as

initial and boundary conditions, assimilating SST at

higher spatial resolution, and assimilating the

satellite rainfall estimates to update both

the atmospheric sensible and latent heat exchanges

by means of the cumulus convection para-

meterisation scheme and the land surface conditions

by means of the soil–vegetation–atmosphere trans-

fer scheme. An extensive verification over a

relatively long continuous period is also planned.

It is also suggested that running an ensemble

prediction system, as described for example by

Marsigli et al. (2001), would be potentially very

useful, especially considering the relevant uncertain-

ties in the triggering of deep convective systems.

What is shown in this work is that a very high spatial

horizontal resolution is needed to explicitly represent

deep convection and heavy rainstorms, which means

very high computational demands, thus possibly

limiting at least the size of the ensemble prediction

system.

Rapid update cycles (e.g. 6 h) of very high-

resolution numerical forecasts could represent viable

alternatives, and initiatives are paving the way for

such developments.

Acknowledgements

This research was supported by the Arno River

Basin Authority, which is gratefully acknowledged.

Two anonymous reviewers are also acknowledged,

whose comments and suggestions allowed a substan-

tial improvement of the manuscript.

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