A Reanalysis System for the Generation of Mesoscale...

A Reanalysis System for the Generation of Mesoscale Climatographies

ANDREA N. HAHMANN,*,1 DORITA ROSTKIER-EDELSTEIN,*,# THOMAS T. WARNER,*,@

FRANCOIS VANDENBERGHE,* YUBAO LIU,* RICHARD BABARSKY,& AND SCOTT P. SWERDLIN*

* National Center for Atmospheric Research, Boulder, Colorado**1 Risø National Laboratory for Sustainable Energy, Technical University of Denmark, Roskilde, Denmark

@ Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado& National Ground Intelligence Center, Charlottesville, Virginia

(Manuscript received 22 July 2009, in final form 4 November 2009)

ABSTRACT

The use of a mesoscale model–based four-dimensional data assimilation (FDDA) system for generating

mesoscale climatographies is demonstrated. This dynamical downscaling method utilizes the fifth-generation

Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), wherein

Newtonian relaxation terms in the prognostic equations continually nudge the model solution toward surface

and upper-air observations. When applied to a mesoscale climatography, the system is called Climate-FDDA

(CFDDA). Here, the CFDDA system is used for downscaling eastern Mediterranean climatographies for

January and July. The downscaling method performance is verified by using independent observations of

monthly rainfall, Quick Scatterometer (QuikSCAT) ocean-surface winds, gauge rainfall, and hourly winds from

near-coastal towers. The focus is on the CFDDA system’s ability to represent the frequency distributions of

atmospheric states in addition to time means. The verification of the monthly rainfall climatography shows that

CFDDA captures most of the observed spatial and interannual variability, although the model tends to un-

derestimate rainfall amounts over the sea. The frequency distributions of daily rainfall are also accurately

diagnosed for various regions of the Levant, except that very light rainfall days and heavy precipitation amounts

are overestimated over Lebanon. The verification of the CFDDA against QuikSCAT ocean winds illustrates an

excellent general correspondence between observed and modeled winds, although the CFDDA speeds are

slightly lower than those observed. Over land, CFDDA- and the ECMWF-derived wind climatographies when

compared with mast observations show similar errors related to their inability to properly represent the local

orography and coastline. However, the diurnal variability of the winds is better estimated by CFDDA because

of its higher horizontal resolution.

1. Introduction

Atmospheric models have been used for decades for

filling space and time gaps among observations with var-

ious motivations, for example, long-term reanalyses have

been generated with global-model data assimilation

systems. The resulting model-assimilated datasets (MADS)

are used for analyzing trends, studying physical pro-

cesses, and identifying erroneous observational data. On

smaller scales, mesoscale models have been used in short

simulations for ‘‘case study’’ analyses, where good model

simulation skill at observation locations has been justi-

fication for believing the simulation in the space and

time gaps between the observations. In both cases, the

strength in using the models to fill the observation gaps

is that the fields are dynamically consistent and they are

defined on a regular grid. Additionally, the models re-

spond to local forcing that adds information beyond

what can be represented by the observations.

More recently, the availability of greater computing

power has allowed the generation of long-term mesoscale

reanalyses (Castro et al. 2007; Kanamitsu and Kanamaru

2007; Lo et al. 2008; among many others), where

the resulting gridded datasets are used for various

# Permanent affiliation: Israel Institute of Biological Research,

Ness-Ziona, Israel.

** The National Center for Atmospheric Research is sponsored

by the National Science Foundation.

Corresponding author address: Andrea N. Hahmann, Risø Na-

tional Laboratory for Sustainable Energy, Technical University of

Denmark, P.O. Box 49, 4000 Roskilde, Denmark.

E-mail: [email protected]

954 J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y VOLUME 49

DOI: 10.1175/2009JAMC2351.1

� 2010 American Meteorological Society

applications. For example, mesoscale climatographies1

that are based on reanalyses can be used to define the

statistical distribution of wind speeds for wind power

assessment (Hagemann 2008). Boundary layer (BL) cli-

matographies can be used with transport and diffusion

(T&D) or air-quality models to define prevailing pat-

terns in source–receptor relationships. An example of the

latter application is the use of a multimonth mesoscale

reanalysis of BL properties in Iraq to calculate the ex-

posure of civilian and military personnel to potential

releases of Sarin nerve gas during the 1991 Gulf War

(Shi et al. 2004; Warner and Sheu 2000). In a similar

but more-contemporary application, the U.S. National

Ground Intelligence Center (NGIC) routinely performs

40-yr mesoscale reanalyses for various areas of the world

to estimate the T&D of hazardous material released into

the atmosphere for different seasons and various times-

of-day. In addition, such BL climatographies can allow an

assessment of the statistical risk to populations from the

release of hazardous material into the atmosphere from

chemical or nuclear power generation facilities. Last, the

automated interpretation of long-period reconstructions

of the atmosphere can be used to define physical pro-

cesses in ways that are much more robust than what is

obtainable from the use of a few case studies.

Other dynamical downscaling efforts have verified

climatographies of near-surface model winds over land.

Zagar et al. (2006) verified near-surface model winds

obtained by the dynamical downscaling of the European

Centre for Medium-Range Weather Forecasts (ECMWF)

40-yr Reanalysis (ERA40) with a mesoscale model using

a 10-km horizontal grid increment. Verification was per-

formed against wind observations at 10-m height AGL

for a period of 70 days. Kanamitsu and Kanamaru (2007)

have compared near-surface winds from the 57-yr

California reanalysis downscaling on a 10-km grid to the

North American regional reanalysis that used a 32-km

grid. Recently, dynamical downscaling has become a

well-established method for wind power resource as-

sessment purposes (e.g., Landberg et al. 2003), however,

very few objective verifications have been reported in

the literature. Jimenez et al. (2007) performed a verifi-

cation of downscaled near-surface winds from the Na-

tional Centers for Environmental Prediction–National

Center for Atmospheric Research (NCEP–NCAR) Re-

analysis Product by the Pennsylvania State University–

NCAR Mesoscale Model (MM5; their procedure does

not include the assimilation of observations as done in

our technique described in section 2) using measure-

ments taken on masts at several heights up to 130 m AGL.

However, the emphasis in that work was on offshore winds

rather than on winds over the land, where there is the

complication from orographic effects. Hagemann (2008)

conducted an extensive study of the sensitivity of

MM5-simulated winds to various combinations of land

surface and BL parameterizations with emphasis on the

wind power resource assessment in South Africa.

