Chiang Mai J. Sci. 2012; 39(4) 623

Chiang Mai J. Sci. 2012; 39(4) : 623-638http://it.science.cmu.ac.th/ejournal/Contributed Paper

Evaluation of Precipitation Simulations over Thailandusing a WRF Regional Climate ModelChakrit Chotamonsak*[a], Eric P. Salath Jr. [b], Jiemjai Kreasuwan [c]and Somporn Chantara [d][a] Environmental Science Program and Centre for Environmental Health, Toxicology and Management of

Chemicals (ETM)., Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand .[b] Science and Technology Program, University of Washington, Bothell, Washington, USA.[c] Department of Physics and Materials Science, Faculty of Science, Chiang Mai University, Chiang Mai

50200, Thailand.[d] Environmental Science Program and Department of Chemistry, Faculty of Science, Chiang Mai University,

Chiang Mai 50200,Thailand.*Author for correspondence; e-mail: chotamonsak@gmail.com

Received: 24 August 2011Accepted: 5 February 2012

ABSTRACTThe Weather Research and Forecasting (WRF) model is widely used as a regional

climate model for dynamical downscaling in many regions world-wide. This studyevaluates the WRF model for regional climate applications over Thailand, focusing onsimulated precipitation using various convective parameterization schemes available in WRF.The NCEP/NCAR Reanalysis are used as forcing boundary data. The WRF simulations wereperformed at a 20-km grid spacing with an outer 60-km nest, to which the boundary forcingwas applied. Experiments are presented for the year 2005 using four convective cumulusparameterization schemes, namely, BMJ (Betts-Miller-Janjic), GD (Grell-Devenyi), G3D(improved Grell-Denenyi) and KF (Kain-Fritsch) with and without nudging applied to theoutermost nest. Results are evaluated both against station data and gridded observations.In general, the experiments with nudging perform better than un-nudged experiments andthe BMJ cumulus scheme with nudging yields the smallest bias relative to observations.

Keywords: WRF, Regional climate model, dynamical downscaling, model evaluation,convective parameterization, precipitation, Thailand

1. INTRODUCTIONA sensitivity study of atmospheric

model simulations to various cumulusparameterization schemes is an importanttopic not only in numerical weather prediction[1-3] but also in climate modeling studies[4-8]. Previous studies have confirmed thatnone of the available cumulus schemes isable to produce a perfect simulation of

precipitation in their model domains. Wangand Seaman [2] compared precipitationresults from four cumulus parameterizationschemes in MM5 model, namely, theAnthes-Kuo, Betts-Miller, Grell, and Kain-Fritsch schemes. They found that the 6-hprecipitation forecast skill for these schemeswas fairly good, even for higher precipitation

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thresholds. They also found that the forecastskill was generally higher for cold-seasonevents than for warm-season events, andthe model’s precipitation forecast skill wasbetter in rainfall volume than in either theareal coverage or the peak amount.Furthermore, they found that theKain-Fritsch method appeared to performbetter compared to other parameterizations.However, this does not necessarily meanthat the Kain-Fritsch scheme is superiorfor other regions, seasons, or combinationswith other physical processes in the model.For example, the Betts-Miller schemeavailable in the MM5 model has shown thebest performance for maximum rainfallforecasts over the Taiwan area [9] while theGrell cumulus convection scheme has beenfound to perform best in a Bay of Bengaltropical cyclone study [10]. In another study,Lee at al. [7] found that the Anthes-Kuoscheme reproduced the East Asian Summerprecipitation properly while the Grell schemewas on the whole suitable to simulate thegeneral features of East Asian summermonsoon for four months (MJJA) simulationin 1998. Pattanaik et al. [11] compared thesensitivity of three cumulus parameterizationscheme in WRF model in forecasting themonsoon depression over India. They foundthat the overall rainfall forecast associatedwith the monsoon depression was capturedwell in WRF model with KF schemecompared to that of GD scheme andBMJ scheme with observed heavy rainfall.While Mukhopadhyay et al [12] shownthat the Betts-Miller-Janjic (BMJ) cumulusscheme was found to produce better resultscompared to other cumulus schemes overthe same region. Therefore, the evaluationof precipitation and other variables producedby alternative cumulus parameterizationschemes and others physics process availablein models, is an important step in developing

climate simulations over selected regions.When using coarse-scale data (either from

