Weather Forecasts by the WRF-ARW Model with the GSI Data

22 Pages·2009·9 MB·English

AUGUST2009 XU ET AL. 987
Weather Forecasts by the WRF-ARW Model with the GSI Data Assimilation
System in the Complex Terrain Areas of Southwest Asia
J.XU
UniversityCorporationforAtmosphericResearch,Boulder,Colorado,andAirForceWeatherAgency,OffuttAirForceBase,
Nebraska,andJointCenterforSatelliteDataAssimilation,CampSprings,Maryland
S. RUGGANDL. BYERLE
AirForceWeatherAgency,OffuttAirForceBase,Nebraska
Z.LIU
NationalCenterforAtmosphericResearch,Boulder,Colorado
(Manuscriptreceived10November2008,infinalform5January2009)
ABSTRACT
This paper will first describe the forecastingerrors encountered from runningthe National Centerfor
AtmosphericResearch(NCAR)mesoscalemodel(theAdvancedResearchWeatherResearchandFore-
castingmodel;ARW)inthecomplexterrainofsouthwestAsiafrom1to31May2006.Thesubsequent
statisticalevaluationisdesignedtoassessthemodel’ssurfaceandupper-airforecastaccuracy.Resultsshow
that the model biases caused by inadequate parameterization of physical processes are relatively small,
exceptforthe2-mtemperature,ascomparedtothenonsystematicerrorsresultinginpartfromtheuncer-
tainty in the initial conditions. The total model forecast errors at the surface show a substantial spatial
heterogeneity;theerrorsarerelativelylargerinhighermountainareas.Theperformanceof2-mtemperature
forecastsisdifferentfromtheothersurfacevariables’forecasts;themodelforecasterrorsin2-mtemperature
forecastsarecloselyrelatedtotheterrainconfiguration.Thediurnalcyclevariationofthesenear-surface
temperatureforecastsfromthemodelismuchsmallerthanwhatisobserved.
Second,inordertounderstandtheroleoftheinitialconditionsinrelationtotheaccuracyofthemodel
forecasts,thisstudyassimilatedaformofsatelliteradiancedataintothismodelthroughtheJointCenterfor
SatelliteDataAssimilation(JCSDA)analysissystemcalledtheGridpointStatisticalInterpolation(GSI).
Theresultsindicatethatonaverageovera30-dayexperimentforthe24-and48-h(second24h)forecasts,the
satellitedataprovidebeneficialinformationforimprovingtheinitialconditionsandthemodelerrorsare
reducedtosomedegreeoversomeofthestudylocations.Thediurnalcycleforsomeforecastingvariablescan
beimprovedaftersatellitedataassimilation;however,theimprovementisverylimited.
1. Introduction layastotheeast,andcrossedbytheimpressiveTauros,
Zagros, and Hindu Kush mountains. A few previous
WeatherpredictioninsouthwestAsia(SWA)isoften
modelstudies(EvansandSmith2001,2006;Evansetal.
very complex because of mesoscale variations induced
2004;Zaitchiketal.2007a,b;MarcellaandEltahir2008)
bythediversetopography.Thisispredominatelyasemi-
providedsomeinterestingresultsforthebasicweather
aridtoaridregionsurroundedbytheBlackandCaspian
simulationinSWAusingaregionalclimatemodel[the
Seas to the north, the Mediterranean to the west, the
second-generation National Center for Atmospheric
Arabian Sea and Persian Gulf to the south, the Hima-
Research(NCAR)RegionalClimateModel(RegCM2)]
or the fifth-generation Pennsylvania State University–
NCARMesoscale Model(MM5)model.Theypointed
Correspondingauthoraddress:Dr.JianjunXu,JointCenterfor
outthattheregionalmodelhasdifficultyproducingan
SatelliteDataAssimilation,WWB,5200AuthRd.,CampSprings,
accurate simulation of precipitation in certain subre-
MD20746.