For a MADS to be used for a particular purpose, the

veracity of the analysis needs to be verified in the con-

text of the application. For example, for wind power

generation purposes the model must be able to reason-

ably reproduce the probability distribution function (PDF)

of the wind speed at the height of the generator. Because

the generation equipment is vulnerable to speeds above

a threshold, it is especially important that the model

capture the upper tail of the speed PDF. Similarly, for

air-quality or other T&D applications the BL depth

must be simulated accurately, as must the mean wind in

the BL, and for hydrologic purposes the PDF of rainfall

intensity is needed.

This paper describes a mesoscale reanalysis system

and demonstrates its ability to define unassimilated vari-

ables and fill in data voids. Section 2 describes the system’s

numerical basis, and section 3 summarizes the prevailing

meteorology for the eastern Mediterranean (EM) Sea

and nearby land areas. Section 4 describes the accuracy

of the mesoscale reanalysis of unassimilated variables,

and section 5 contains a summary and conclusions.

2. Numerical basis of the CFDDA system

Given the many existing and potential applications of

high-quality mesoscale MADS, NCAR has adapted its

real-time four-dimensional data assimilation (RTFDDA;

Liu et al. 2008) system for their creation. The RTFDDA

system uses data assimilation by Newtonian relaxation to

generate a mesoscale reanalysis that is consistent with

both observations and model dynamics. Even though it

was originally developed for dynamic initialization of

mesoscale forecasts with gridded datasets that contained

fully developed mesoscale processes, the continuous data

assimilation process in RTFDDA is also ideal for the

generation of mesoscale climatographies. The RTFDDA

technology applied in this way is called the climate-

FDDA (CFDDA) system. Versions of the CFDDA sys-

tem are based on both MM5 and its sequel, the community

Weather Research and Forecast model. One current

application of CFDDA is related to the previously

mentioned mission of the NGIC, which must assess the

potential risk to civilian and military personnel of the

release of hazardous material into the atmosphere for

1 The term climatography is used here to refer to a description of

the climate based on an interpretation of a series of observations in

contrast to the term climatology, which is the study of climate.

MAY 2010 H A H M A N N E T A L . 955

times that are months or years in the future. For this

purpose, the CFDDA climatography is generated for

a region of interest in the world and typical BL condi-

tions are used to define likely directions and speeds of

hazardous material transport for different seasons and

times of day. In addition, by quantifying the variance in

the wind fields over the historical period and inputting

this data to the probabilistic T&D model one can rep-

resent the uncertainty in consequence assessment prod-

ucts that is attributable to historical weather variability.

a. The basic CFDDA system configuration

The CFDDA system is currently based on the MM5,

version 3.6 (Grell et al. 1995). The general configuration

of MM5, as used in the CFDDA system, is summarized

in Table 1—details about specific model characteristics

can be found in the NCAR technical note on MM5

(Grell et al. 1995). In the simulations presented here,

default values have been used for the various parame-

ters in the parameterizations and no attempt has been

made to tune them to better represent the climate of the

region.

b. The data assimilation technique

Data assimilation by Newtonian relaxation is accom-

plished by adding nonphysical nudging terms to the

model predictive equations. These terms force the model

solution at each grid point to observations, or analyses of

observations, in proportion to the difference between

the model solution and the observations or analysis. This

approach is used because it is relatively efficient com-

putationally; it is robust; it allows the model to ingest

data continuously rather than intermittently; the full

model dynamics are part of the assimilation system so

that analyses contain all locally forced mesoscale fea-

tures; and it does not unduly complicate the structure

of the model code. The implementation of Newtonian

relaxation in the CFDDA system forces the model so-

lution toward observations (Stauffer and Seaman 1994)

rather than toward a gridded analysis of the data. This

approach was chosen because observations on the me-

soscale are sometimes sparse and typically are not very

uniformly distributed in space, making objective analy-

sis difficult. With observation nudging, each observation

is ingested into the model at its observed time and loca-

tion, with proper space and time weights, and the model

spreads the information in time and space according to

the model dynamics. In addition, the observational values

are quality controlled against the large-scale analysis

value as described by Liu et al. (2004).

Studies using Newtonian relaxation include Stauffer

et al. (1991), Stauffer and Seaman (1994), Seaman et al.

(1995), and Fast (1995). A common finding in these studies

is that analysis nudging works better than intermittent

assimilation2 on synoptic scales. Stauffer and Seaman

(1994) and Seaman et al. (1995) showed that nudging

toward observations was more successful on the meso-

scale than nudging toward analyses.

c. Model configuration and setup

The model uses 36 computational levels, with approx-

imately 12 levels within the lowest 1 km and with the

model top at 50 hPa. Figure 1 displays the geographic

location of the model grids and the surface elevation on

each domain. The model has a horizontal grid increment

of 45 km on the outer grid (D1) that covers most of the

Mediterranean Sea and extends eastward to cover the

Black and Caspian Seas. The first nested grid (D2),

which has a grid increment of 15 km, covers the EM and

Middle East regions. A third nested domain (D3), with

a grid increment of 5 km, is located along the Levant

(Lebanon, Syria, and Israel). One-way nesting is used to

preserve the horizontal continuity of the results on each

nest.

TABLE 1. Summary of setup and parameterizations used in the CFDDA system.

Nonhydrostatic dynamics

One-way interactive nesting procedure

Radiative upper-boundary condition that mitigates noise resulting from the reflection of vertically propagating waves

Coarse-domain time-dependent lateral-boundary conditions, relaxed toward large-scale reanalyses

Grell (1993) cumulus parameterization on 10-km grid increment, or coarser, grids

Reisner et al. (1998) mixed-phase microphysics parameterization that includes explicit prognostic equations

for cloud water, rainwater, ice particles and snow processes

Modified medium-range forecast model (Hong and Pan 1996; Liu et al. 2006) boundary layer parameterization

Cloud effects on radiative transfer (Dudhia 1989) for shortwave and rapid radiative transfer model (Mlawer et al. 1997) for longwave

Noah land surface model (Chen and Dudhia 2001) with four soil layers

2 Intermittent data assimilation involves restarting the model at

regular intervals, where the initial conditions are typically based on

data within a certain time window being used to correct a first-guess

field that is the forecast from the previous cycle. Restart intervals

may typically be 1–6 h.


The CFDDA simulations cover 10 Januarys and

10 Julys for the period 1998–2007. Individual simulations

are reinitialized every 8.5 days, using the large-scale reanal-

ysis (2.58 3 2.58 grid) of the NCEP–U.S. Department of

Energy reanalysis II (R2; Kanamitsu et al. 2002). These

same analyses are used to calculate the lateral boundary

conditions. Even though the rationale for the cold starts

is that they prevent the accumulation of error in data-

sparse areas, there has been no evidence of this problem.