a reanalysis or a GCM) as lateral boundaryconditions driving a regional climate modelwithout any further nudging, the interiormeteorological fields simulated by theregional model can deviate significantlyfrom those of the driving fields [13]. Four-dimensional data assimilation (FDDA)techniques provide one way to constrain theRCM and keep it from diverging too far fromthe coarse-scale fields. The understanding ofthe influence of nudging for regional climatemodeling has become an interested area ofresearch. There has been comparativelylimited effort to understand the effects ofnudging using the regional climate models.For example, Bowden et al. [13] used WRFmodel to evaluate interior nudging techniquesusing the Weather Research and Forecasting(WRF) model for regional climate modelingover the contiguous United States (CONUS)by nudging only at the lateral boundaries, usinggrid point (i.e. analysis) nudging, and usingspectral nudging. They found that using interiornudging reduces the mean biases for2-m temperature, precipitation, 500-hPageopotential height, and 850-hPa meridionalwind throughout the CONUS compared tothe simulation without interior nudging.They also found that the predictions of2-m temperature and fields aloft behavesimilarly when either analysis or spectralnudging is used. For precipitation, however,analysis nudging generates monthlyprecipitation totals, intensity and frequencyof precipitation that are closer to observedfields than spectral nudging. Others study,Lo et al. [14] used WRF regional climateapplication to compare lateral boundarynudging, frequent re-initialization, andanalysis nudging in the one-year simulations.They found that both analysis nudging andfrequent re-initialization are effective to

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constrain the large-scale circulation andimprove the accuracy of the downscaledfields.

In this study, we provide additionalinsights into the advantages of usinganalysis nudging for continuous integrationsin the WRF regional climate modelingapplications. While WRF provides an optionfor spectral nudging, experiments wereperformed only using analysis nudging,following the methods of Mass et al [26]where nudging is applied only to the outerdomain, with the inner domain forced onlyat the boundaries. This approach achievesone of the benefits of spectral nudging,which is to constrain only the large-scalepatterns in the simulation. However, this studydoes not comprehensively address allaspects of using nudging in WRF for regionalclimate modeling, which would includevariations in the nudging parameters, theuse of spectral nudging, and otherapproaches to nudging across model nests.

The objective of this study is toevaluate the precipitation results in eightWRF regional climate simulations with atwo-nest model configuration overThailand using reanalysis boundary

conditions for the year 2005. We comparefour different cumulus parameterizationschemes with and without analysis nudgingapplied to the outer domain. Our analysisfocuses in particular on reproduction ofthe observed precipitation.

This study is focused over Thailand,which is a region characterized by severalseasonal and climatic features with complexterrain and coastlines. Thailand lies within thehumid tropics and remains warm throughoutthe year. Annual mean temperatures vary fromthe north to the south, with different regionalrainfall regimes prevailing in dry northeast andwet southwest. With the exception of thesouthern isthmus, which receives rainfallthroughout the year, Thailand has a dry seasonthat extends from November to Aprilcorresponding with the period of theNortheast monsoon, marked by cool-dry(November to February) and hot-dry (Marchand April) periods. A hot-wet rainy seasonprevails from May to October, correspondingwith the Southwest monsoon. Thailand haswide variations in extremes of temperatureat different locations. The coastal area alongthe Andaman sea (west coast) and China sea(east coast) (as shown in Figure 1) is usually

Figure 1. Model domains (left) and station locations used for model evaluation (right). Theouter domain is 60 km and the nested domain is 20 km horizontal resolution, respectively. Theshading indicates topography.

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warm throughout the year, whereas themountainous area far north is relativelycool in winter.

2. EXPERIMENTAL DESIGN2.1 Regional Climate Model and ForcingData

The model used in this study is theWeather Research and Forecasting (WRF)model, developed at the National Center forAtmospheric Research (NCAR). WRF is anadvanced mesoscale numerical weatherprediction system designed to serve bothoperational forecasting and atmosphericresearch needs (http://www.wrf-model.org).The model configuration used here followsthat of the WRF regional climate model usedin previous studies [15, 16]. The fixed physicsoptions used in this study include the WRFSingle-Moment 6-Class Microphysics (WSM6)scheme [17], Dudhia shortwave radiation [18]and Rapid Radiative Transfer Model (RRTM)long-wave radiation [19], the YonseiUniversity planetary boundary layer (PBL)scheme [20] , and the Noah Land SurfaceModel (LSM) 4-layer soil temperature andmoisture model with canopy moisture andsnow-cover prediction [21]. The fourdifferent cumulus parameterization schemesused in this study are those available in WRFARW vision 3.1: Betts-Miller-Janjic (BMJ)[22], Grell-Devenyi ensemble scheme (GD)[23], Grell 3d ensemble cumulus scheme(G3D), and Kain-Fritsh scheme (KF) [24].The 2.5x2.5 degree NCEP/NCAR Reanalysis[25] were used as the forcing data to provideinitial and boundary conditions. The boundaryconditions were updated 6 hourly. We useNCEP/NCAR reanalysis data to satisfy aprerequisite for estimating climate changeprojections by assessing the ability of themodel to simulate current climate and itsphysical processes. The WRF regional climatemodel was run using one-way nesting at 60-