E-mail:[email protected] gions, which is related to an accurate description of
DOI:10.1175/2009WAF2222229.1
(cid:2)2009AmericanMeteorologicalSociety
988 WEATHER AND FORECASTING VOLUME24
stormtracks,topographicinteractions,andatmospheric considerable effort has focused on improving the esti-
stability. However, for a next-generation mesoscale mates of the model initial states through advanced
forecast model (the Advanced Research Weather Re- techniques. One such technique is data assimilation.
search and Forecasting model, ARW) developed by Thus,thesecondpurposeofthispaperistoevaluatethe
NCAR(Skamarocketal.2005),theperformanceofthis impact of data assimilation on weather forecasts over
model employed in the operational forecasts over this SWAareas.
region is not fully understood. One of the purposes in Thispaperisorganizedasfollows.Section2describes
this paper is to evaluate the performance of the ARW the real-time configuration of WRF-ARW as run op-
modelinSWAcomplexterrain. erationally within SWA. The observational datasets
This evaluation primarily concentrates on the fore- usedintheverificationaregiveninsection3.Section4
casts of wind, temperature, and precipitation since explains the methodology used in the evaluation. The
SWA is dominated by hot, dusty, windy weather results of the forecast error for the May 2006 case are
(Agrawala et al. 2001). During the transitional season presentedinsection5.Section6investigatestheimpact
from winter to summer, the temperature and wind in- ofdataassimilationontheforecasts.Finally,asummary
crease substantially; contrastingly, the precipitation anddiscussionaregiveninsection7.
decreases significantly. Subsequently, during this sea-
sonal transition, the occurrence of blowing sand/dust
and unstable local-scale weather events increases as
2. WRF-ARW model and forecasts
well, and the prediction accuracy of these events is
highlydependentupontheaccuracyofthetemperature, The weather model used in this study is the WRF
precipitation,andwindforecastsfromthemodel. model utilizing the Advanced Research WRF dynami-
SomerecentstudieshaveevaluatedtheARWmodel calcore(WRF-ARW;Michalakesetal.2001;Skamarock
based on objective error statistics for precipitation et al. 2005), which is a nonhydrostatic, fully compress-
forecastsovertheUnitedStates.ChengandSteenburgh ible, primitive equation model. Lead institutions in-
(2005)producedsurfacesensibleweatherforecastswith volved in the effort include NCAR, the Air Force
ARWand Eta models overthewesternUnitedStates. Weather Agency (AFWA), the National Oceanic and
Theirresultssuggestthatimprovementsininitialization Atmospheric Administration(NOAA), andother gov-
maybeasimportant,ormoreso,thanimprovementsin ernmentalagenciesaswellasseveraluniversities.WRF
the physics for land surface processes. Gallus and is built around a software architectural framework
Bresch (2006) compared the impacts of Weather Re- within which different dynamical cores and model
search and Forecasting (WRF) model dynamics core, physics packages are accessible within the same code.
physicspackage,andinitialconditionsonwarmseason Within the WRF framework, it is possible to mix the
rainfall forecasts over the central United States. They dynamical cores with differing physics packages to op-
foundthatthesensitivityofrainfallforecastsisafunc- timize performance since each core has strengths and
tionofmodelphysics,dynamics,andinitialconditions, weaknessesindifferentareas.WRF-ARWusesaterrain-
and they are situationally dependent on which domi- following pressure coordinate and the Arakawa C grid
nates most. For heavier rainfall, sensitivity to initial staggering.
conditions is generally less substantial than the sensi- Similar to AFWA operational setups, a 15-km grid
tivity to changes in the dynamic core or physics. For spacing centered over SWA (Fig. 1) is used to encom-
light rainfall, the WRF model using NCAR physics is passtheregion’scomplextopographyanditsassociated
much more sensitive to a change in the dynamic core spatial variability in surface characteristics. To assess
than the WRF model using the National Centers for modelpredictiveskill,48-hforecastsaremadeforeach
Environmental Prediction (NCEP) physics. It is very daystartingat0000UTCfortheperiod1–31May2006.