Each simulation overlaps the previous one by 12 h, to

avoid using the time during which the model is spinning

up mesoscale processes. The choice of 8.5-day in-

tegration segments is due to restrictions on the size of

the lateral boundary condition file and the need to

minimize the error growth in areas with sparse observa-

tions. Qian et al. (2003) and Lo et al. (2008) recommend

this approach. An added advantage of segmenting the

simulation is that it allows for simultaneous integrations

for various time periods.

Initial land surface conditions are based on the Na-

tional Aeronautics and Space Administration (NASA)

Global Land Data Assimilation System (GLDAS; Rodell

et al. 2004) dataset that is defined on a 18 3 18 grid. The

GLDAS fields used here are substrate soil moisture and

temperature at depths of 10 and 200 cm, ground skin

temperature, and snow water equivalent. These fields

are interpolated to all land grid points on each model

domain and replace the standard values defined in the

R2 analysis. Because the same land surface scheme is

used in both GLDAS and MM5, no soil moisture con-

version is required. Sea surface temperatures (SSTs) are

interpolated to CFDDA ocean grid points on each model

domain using the NCEP Version 2.0 global SST dataset

(Reynolds et al. 2002), which is defined on a 18 3 18 grid

and updated daily.

d. Data sources for assimilated observations

The type, spatial density, and frequency of observa-

tions used by the CFDDA system vary greatly in different

parts of the world. The Advanced Data Processing

datasets, available from the NCAR Computational and

Information Systems Laboratory’s (CISL) Data Support

Section, are employed. The data represent a global syn-

optic set of surface hourly and 6-hourly data reports,

operationally collected by NCEP. The surface dataset

includes mostly surface synoptic observations (SYNOP)

and Meteorological Aerodrome Report (METAR) land

reports but a few ship observations also exist. The upper-

air data consist mainly of radiosonde soundings.

Figure 2 displays the geographic data distribution and

the approximate frequency of data availability in January

2000 for the model’s outermost domain. The map rep-

resenting surface data (Fig. 2a) shows a very inho-

mogeneous geographic distribution, with most of the

observations having greater than 3-h time resolution

located over western and central Europe—the lowest

concentration of observations is found over northern

Africa away from the coast. Both the Mediterranean

and Red Seas show a few locations with oceanic data

from ships, but their time resolution is poor (less than

1 observation per day on average). During January 2000

in this region, there were over 615 000 observations at

FIG. 1. CFDDA system configuration and terrain elevation of

(a) outermost domain (Dx 5 45 km; D1) and (b) innermost domain

(Dx 5 5 km; D3). The outer edges of D2 and D3 are shown by the

two rectangles in (a). The dots in (b) indicate the approximate

location of rain gauges used in the verification section of this paper.

Black squares at A and B mark the location of the wind towers.

MAY 2010 H A H M A N N E T A L . 957

over 2000 stations. The geographic distribution of upper-

air observations in Fig. 2b displays a similar pattern, with

the most frequently reporting (2–4 times a day) stations

over western Europe. Over the region covered by D3,

only four radiosonde sites are available, with an aver-

aged frequency of 1–2 observations per day.

3. Prevailing meteorological processes in the EM

The EM, located within the transition area between

the subtropical high-pressure belt and the midlatitude

westerlies, is very interesting and meteorologically com-

plex. Superimposed on the large-scale processes are

mesoscale effects related to the complex coastline and

nearby mountains (see terrain elevation in Fig. 1b). The

cold-season precipitation in this area is associated with

Mediterranean cyclones, for which there are many cli-

matological descriptions (e.g., Alpert et al. 1990; Trigo

et al. 1999; Genoves et al. 2006). There are strong near-

coastal gradients in observed precipitation, the positions

of which have huge hydrologic consequences. These

gradients are due to the preferred tracks followed by the

cyclones, their intensity, and their interaction with the

local topography (e.g., Saaroni et al. 2010). The north-

eastern and eastern coast of the Mediterranean basin is

rich in cyclogenetic regions, resulting in considerable

precipitation amounts. The opposite is true in the south-

eastern coast, leading to scarce precipitations and desert

climate characteristics. Some adjacent lands to the north-

ern and eastern Mediterranean coastal areas are rich in

cyclonic activity as well, leading to significant precipi-

tation amounts during the cold season—that is, the south

coast of the Black sea and the Fertile Crescent extend-

ing throughout north Levant to the Persian Gulf. Thus,

proper reproduction of the winter climate over this region

compels correct simulation of the synoptic flow domi-

nated by extratropical cyclones as well as the mesoscale

flow dictated by the complex coast and terrain forcing.

Throughout the summer, the EM is dominated by a

subtropical ridge extending from the north-African coast

to the east and by a Persian trough extending from the

monsoonal low through the Persian Gulf to the north-

east Mediterranean and Turkey (Alpert et al. 1992). The

synoptic-scale near-surface wind direction is generally

westerly and is modulated by the sea breeze. This results

in a westerly to northwesterly wind flow of nearly 7 m s21

during the daytime. In addition to the synoptic flow, the

westerly daytime sea breeze is accelerated by anabatic

winds forced by the inland mountainous regions (Alpert

and Rabinovich-Hadar 2003; Doron and Neumann 1977;

Berkovic and Feliks 2005). The easterly land breeze

develops along the coast, because of the sea–land dif-

ferential cooling after sunset. As the night progresses,

katabatic flow originating on the western slopes of the

mountain ridges (;30 km inland in the south of Israel

and adjacent to the coast in the north) reaches the coast,

increasing the wind intensity.

4. Verification of CFDDA for the easternMediterranean

This paper assesses the success of the downscaling sys-

tem by comparing its climatographies to various datasets

that have not been assimilated during the model inte-

gration. The verification focuses on the ability of the

downscaling system to represent the frequency distri-

bution of atmospheric states in addition to the simple

time means.