km and 20-km horizontal grid spacings and28 vertical levels with 10 mb of the top model.The inner domain, with a 20-km grid spacingcovering Thailand and neighboring countries,was analyzed in this study, Eight simulationswere performed with and without nudgingapplied for all four cumulus schemes.Nudging with Newtonian relaxation (grid oranalysis nudging) on the driving fields forwind, temperature and moisture was appliedat all vertical levels in the 60-km WRF outerdomain in order to keep the simulated statesclose to the large-scale states of the reanalysisfields. Nudging was not applied to the inner20-km domain, which allowed the WRFmodel to generate small-scale features. Thisapproach addresses the dual objectives ofdownscaling, which is to generate mesoscalemeteorological details while maintainingconsistency with the large-scale state [26, 27].The nudging terms used in theseexperiments are taken from Mass et al [26](u=0.0001, v=0.0001, t=0.0001,q=0.000001). The equations and furtherdetails of analysis nudging can be found inStauffer and Seaman [28] and Stauffer et al.[29]. The WRF runs were initialized at 0000UTC 1 December, 2004 and ended at 0000UTC 1 January, 2006 for a one-year (2005)simulation. The first month simulation(December 2004) was regarded as modelspin-up and excluded from analysis. Themodel output was hourly. Table 1summarizes the selected physics options andexperimental design while Table 2 lists thesimulation experiment names and designspecifics.

2.2 Observational dataThe observational data used for model

evaluation in this study are measurements madeat 69 stations of the Thai MeteorologicalDepartment (TMD), selected with morethan 95% data available for the year 2005.

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The location of stations is shown in Figure1. Another data set used for spatialevaluation is the high-resolution (0.5 × 0.5degree) gridded data over global land areas[30, 31] available through the ClimaticResearch Unit (CRU TS3.0, 2008) at theUniversity of East Anglia.

3. RESULTSBelow we present results from the eight

WRF simulations to evaluate the relative

performance of the different convectiveparameterizations and the effect ofnudging. Comparisons are made againstboth station observations and griddedprecipitation.

3.1 Station ResultsFigure 2 shows scatter plots of daily

mean precipitation for all seasons betweenstation observations and WRF simulations atall 69 stations. Each solid circle in Figure 2

Table 1. Physics process and options used in this study.

Physics Process and options Selected Option

Microphysics WSM6Cumulus parameterization BMJ, GD, G3D and KFShort-wave radiation DudhiaLong-wave radiation RRTMPlanetary Boundary Layer (PBL) Yonsei UniversityGrid analysis nudging No nudging and nudgingSST update 6 hourly updateRun period 1 year (2005)

Figure 2. Scatter plots of daily mean precipitation between station observed and WRFsimulated at 69 stations for rainy season (a), cool-dry season (b), hot-dry season (c) and annual(d) for BMJ-NONUD (1st column), GD-NONUD (2nd column), G3D-NONUD (3rd

column), KF-NONUD (4thcolumn), BMJ-NUD (5th column), GD-NUD (6th column),G3D-NONUD (7th column) and KF-NONUD (8th column). Each scatter marker representsone station.

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represents the daily mean precipitation atone station for the indicated season. Table3 and Table 4 list the correlation coefficient,regression slope (from Figure 2), bias andabsolute bias (average absolute differencebetween observed and simulated values) ofprecipitation between the 69 stationobservations and WRF simulationsaveraged over rainy, cool-dry, hot-dryseason and annual mean, for the four no-nudge experiments (Table 3) and the fournudge experiments (Table 4). The statisticalsignificance of the correlation coefficientwas found using a Students t-test with 69degrees of freedom (i.e. assuming stationsare statistically independent). In Tables 3and 4, correlation coefficients that aresignificant at 99% are indicated in bold;

correlations are significant for allsimulations except in the hot-dry seasonwhen none of the simulations producesstatistically significant correlation withobservations.