clearthattheerrorsintheweatherforecastsarecaused Forecasts without data assimilation are labeled CTRL
by many reasons. One of these reasons isattributed to inordertodistinguishthemfromtheforecastswithdata
the imperfection of the numerical weather models’ assimilationfoundinsection6.Theinitialatmospheric
representation of the actual atmosphere. However, as andlateralboundaryconditions,includingsoilmoisture
Lorenz(1963)pointedout,themostfundamentalcause and temperature, are taken from the NCEP Global
offorecastfailureisthattheatmosphereissensitiveto ForecastSystem(GFS)real-timeforecastsat3-hinter-
initial conditions (ICs). This means that an arbitrarily vals, which are gridded to a horizontal resolution of
small error in the analysis of the initial state of the at- 18318.ThroughtheWRFPreprocessingSystem(WPS),
mosphere can have an overwhelming effect in a finite the global soil categories, land-use category, terrain
amount of time. Therefore, it is not surprising that height, annual mean deep soil temperature, monthly
AUGUST2009 XU ET AL. 989
FIG.1.Domainofthemodelandsubregiondefinitions.Shadingindicatestheelevationofterrain(m).Thesubregionsaredefinedas
northernIraq(A;348–368N,418–438E),northwesternIran(B;348–368N,468–488E),north-centralIran(C;348–368N,548–568E),central
Afghanistan(D;348–368N,668–688E),thewesternHimalayanMountains(E;348–368N,748–768E),westernSaudiArabia(F;228–248N,
418–438E),easternSaudiArabia(G;228–248N,518–538E),theArabianSea(H;228–248N,638–658E),andnorthwesternIndia(I;228–248N,
708–728E).
vegetation fraction, monthly albedo, maximum snow with rain gauge data and model analysis. The merging
albedo, and slope category are interpolated into the technique has been shown to significantly reduce bias
modelgridsofthestudydomain.Thephysicspackages andrandomerrorcomparedtoindividualprecipitation
chosenaretheWRFsingle-momentfive-class(WSM5) datasources,thusincreasingtheaccuracyoftherainfall
microphysics scheme, the Yonsei University planetary estimates (Xie and Arkin 1996a). Geostationary satel-
boundary layer (YSUPBL) scheme, the Noah land litedataareutilizedforthedeterminationofcloud-top
surface scheme, the Grell–Devenyi ensemble cumulus temperature. Meteosat-5 thermal infrared (IR) digital
scheme,theRapidRadiativeTransferModel(RRTM) dataat5-km-pixelresolutionareaccessedevery30min
longwave radiation, and the Dudhia shortwave radia- and then reformatted and converted to a geographic
tionscheme. grid with 0.18 resolution. The grid is 751 3 501 points,
whichbeginswithpoint(1,1)at108N,208Eandendsat
point (751, 501) at 608N, 958E. A horizontal resolution
3. Observed and analyzed datasets of 0.18 was chosen for the estimate computations to
correspond with the absolute positioning error for the
a. Observed precipitation
satellite of approximately 10 km. Arrays are used to
The observed precipitation data are taken from the accumulate the occurrences of cloud-top temperatures
Climate Prediction Center’s(CPC) Famine Early Warn- below temperatures of 235 and 275 K. Rain gauge re-
ing System (FEWS) program, which is derived from ports transmitted via the Global Telecommunications
geostationarysatelliteretrievalprecipitationdatamerged System(GTS)arereceivedevery6handareutilizedin
990 WEATHER AND FORECASTING VOLUME24
TABLE1.Theaverageheight(m)oftopography,vegetationtype(Veg),andsoiltype(Soil)intheninesubregions(definedinFig.1)
overSWA.