Because precipitation data are not assimilated and

many aspects of model physics typically need to operate

properly for precipitation to be correctly simulated,

FIG. 2. Geographic location and time frequency of (a) surface

and (b) upper-air observations assimilated in the CFDDA system

within the domain during January 2000. The various symbols rep-

resent the approximate temporal frequency of the observations.


verifying the simulated precipitation fields is a good test

of the CFDDA system’s ability to define unobserved or

poorly observed fields. We verify the spatial distribution

of monthly multiyear totals, the interannual variability

of regional precipitation totals, and the frequency distri-

bution of daily rainfall totals against gridded and gauge

rainfall totals. Also evaluated are the CFDDA clima-

tography of the 10-m winds over the Mediterranean Sea

using NASA Quick Scatterometer (QuikSCAT) mea-

surements (OBS). Coastal wind tower observations are

also used for verification.

a. Large-scale circulation

Before focusing on the details of the validation of

the CFDDA high-resolution simulations, it is impor-

tant to assess the ability of the modeling system to

correctly represent the large-scale features of the cir-

culation over this particular region. To accomplish this,

the 6-hourly real-time analyses from the ECMWF have

been used to provide an estimate of the large-scale

circulation. This ECMWF operational analysis is avail-

able at 1.1258 3 1.1258 resolution. Figure 3 displays the

anomaly correlation between the ECMWF analysis and

the corresponding CFDDA-D1 fields interpolated to

the ECMWF grid. Correlations are plotted every 6 h for

each January and July for 1998–2007 for the 700-hPa

temperature and relative-humidity fields.

The large-scale thermal and moisture patterns simu-

lated by the CFDDA modeling system closely correlate

with those defined by the ECMWF analysis. The anomaly

correlation values for 700-hPa temperature (Figs. 3a,b)

average 0.985, with higher overall values in winter than

in summer. The same seasonal trend is true for the

anomaly correlations for 700-hPa relative humidity (RH;

Figs. 3c,d), which has an average of 0.83. Higher in the

troposphere, anomaly correlation values (not shown)

are even larger. The anomaly correlations for each year

of the simulation are displayed separately, and it can be

seen that there is a tendency for higher correlations to

exist for later years. This is probably a consequence of

the increased number and quality of observations as-

similated in both the ECMWF and CFDDA analysis

and improvements in the ECMWF analysis cycle. For

January 2006, a CFDDA simulation with no assimila-

tion of point observations was conducted. The anomaly

correlation values decreased to 0.85–0.9 for the 700-hPa

temperature and to 0.55–0.7 for the RH. This result

emphasizes the importance of the FDDA portion of

FIG. 3. Time series of anomaly correlation between upper-air fields from the ECMWF analysis and those simulated by the CFDDA-D1

system for the period 1998–2007. Correlations are for 700-hPa temperature for (a) January and (b) July, and 700-hPa relative humidity for

(c) January and (d) July.

MAY 2010 H A H M A N N E T A L . 959

the system for constraining the simulation of the large

scales.

b. Monthly precipitation climatography

The model-derived monthly rainfall totals are com-

pared to three different gridded estimates of monthly

rainfall. For gauge-only estimates, we employ the Global

Precipitation Climatology Centre (GPCC; Beck et al.

2005) dataset, which provides monthly precipitation to-

tals from 1901 to the present on a 0.58 3 0.58 global grid

but only over land. Because a considerable fraction of

the CFDDA domain is located over the sea, we also

utilize the merged Tropical Rainfall Measuring Mission

(TRMM) product (3B43; Huffman et al. 2007). These

data combine precipitation estimates from multiple sat-

ellites (retrievals from measurements in the microwave

and infrared regions of the spectrum) as well as gauge-

based analyses on a 0.258 3 0.258 grid that extends from

508N–508S for the period from 1998 to the present. Fi-

nally, following the work of Ebert et al. (2007), we also

use precipitation derived from the short-range forecasts

(i.e., the accumulated precipitation during the forecast

period between 12 and 36 h) from the ECMWF model,

available daily on a 1.1258 3 1.1258 global grid. These

daily estimates were aggregated to produce a monthly

rainfall total and have an accuracy that is comparable to

that of satellite estimates, especially in winter and at

higher latitudes (Ebert et al. 2007). The focus here is

mainly in the geographic distribution of rainfall; there-

fore, we have chosen not to interpolate the data from the

various sources to a common grid. A more quantitative

analysis will be done later.

Figures 4a–c show the average (1998–2007) January

precipitation amount based on the three observational

datasets described above. Large precipitation maxima

over land are seen along the coastlines of the Levant,

FIG. 4. Total monthly rainfall (mm) averaged during 10 Januarys (1998–2007) for (a) 0.58 3 0.58 GPCC, (b) 0.258 3 0.258 3B43 merged

TRMM, (c) 12–36-h forecast rainfall from ECMWF model, and (d) CFDDA-D1 simulations. The two black boxes in (a) outline the areas

averaged in Fig. 7.


Turkey, and along the west coast of the Balkan states.

There is also precipitation over the Mediterranean Sea

but in various degrees from TRMM (Fig. 4b) or ECMWF

(Fig. 4c). A distinct line of maximum rainfall is also

apparent along the Fertile Crescent. The precipitation

gradient is very large perpendicular to the North Afri-

can coastline, with values of about 50 mm along the

Libyan coast, decreasing to less than 5 mm across the

interior just 100 km inland. The gradient is somewhat

less along the Levant and Turkey coasts, but it is still

large. Overall, the precipitation estimated by CFDDA

(Fig. 4d) represents most of these spatial patterns, ex-

cept for the observed coastal maximum over Algeria

and France (upper-left edge of the domain). There is less

precipitation estimated by CFDDA over the central and

southern Mediterranean than is estimated by TRMM

(more tan color, Fig. 4b). However, verification of pre-

cipitation over the Mediterranean Sea is problematic

because the TRMM dataset has a known negative bias

to low-precipitation rates over the oceans (Huffman

et al. 2007).

During July (Fig. 5), observations show areas of en-

hanced precipitation along the eastern Black Sea coasts

of Turkey and Georgia. A secondary region is seen over

Romania, extending northward and southwestward to

the Balkan countries. The largest maximum is located

over the Alps, along the northern Italian border. All

observational estimates show no significant rainfall be-

low about 358N and considerably more rainfall over land

than over oceanic regions. Overall, a very similar pattern

is seen in the CFDDA rainfall climatography (Fig. 5d).

However, the model rainfall has more small-scale vari-

ability than that observed by the blended TRMM prod-

uct that is shown at a similar spatial resolution (Fig. 5b).

For example, in the CFDDA-derived rainfall climato-

graphy, a series of northwest to southeast oriented areas

of maximum precipitation are seen over Romania and

southern and western Ukraine. Similar maxima, but more

FIG. 5. Total monthly rainfall (mm) averaged during 10 Julys (1998–2007) for (a) 0.58 3 0.58 GPCC, (b) 0.258 3 0.258 3B43 merged TRMM,

(c) 12–36-h forecast rainfall from ECMWF model, and (d) CFDDA-D1 simulations.