Table 3 and Table 4 indicates that, forthe rainy season, nudging improves allsimulations both for slope and absolutebias, indicating that the nudged simulationsbetter represent both the observed rangeand magnitude of precipitation. Nudgingconsistently improves results for rainy-seasonprecipitation for all parameterizations asindicated by absolute bias, relative bias, andslope. Results for correlation are comparableor better (BMJ, K-F) when nudging isemployed. For the two dry seasons, nudgingmakes less difference in the results, with some

Table 3. The correlation coefficient, regression slope (from Figure 2) , biases and absolutebias of precipitation over all 69 stations between observation and WRF simulation forrainy, cool-dry and hot-dry season and annual using four different cumulusparameterization schemes with outermost domain no nudging applied. The blue colorindicate the best score while the red color is the worst score. The correlation coefficientsthat are significant at 99% are indicated in bold (using Student t-test).

TimeScale

StatisticScore

BMJ-NONUD

GD-NONUD

G3D-NONUD

KF-NONUD

Corr 0.47 0.86 0.78 0.75Slope 0.36 0.62 0.58 2.24Biases 0.33 -3.19 -2.27 8.94Abs bias 2.26 3.23 2.59 9.11Corr 0.87 0.90 0.93 0.81Slope 0.68 0.60 1.08 0.54Biases 0.04 -1.05 -0.39 0.84Abs bias 1.01 1.08 0.98 1.60Corr 0.15 0.21 0.25 0.28Slope 0.06 0.17 0.31 0.43Biases -1.3 -0.63 -0.34 0.41Abs bias 1.31 1.06 1.13 1.25Corr 0.47 0.88 0.79 0.60Slope 0.32 0.71 0.88 1.49Biases 0.08 -2.09 -1.37 4.99Abs bias 1.25 2.09 1.71 5.07

Rain

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parameterizations performing better andsome worse. For the hot-dry season, the WRFresults perform relatively poorly for allconfigurations, making comparativeevaluation difficult. Among parameteriza-tions, the BMJ scheme shows the smallestabsolute and total bias, both for no-nudgedand nudged cases. This scheme, however,exhibits the lowest correlation (0.47) andslope (0.47) when used without nudging;other simulations show higher correla-tions, ranging from 0.75 to 0.86. The useof nudging, however, yields a much betterimprovement in the correlation score forBMJ than for other parameterizations, andBMJ-NUD has doubled the correlationscore of BMJ-NONUD which is comparableto those of the other nudged experiments.

For the cool-dry season, all simulationsshow relatively high correlation (>0.8). Thereare substantial differences among thesimulations in regression slopes, with allsimulations underestimating the observed

range except for the KF-NUD (1.2) andG3D-NONUD (1.08) cases. The un-nudgedBMJ simulation produces the smallestbias, with a slight increase in bias withnudging. Overall, however, nudging makeslittle difference in model scores for theexperiments.

For the hot-dry season, all experimentsshow rather low correlations (< 0.29) andregressions slope (

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However, nudging results in a positive biasfor the hot-dry season. The BMJ-NONUD shows slight overestimation inthe rainy and cool-dry seasons butunderestimation in the hot-dry season withan average bias of -1.3 mm/day. The BMJmethod yields the smallest bias comparedto other simulations (except for the un-nudged hot-dry case) and the use ofnudging generally reduces the bias exceptin the cool dry season when nudgingintroduces a small dry bias.

The temporal correlation coefficientsof simulated and observed daily precipita-tion at all 69 stations in Thailand showmostly low correlation (not shown). Highcorrelation of daily precipitation requiresthe simulation to accurately represent thetiming of convective precipitation eventsto within a 24-hour period, which is ademanding test given the coarse (2.5×2.5degree) forcing data. To examine thecorrelation over longer time scales, whichare better resolved by the reanalysis, the5-day and 30-day timescales are examinedfollowing Zhang et al. [16]. The runningmeans of the observed and WRF simulatedprecipitation time series at each station wascalculated and the correlation coefficientswas calculated at corresponding stations.Figure 3 shows the 5-day and 30-day runmean temporal correlation coefficients atall locations across Thailand. The number onthe lower right corner of each plot indicatesthe averaged correlation over all stations. Asin Zhang et al. [16], the correlation coefficientsof all experiments have increased whenaveraging precipitation over increasingnumber of days. At 30-day averaging, thecorrelation is larger than 0.60 andessentially indicates that the simulationcaptures the annual cycle well.

3.2 Heavy and light precipitationFor climate studies, the probability of

precipitation extremes, both dry days andheavy precipitation, is more important thanthe timing of events, which is reflected inthe temporal correlations discussed above.To compare the simulated precipitationintensities with the observations, Figure 4shows the histogram of daily precipitationfrom all 69 stations. We find that the BMJscheme reproduces the frequency ofprecipitation at all thresholds, with orwithout nudging. The GD and G3Dschemes over-predict light precipitation(

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Figure 3. Temporal correlation of 5-day running mean (a) and 30-day running mean (b)of precipitation for all 69 stations between observation and WRF simulation for BMJ-NONUD (a), GD-NONUD (b), G3D-NONUD (c), KF-NONUD (d), BMJ-NUD(e), GD-NUD (f), G3D -NUD (g) and KF-NUD (h). The number on lower-right cornerof each plot is the averaged correlation over 69 stations.

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Figure 4. Histograms of daily of precipitation averaged over all 69 stations.

Figure 5. Correlation (a) and biases (mm/day) (b) of precipitation between the griddedCRU observations and station observations at 69 stations, and seasonal and annual meanCRU gridded precipitation (c). The numbers on the lower-right corners of (a) and (b) are theaveraged value over 69 stations.

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of south-westerly winds associated with thesouth-Asian monsoon and the cycloneseason, the primary mechanisms for heavyprecipitation over the region.

Figure 6 shows the bias between theWRF and CRU precipitation. We findsubstantial differences among simulationsfor the rainy season (1st row). The BMJ-NONUD simulation produces a slight drybias in southern Thailand but wet bias innorthern Thailand. Both these deficienciesare improved in the BMJ-NUD case. TheGD and G3D simulations mostly under-predict precipitation over Thailand, withsubstantial improvement with nudgingapplied. The KF simulated precipitation istoo high throughout Thailand andneighboring countries with only modestimprovement from nudging. A consistentfeature of most simulations (GD-NUD,

G3D-NUD KF-NONUD and KF-NUD)is a relatively high wet bias in the rainyseason along the Thanon Thongchai andTanao Sri mountain ranges that form theThailand-Burma border. Additionally, awet bias is also noted along the mountainoussouthwestern coast of Cambodia in allexperiments. In the GC-NUD and G3D-NUD experiments, there is also a dry biason the opposite slopes of these mountains.During the south-west monsoon, thesemountains form an orographic barrier,with heavy precipitation along the westernand southern (i.e. windward) sides (seeobservations, Figure 5c) and rain shadowingon the opposite slopes. Despite overallimprovement in the simulation withnudging, the nudged experiments tend toexaggerate this feature, with the BMJ-NUDshowing distinctly better performance.

Figure 6. Seasonal mean precipitation bias (mm/day) between model simulated andobserved for rainy (1st row), cool dry season (2nd row), hot dry season (3rd row) andannual mean (4th row) for BMJ-NONUD (1st column), GD-NONUD (2nd column),G3D-NONUD (3rd column), KF-NONUD (4th column), BMJ-NUD (5th column), GD-NUD (6th column), G3D -NUD (7th column) and KF-NUD (8th column).

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The cool-dry season (Figure 6 2nd row)is characterized by dry conditions over theNorthern, Central and NortheasternThailand and heavy precipitation oversouthern Thailand. Other than BMJ-NONUD and KD-NONUD, allexperiments produce similar precipitationpatterns to the observed over the northernpart, but vary over the south. The GDsimulations show a dry bias over the entiresouth. The other simulations consistentlyproduce too little precipitation along thesouthern coast and too much precipitationover the southern Malay Peninsula. Thispattern suggests a general southwarddisplacement of intense convection by theregional simulations, and may also resultin part from deficiencies in the large-scaleforcing.

Precipitation is infrequent during thehot-dry season (Figure 6 3nd row) and ismostly associated with scatteredthunderstorms throughout the region. Theexperiments without nudging show smallwet or dry biases depending on thelocation. With nudging, all parameteriza-tions over-estimate precipitation, with thesmallest bias noted for the BMJ scheme.

In the annual mean (Figure 6 4nd row),precipitation bias is dominated by that inthe rainy season, but in many cases, seasonalbiases cancel out each other resulting inbetter performance in reproducing theannual total rainfall. For the annual meanresults, there is a clear improvement forsimulations using nudging and for the BMJparameterization.