MAY 2010 H A H M A N N E T A L . 961

widespread in character, are seen in the GPCC and

TRMM climatographies (Figs. 5a,b, respectively) and a

similar pattern is seen in the ECMWF-forecasted rain-

fall (Fig. 5c).

c. Variability of daily rainfall

Daily rainfall totals simulated by the CFDDA system

are verified against a database of daily precipitation to-

tals from rain gauge stations for the geographic region

covered by D3. The locations of the rainfall stations are

shown in Fig. 1b. The data for a majority of these rain

gauge observations were obtained from the Israeli Min-

istry of Agriculture. The dataset was augmented (in

particular for the stations over Lebanon) with daily

rainfall totals from the National Oceanic and Atmo-

spheric Administration global surface summary of the

day (GSOD). The analysis is conducted with only sta-

tions that have data for at least 70% of the days in the

10 Januarys covering the period from 1998 to 2007.

Rainfall in this region during the summer months is vir-

tually nonexistent; thus, we have limited our analysis to

the winter season.

During winter, a marked north–south gradient in rain-

fall exists in D3, with the larger totals in the northern

Levant and lesser amounts toward the south. This gra-

dient is mainly a consequence of the preferred path of

extratropical cyclones (Alpert et al. 1990; Trigo et al.

1999; Schadler and Sasse 2006). The rainfall stations have

been classified into three groups based on this north–

south contrast. A summary of the rainfall statistics for

each group of stations is presented in Table 2, with

the lowest observed monthly rainfall totals in southern

Israel (stations 1–6) and the largest observed values in

northern Israel (stations 7–12). The CFDDA-D3 and

the ECMWF operational 12–36-h forecast daily rainfall

totals have been interpolated to the station locations

for those days with observed values—only nonzero pre-

cipitation days in both models and observations are used

in the statistics and subsequent frequency distributions.

The simulated CFDDA monthly rainfall totals compare

well with those observed over the two regions in Israel, but

they overestimate the total over Lebanon. The ECMWF

forecasts underestimate rainfall totals in all three re-

gions. The mean daily total rainfall for rainy days is

underestimated by CFDDA for Israel but values are

better than those from ECMWF in all three regions.

Table 2 also presents the percentage of ‘‘drizzle’’ days

with precipitation of less than 0.1 mm but not including

days with zero precipitation. This varies from 13% in

southern Israel to almost zero over Lebanon. This value

is reasonably well simulated for Israel by both CFDDA

and ECMWF, but both models severely overestimated

this percentage for Lebanon. Similar behavior is seen

in other weather and climate models (Chen et al. 1996;

Knievel et al. 2004).

The frequency distribution of observed daily rainfall

totals was compared for each group of stations to the

distributions from the CFDDA simulations and ECMWF

forecasts. For southern Israel (Fig. 6a) the observed

distribution shows that most of the rainfall events result

in daily totals of 4–8 mm; for the northern Israel stations

(Fig. 6b) and the stations in Lebanon (Fig. 6c) the dis-

tributions are shifted toward 8–16 mm of daily rainfall.

The northern Israel stations show a few events (;10)

with daily rainfall totals above 64 mm, with fewer such

events in southern Israel and Lebanon. The CFDDA

distribution of daily rainfall totals in categories above

1–2 mm is similar to the observed distribution; however,

CFDDA consistently overestimates the number of light

rainfall events (0.1–1 mm) in all regions, perhaps by a

similar mechanism to that responsible for the overpre-

diction of drizzle days over Lebanon. The consistency

between CFDDA and the observed distribution is par-

ticularly good for the northern Israel stations (Fig. 6b),

with errors of less than 15 events in all classes above

2 mm. In Lebanon, the CFDDA system overestimates

the values in the higher-intensity classes (.16 mm). This

may be due to a lack of representativeness of the ob-

served rainfall totals in stations located in complex terrain

(Chen et al. 2002). Furthermore, rainfall totals for the

Lebanon stations were derived from the GSOD archive,

which has only minimal quality control of the measure-

ments. Similar error statistics have been found in other

MM5 studies at similar resolutions over complex terrain

(Colle and Mass 2000; Mass et al. 2002).

TABLE 2. Rainfall statistics for January 1998–2007 for the three subregions in Fig. 1b.

Statistic Southern Israel Northern Israel Lebanon

OBS CFDDA ECMWF OBS CFDDA ECMWF OBS CFDDA ECMWF

Total monthly rainfall (mm) 108.6 121.7 70.7 169.3 167.6 103.5 152.6 269.4 124.3

Mean daily rainfall

intensity (mm day21)

7.0 4.9 3.5 10.8 7.0 5.0 10.9 10.4 5.6

Fraction of days with

0 , p , 0.1 mm (%)

12.9 13.2 10.0 6.3 10.0 7.7 0.1 10.1 8.0


The rainfall from the ECMWF forecasts consistently

has excessively high frequencies in the low-rainfall classes

and too low frequencies for the higher precipitation

amounts for all station groups. Daily amounts above

32 mm, which are not rare in the observed or CFDDA

distributions, are almost nonexistent in the ECMWF

daily rainfall totals. Therefore, although the monthly

rainfall values forecast by ECMWF are reasonably close

to those observed, the rainfall occurs too often and with

a lower-than-observed intensity.

d. Interannual variability of rainfall totals

The gridded January rainfall data from observations

and models are compared in terms of their interannual

variability for two geographic regions: the Levant (318–

368N, 368–378E) and southwest Turkey (368–388N, 258–

328E), which are delineated by black boxes in Fig. 4a.

These areas were chosen because of the large prevail-

ing rainfall amounts in January (Figs. 4b,c). The area-

averaged rainfall for these two regions during all Januarys

for the period 1998–2007 is displayed in Fig. 7. Because

the CPCC rainfall estimate is available for land only,

and TRMM data are available for land and water, for

CFDDA we display both the values averaged over all

grid points and over land-only grid points. In most years,

these two values are very similar over coastal Turkey but

the averages over the Levant using D2 rainfall data differ

significantly. Over the Levant, the CFDDA-D2 and

ECMWF rainfall totals are consistently larger than the

TRMM estimates for all years. Over southwest Turkey,

the TRMM estimates are often larger than the CFDDA

and ECMWF values. The CFDDA-D2 precipitation

values are uniformly larger than those for D1, for both

regions and all years. Considerable interannual variabil-

ity is seen in the observed precipitation in both regions,

and the variation from one year to the next in both re-

gions is well represented in the CFDDA and ECMWF

rainfall.

e. Near-surface winds over the ocean

To verify the CFDDA-simulated low-level winds over

the ocean, we use data from the NASA QuikSCAT

satellite. The SeaWinds instrument on QuikSCAT is an

active microwave radar that measures the backscatter

from surface ocean waves, and winds can be obtained in

FIG. 6. Distribution of daily rainfall totals for observations and

model simulations from CFDDA-D3 and 12–36-h forecast from

the ECMWF model for the stations shown in Fig. 1b located in (a)

southern Israel (stations 1–6), (b) northern Israel (stations 7–12),

and (c) Lebanon (stations 13–15).