3.4 The southwest monsoonThe monsoon, a system of winds that

influences the climate of large area and thatreverses direction with the seasons.Monsoons are caused primarily by themuch greater annual variation in

temperature over large areas of land thanover large areas of adjacent ocean water.This variation causes an excess ofatmospheric pressure over the continentsin the winter, and a deficit in the summer.The disparity causes strong winds to blowbetween the ocean and the land, bringingheavy seasonal rainfall. In southeast Asia, awind that is part of such a system and thatblows from the southwest in the summer andusually brings heavy rain. Figure 7 showsthe seasonal averaged wind directionsimulated by WRF model using fourdifferent cumulus parameterizationschemes with nudging for June-July-August, a typically strong southwestmonsoon season. It was found that allcumulus parameterization schemes cancapture the southwest monsoon well,compared to NCEP/NCAR reanalysisdata. Also, Figure 8 shown daily averagedwind direction simulated from WRFmodel compared to observation andNCEP/NCAR reanalysis at UbonRatchathani (Figure 8.a) and Songkhla(Figure 8.b) stations, to show how well theWRF model capture the monsoon onset(wind reversal). Two stations are selectedto show the daily surface directions avoidthe topography affect. It was found thatall schemes can capture the temporal of themonsoon onset (wind reversal) wellcompare to both the observation andreanalysis data.

4. CONCLUSIONSIn this paper, we have presented

precipitation results from eight WRF regionalclimate model simulations covering a domainof southeast Asia centered over Thailand at20-km resolution that is nested into a 60-kmouter domain. The experiments simulatedthe year 2005 using large-scale forcing fromthe NCEP/NCAR reanalysis. The results

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Figure 7. JJA mean surface wind simulated using a) BMJ scheme, b) GD scheme, c)G3D scheme and d) KF scheme compared to NCEP/NCAR reanalysis data.

Figure 8. Daily mean surface wind direction at Ubon Rtachathani (a) and Songkla (b)station from WRF simulations using BMJ, GD, G3D and KF schemes, compared tostation observed (OBS) and NCEP/NCAR reanalysis data (NNRP).

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are evaluated against two observationaldatasets to establish both the overall skillof the regional model and its dependenceon convective parameterization and the useof nudging. Four different convectiveparameterizations are used with and withoutnudging applied. In the nudged experiments,Newtonian relaxation is applied to the outmost(60-km) grid to maintain the large-scalestructure of the forcing fields across theinterior of the domain.

Our results show that differences incumulus parameterizations and the applicationof nudging can have substantial impacts onsimulated convection and precipitation.Deficiencies in simulated precipitation canresult directly from the regional model, theconvective parameterization used, or fromthe large-scale boundary conditions (i.e., thereanalysis fields). It is difficult or impossibleto isolate the sources of these errors sincesome errors may be related to deficiencies ininternal model components or to theinteractions and coupling of a cumulusparameterization scheme with other modelcomponents [2].

With these limitations in mind, the resultspresented here can give some guidance toconfiguring WRF for simulations oversoutheast Asia and the suitability of themodel for regional climate simulations.There was no clear leader among theexperiments that consistently performedbetter than the others. However, in general,nudging improved most simulations,especially in the rainy season. Overall, theBMJ scheme produced the smallest biases,both averaged over the domain and locally.In particular, BMJ produced the bestprobability distribution of precipitation,which is critical for climate simulations.

The WRF simulations show realisticmonsoon flow over Thailand, and capturewell the monsoon onset.

6. ACKNOWLEDGEMENTSThis research has been financially

supported by the Science and Technology(S&T) Program, University of WashingtonBothell for VISIT Program internship andOffice of the Higher Education Commission, Ministry of Education, Thailand. Thepartially supported by the Graduate School,Faculty of Science, Chiang Mai Universityand Centre for Environmental Health,Toxicology and Management of Chemicals(ETM), Thailand. Global Reanalysis datafrom NCAR/NCEP, WRF model fromNCAR and meteorological stationsobserved data from Thai MeteorologicalDepartment are highly appreciated. Wealso appreciate Rick Steed and YongxinZhang (now at NCAR) at University ofWashington to provide model configuremethod and scripts. Dr. Cindy Bruyere atNCAR provided modified KF scheme (notincluded in this study) to compare withoriginal KF scheme result are alsoappreciated. We also highly appreciate theJoint Institute for the Study of theAtmosphere an Ocean (JISAO), Universityof Washington as the constructive host andcomputer resource.

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