MAY 2010 H A H M A N N E T A L . 963

all conditions except for moderate to heavy rain (Hoffman

and Leidner 2005). A function is used to relate the

measured backscatter to the 10 m MSL neutral stability

equivalent winds. The rms differences between quality-

controlled ship winds and QuikSCAT winds are typically

about 1 m s21 in speed and 158 in direction (Bourassa

et al. 2003). Very high- and very low-speed winds have

problematic retrievals. In particular, low speeds result in

very poor directional skill, while winds above 25 m s21

are often underestimated (Hoffman and Leidner 2005).

Also, because of the previous assumption of neutral sta-

bility, winds of less than 6 m s21 are underestimated by

20%–30% under unstable conditions, which are often

found over the Mediterranean during winter (Zecchetto

and De Biasio 2007).

In the validation presented here, we use the QuikSCAT

level-3 daily, gridded ocean wind vectors. These data are

provided on an approximate 0.58 3 0.58 global grid with

separate maps for the ascending (0600 LT equator cross-

ing) and descending (1800 LT equator crossing) passes.

The data are available for the period 2000–present. The

CFDDA 10-m winds for each domain are linearly in-

terpolated to each individual QuikSCAT wind vector

location, and matched to the closest valid time of the

observation. Figures 8 and 9 compare the 10-m wind

speed composites from QuikSCAT and CFDDA-D2 for

January and July 2000–07, respectively. During January,

FIG. 7. Area-averaged January precipitation (mm) as a function

of the year (1998–2007) for (a) Levant (318–368N, 348–378E; right

rectangle in Fig. 4a) and (b) SW Turkey (368–388N, 258–328E; left

rectangle in Fig. 4a) for CFDDA system simulations over domains

D1 and D2, and for all points vs land-only (L) points for observa-

tional estimates from GCPP and TRMM and the 12–36-h forecasts

from the ECMWF model.

FIG. 8. January (2000–07) composite of 10-m winds (m s21) from

(a) QuikSCAT, (b) CFDDA-D2 system simulations, and (c) the

difference between the CFDDA-D2 and QuikSCAT composites.

The black rectangles in (c) mark the locations of the data used in

the wind roses in Figs. 10 and 11.


the observed wind climatography is dominated by the

northwesterly Etesian winds (Ziv et al. 2004) in the

Levantine Basin and the mainly northerly flow in the Black

Sea area. During winter, the winds in the entire region are

characterized by low steadiness (a measure of the con-

stancy of the wind direction) resulting from the frequent

passage of fronts (Zecchetto and De Biasio 2007). The

observed mean 10-m wind speed is 6–10 m s21 across the

Mediterranean Sea (Fig. 8a), with speeds decreasing

toward the southeast. Over the Black Sea, wind speeds

range from up to 10 m s21 in the west to 4 m s21 in the

east. The CFDDA 10-m winds (Fig. 8b) display a similar

geographic distribution but the speed is underestimated

by 1–2 m s21. In summer (Fig. 9), when conditions over

the Mediterranean Sea are dominated by steady local

winds, the CFDDA system is able to capture very well

the wind distribution observed by QuikSCAT. The area

of wind funneling through the strait separating the islands

of Crete and Rhodes (Zecchetto and De Biasio 2007) is

clearly seen in the CFDDA-derived analysis. Mean ab-

solute differences between QuikSCAT and the model are

mostly below 1 m s21 over more than 90% of the ocean

grid points in D2.

To further examine the model-simulated winds as

they relate to those observed by QuikSCAT, we show

wind roses of model-derived 10-m winds and observed

winds for two 58 3 58 regions in the EM. Solid lines in

Fig. 8c show the two regions. In Fig. 10, the wind roses

for January 2000–07 (combining both QuikSCAT ascent

and descent times) for the area 328–378N and 258–308E

(left box in Fig. 8c) are shown for QuikSCAT and

CFDDA-D2. Observed winds are mostly from the west

and northwest, with the most frequent high speeds (av-

eraging 9 m s21) being from the northwest. The roses

generated from the CFDDA-D2 winds show very simi-

lar directional frequency distributions but the model un-

derestimates the frequency of the strongest wind class.

Almost identical results are seen in the CFDDA-D1

winds (not shown). In Fig. 11, the wind roses generated

with data from July 2000–2007 for a region closer to the

EM coast (328–378N, 308–358E, right box in Fig. 8c) are

shown. This time, separate wind roses are shown for the

morning (ascent) and evening (descent) overpasses to

isolate diurnal effects. During this time of the year,

westerlies dominate with stronger winds more likely in

the evening than in the morning. The CFDDA-derived

winds reproduce this diurnal variation quite well, espe-

cially at higher resolution (Figs. 11c,f). As in winter, the

model underestimates the speeds. However, the differ-

ences are more evident in the morning than in the

evening. Because very few observations are available

over the sea (Fig. 2a), only a limited impact of data as-

similation is expected in these regions.

f. Boundary layer winds over land

Here, the CFDDA wind speed distributions for January

and July are compared to observations on two towers.

Data from these towers were not assimilated in the model

integration. In addition, we verify the winds from the

6-hourly real-time ECMWF analyses at a resolution of

1.1258. The aim of this comparison is to assess the ca-

pability of CFDDA to reproduce wind speed statistics

that are relevant to wind power generation applications.

The observations used in the verification consist of

winds measured at the two locations depicted in Fig. 1b.

Hourly observations at 65 m AGL were available for

January and July 1999–2007 at station A, and half-hourly

observations at 60 m AGL were available for January

and July 1998–2007 at station B. Both stations are situ-

ated at a distance of about 8 km inland from the coast,

FIG. 9. As in Fig. 8, but for July.

MAY 2010 H A H M A N N E T A L . 965

in areas characterized by a mixed canopy consisting of

urban buildings, shrubs, and trees, with a roughness length

of approximately 0.3 m. Station A is located in an area of

rather flat terrain—the tower base is 62 m MSL and the

coastline is relatively straight. Station B is situated in

a region of complex terrain and complex coastline con-

figuration and the tower base is at 60 m MSL. The latter

area has been described in Rostkier-Edelstein and Givati

(2003). Although neither of these observations is located

in open rural areas suitable for wind power production,

we opted to verify CFDDA against them because they

provide a high-quality multiyear dataset at heights rele-

vant to wind power applications.

The CFDDA-derived winds from D3 were interpolated

to the location of the observations. The CFDDA terrain

heights at the location of the stations are 36 m and 51 m

MSL, for stations A and B, respectively. The lowest

three CFDDA levels are located at approximately 15,

57, and 113 m AGL at both locations, and winds were

vertically interpolated with respect to height using log-

arithmic weights, between the latter two levels, to the

height of the masts. Therefore, the height AGL of the

observed and interpolated wind is the same despite

the elevation difference of the model terrain and the real

terrain. Land grid squares (mainly urban type, with as-

signed roughness length of 1 m) surround the location of

station A in D3. In contrast, station B is surrounded on

three sides by land grid squares, except for the northwest

quadrant, where over 40% of all modeled winds originate,

which is an ocean grid square (with an assigned back-

ground roughness of 0.001 m).

Similarly, ECMWF analysis winds archived on the

1.1258 grid were interpolated to the stations locations.

Because of changes in model resolution, the terrain heights

of the ECMWF analyses at the observation locations

changed during the study period. The ECMWF terrain

height values were appreciably higher than the actual

ones (159–214 m MSL and 171–216 m MSL for stations

A and B, respectively). The ECMWF wind values used

for comparison with the observations are based on log–

height interpolation from the pressure levels of 1000

and 925 hPa. The model that generated the operational

ECMWF analysis system used here underwent changes

in horizontal and vertical resolution, model physics, and

data assimilation during the period 1998–2007. There-

fore, no temporal consistency is expected, but the wind

fields provide the ‘‘best’’ baseline analysis at the time.

Figure 12 displays the wind speed distributions (for all

6 hourly data) based on CFDDA-D3, ECMWF, and

observations for both locations during January and July.

Both models reproduce the observed differences in the

wind speed distributions between January and July. The

results for January (Figs. 12a,b) are characterized by

FIG. 10. Wind roses generated from January 2000–07 winds from

(a) QuikSCAT retrievals (OBS), and (b) CFDDA-D2 (15 km).

The winds are restricted to the area between 328–378N and

258–308E (left square in Fig. 8c) and include both ascent and descent

times. The wind rose radius represents the wind frequency (%) for

each sector and speed range as presented in the grayscale bar below.


a wide distribution of wind speeds (relative to those for

July) with a high-speed tail showing a few observed

events that exceed 10 m s21. These events are associated

with strong pressure gradients within extratropical cy-

clones. The periods between the cyclonic activity, which

are mainly dominated by Red Sea troughs and winter

highs, lead to weaker winds. The observed distributions

for this month show no significant differences between

the two sites, in spite of the differences in the orography

and coastline characteristics. In winter, in the absence of

strong solar heating, no pronounced land–sea tempera-

ture gradients develop and local thermal circulations are

likely weak. Near-surface winds are thus dominated by

the synoptic flow. Both models are able to reproduce the

broad observed distributions. However, there is some

model-dependent disagreement between the modeled

and observed distributions, and some differences exist

between the model solutions for the two sites. The

ECMWF analysis tends to overestimate the frequency

for the low-speed part of the January distributions at

both sites and underestimates the frequency at higher

speeds. The PDFs for CFDDA show underestimation of

the speeds in the middle of the distribution and over-

estimation for winds above 7 m s21 at both sites. The

CFDDA terrain and coastline (not shown, but partly

described previously) is not able to correctly represent

the ridge and bay in the vicinity of station B, as well as

the correct distance to the coast. As a result, the site is

located much closer to the coast in the model than in

reality and is thus affected by lower roughness upstream

during periods of strong winds.

The observed wind distributions for July (Figs. 12c,d)

are narrower than for January, with speeds that do not

exceed 11 m s21. This is a result of the summer weather

FIG. 11. Wind roses generated from July 2000–07 winds derived from (a) QuikSCAT ascent retrievals (OBS), (b) CFDDA-D1 (45 km)

ascent times, (c) CFDDA-D2 (15 km) ascent times, (d) QuikSCAT descent retrievals (OBS), (e) CFDDA-D1 (45 km) descent times, and

(f) CFDDA-D2 (15 km) descent times. The winds are restricted to the area between 328 and 378N and between 308 and 358E (right box on

Fig. 8c). The wind rose radius represents the wind frequency (%) for each sector and speed range in the grayscale bar.

MAY 2010 H A H M A N N E T A L . 967

regime that is characterized by weak westerly to north-

westerly large-scale flow (Section 3), upon which sea–land

breeze and terrain-induced circulations are superimposed.

The observed distributions show some differences be-

tween the two nearby sites, presumably because of the

local orography and coastline. For example, while sta-

tion A (Fig. 12c) shows a maximum frequency for speeds

of about 3 m s21, station B (Fig. 12d) shows a bi-

modal behavior with maximum frequencies for speeds

of 1–2 m s21 and 4–6 m s21. For summer, the ECMWF

product overestimates some of the low- and medium-

speed categories and underestimates higher speeds. The

CFDDA wind distribution for station A is too flat, greatly

underestimating the speeds in the 3–5 m s21 range. For

station B, the modeled distribution is insufficiently flat.

The model does produce a bimodal distribution but the

minimum is at too high a speed.

To better understand the role of the land–sea breeze

in generating the observed wind distributions, and to

assess the ability of the models to reproduce it, Fig. 13

displays the observed and model histograms for wind

speeds at station A for 0000, 0600, and 1200 UTC for

July. These times are not necessarily ideal for showing the

phases of the land–sea breeze but rather are dictated by

the availability of the ECMWF data. Figure 13a (0300 LT)

shows the wind speed distributions at a time dominated by

the land breeze, which is normally weaker than the sea

breeze. The observed wind distribution shows a significant

frequency of calm winds (,1 m s21) and speeds never

exceed 6 m s21. The ECMWF-derived winds fail to re-

produce the calm speeds and most of the events fall in the

2–4 m s21 category. In contrast, CFDDA-derived winds

better capture the distribution. The coarse resolution of

the ECWMF analyses most likely masks the land breeze

FIG. 12. Frequency distributions of 6-hourly wind speeds from observations, and simulated by the CFDDA-D3

(5 km) and ECMWF operational forecast model for (a) station A and (b) station B during January 1999–2007 and

(c) station A and (d) station B during July 1998–2007.


and terrain-induced flow at this time. In the absence of

these, no opposing flow weakens the large-scale westerly

to northwesterly flow. At 0900 LT (Fig. 13b), the sea

breeze begins to develop, with higher observed wind

speeds. The ECMWF winds remain rather similar to

those at 0300 LT. In contrast, CFDDA winds correctly

shift toward higher speeds but the peak is not sufficiently

large. At 1500 LT (Fig. 13c), all three distributions show

a narrow profile, with speeds in the range 3–9 m s21.

The winds derived from the ECMWF analysis still un-

derestimate the observed frequency peak and the higher

speeds.

5. Summary and discussion

We present a method for generating high-resolution

regional climatographies through the use of a mesoscale

model driven by R2 and the assimilation of surface and

upper-air observations by Newtonian relaxation. The re-

sults of 10 yr of simulations for January and July are

verified in terms of the horizontal and seasonal distri-

bution of rainfall and the near-surface wind field, both

over the ocean and at two land sites.

The verification of monthly rainfall shows that CFDDA

is able to correctly capture most of the geographic fea-

tures and interannual variability of the precipitation

field in this region. Over the oceans, CFDDA appears to

underrepresent the monthly rainfall; however, the error

in the CFDDA estimates as compared to those from

TRMM is within the uncertainty of these satellite esti-

mates over the oceans. Daily rainfall totals from the

CFDDA system are verified against rain gauge obser-

vations, and the results show remarkable agreement

both in terms of the mean amount but also in terms of

their temporal distribution for three distinct regions. As

seen in many other models, the CFDDA system appears

to overestimate the amount of precipitation over higher

elevations. It is unclear what the origins of this problem

in the model simulation are, or whether the observations

underestimate the true amount of rainfall of this region

by undercatchement errors (Colle and Mass 2000) or

preferential placement of the verifying gauges in lower

elevation valley locations. Nevertheless, the high quality

of the depiction of the rainfall distribution in most places

by the CFDDA system demonstrates its possible use-

fulness in coupling to hydrological models.

FIG. 13. Frequency distributions of 6-hourly wind speeds

from observation and simulated by the CFDDA-D3 (5 km) and

ECMWF forecast model from station A during July 1999–2007 for

(a) 0000, (b) 0600, and (c) 1200 UTC.

MAY 2010 H A H M A N N E T A L . 969

Wind roses constructed with wind vectors from CFDDA

show considerable accuracy when compared with winds

derived from QuikSCAT observations over the EM Sea.

Although the directional distribution of winds is well

represented, wind speeds simulated by the CFDDA

system over the oceans are consistently slightly lower

than those observed by QuikSCAT. The surface-layer

formulation within the CFDDA system uses Charnock’s

formula as written by Delsol et al. (1971) to compute the

surface roughness over the ocean as a function of the

wind speed. However, the constant used in MM5 is twice

as large as that based on oceanic data (Wu 1969). Colle

et al. (2003) reported similar biases in their verification

of wind speeds over the Gulf of Mexico and the eastern

Atlantic.

The comparison between CFDDA, ECMWF, and

observed wind speed distributions at two mast locations

is accomplished through the construction of wind speed

histograms for observed and simulated data. In this

comparison, representativeness error is not accounted for.

Clearly, both the ECMWF and CFDDA analyses fail to

truthfully represent the terrain (mainly at station B), the

coastline (mainly at station B), and the roughness

characteristics of the areas surrounding the masts ob-

servations. Wind measurements may be exposed to local

influences limiting their spatial representativeness to a

radius of only a few hundreds of meters. Some estimates

of representativeness error for near-surface winds com-

puted with a model grid increment of about 1 km lead

to a value of about 1 m s21 for areas of complex terrain

(e.g., Rife et al. 2004; Steinacker et al. 2000). Landberg

et al. (2003) discussed the nature of these local effects and

described methods to overcome them for wind resource

estimation. These methods rely on dynamical downscal-

ing using a mesoscale model and further fine tuning of the

wind climatographies with a microscale model (Frank

and Landberg 1997).

The verification results, despite the discussed caveats,

confirm the potential of using CFDDA reanalyses as a

tool for wind power resource estimation. Assimilated

surface and upper-air observations are sparse in the vi-

cinity of the masts, thus, showing the capability of the

mesoscale reanalysis to reproduce near-surface wind

statistics in poorly observed areas. To the best of our

knowledge, this is one of the few published verifications

of dynamical downscaled winds over land at turbine

height, in the context of wind resource assessment. It

demonstrates the benefit gained by high-resolution meso-

scale reanalysis as compared with coarse-resolution global

analyses, in particular when the circulation is dictated

by mesoscale mechanisms. Some of the limitations

encountered may be overcome by optimization of the

CFDDA model parameters for the specific domain, for

example, terrain, coastline, surface canopy, and specific

parameters in the physical process schemes. Others

limitations are imposed by the model horizontal grid

size, that is, the representativeness errors due to local

effects. These can be overcome by increasing the hor-

izontal resolution further or by postprocessing the

CFDDA reanalysis with statistical corrections or mi-

croscale modeling.

Many factors play a role in the quality of the meso-

scale reanalysis system: the quality of the mesoscale

model and its physical parameterizations, the quality of

lateral and lower boundary conditions, the quality and

spatial density of observations used in the data assimi-

lation, the increased horizontal and temporal resolution,

etc. It is beyond the scope of the paper to examine the

individual contribution of each of these, but we believe

all are necessary components of a credible system.

In the present study, no attempt has been made to

optimize the model parameterizations for the weather

conditions and climate of the EM. The choice of these is

based on our experience from real-time mesogamma-

scale weather forecasting in the United States (Liu et al.

2008). That type of optimization process, which is be-

yond the scope of this work, would very likely improve

the accuracy of the climatography derived from the

CFDDA system.

Acknowledgments. Andrew Negri of NASA provided

assistance relative to the use of TRMM data. Mark Perel

from the Israeli Ministry of Agriculture is thanked for

providing the data from the rain-gauge stations. Dr. Ilan

Setter from the Israeli Meteorological Service is thanked

for providing the masts wind data. Gregory Roux and

Ming Ge are thanked for their help with data processing

and Jason Knievel is thanked for his help editing the

manuscript. The suggestions of three anonymous re-

viewers greatly improved the quality of the manuscript.

We thank the Army Testing and Evaluation Command

(ATEC) and the NGIC for providing the financial support

for this study. Dorita Rostkier-Edelstein acknowledges

the NCAR Research Applications Laboratory visitor

funds. The CFDDA system simulations were performed

using NCAR/CISL supercomputers.

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