A B C D E F G H I
Height 328 2557 737 3833 4839 958 67 0 75
Veg Barren Grass Barren Shrubland Woodedtundra Barren Barren Water Mixeddryland/cropland
Soil Loam Loam Clayloam Loam Loam Sandyloam Loam Water Loam
the CPC Climate Assessment Data Base (CADB) for This evaluation is designed to present the model er-
monitoring of climate anomalies. Automated quality rorsofsurfacetemperatures,precipitation,windspeeds,
controloftheseGTSobservationswithintheCADBis and upper-atmospheric variables for both 24- and 48-h
donepriortotheprocessingofprecipitationestimates. (e.g.,thesecond24h)forecasts.Thestatisticalmeasures
used to quantify model forecast performance are bias
b. Observed temperature (forecast2observation),mean-squareerror(MSE),and
error standard deviation (SD). For purposes of inter-
The maximum and minimum temperatures at 2-m
pretation,theMSErepresentsthetotalmodelforecast
levels with 0.58 3 0.58 gridded datasets are created by
errorincludingcontributionsfrombothsystematicand
theCPC,whichistakenfromobservationalstationsof
nonsystematic/random errors. Systematic error may
the World Meteorological Organization (WMO) GTS
becausedbyaconsistentmisrepresentationofphysical
datasets. The interpolation method is based on the
parameters such as radiation or model convection. Non-
previousrainfallestimationalgorithm(Xieetal.1996b).
systematic errors are caused by uncertainties in the
model initial conditions or unresolvable differences
c. Analyzedtemperature and wind field
in the scales between the forecasts and observations
The temperature and wind fields are taken from the (NutterandManobianco1999).
NCEP GFS analysis data, which is gridded to a hori- If X represents any of the parameters under consid-
zontalresolutionof18318. erationforagiventimeandverticallevel,thenforecast
errorisdefinedasX95X 2X ,wherethesubscriptsf
f a
andadenoteforecastandanalyzed/observedquantities,
4. Topography and evaluation method
respectively.GivenNvalidpairsofforecastsandanal-
To investigate the spatial heterogeneity of the com- yses,thebiasiscomputedas
plexterraininSWA,ninerepresentativesubregionsare
depictedinFig.1.TheyaredefinedasnorthernIraq(A; N
1 (cid:2)
348–368N, 418–438E), northwestern Iran (B; 348–368N, bias5X95 X9; (1)
N i
468–488E), north-central Iran (C; 348–368N, 548–568E); i51
central Afghanistan (D; 348–368N, 668–688E), west Hi-
themean-squareerroriscomputedas
malayan Mountains (E; 348–368N, 748–768E), western
Saudi Arabia (F; 228–248N, 418–438E), eastern Saudi N
Arabia (G; 228–248N, 518–538E), the Arabian Sea (H; MSE5 1 (cid:2)(X9)2; and (2)
N i
228–248N, 638–658E), and western India (I; 228–248N, i51
708–728E).
theSDerroriscomputedas
The nine subregions effectively represent the heter-
ogeneity of the complex terrain in SWA. Table 1 dis- 2 N 31/2
plays the average topography (Hgt), vegetation type SD541 (cid:2)(X9(cid:2)X9)25 . (3)
(Veg),andsoiltype(Soil)overthesenineregions.Ex- Ni51 i
ceptforthewatertypeintheArabianSea(markedH),
the soil types in all other eight regions are loam; in In Eqs. (1)–(3), N is used rather than N-1 so that a
addition, the vegetation types include barren, grass, decompositionfollowingMurphy[(1988),Eq.(9)]could
shrubland,woodedland,mixeddry/cropland,andwater. beappliedtotheMSE:
Threeregions(B,D,andE)withterrainabove2500m
are covered by short plants with grass (B), shrubland MSE5(X9)21(SD)2. (4)
(D),andwoodedtundra(E).Threeregionswithterrain
under1000m(A,C,andF)andthetwoplainsregions Therefore, the total model forecast error (e.g., MSE)
(GandI)arepracticallyfreeofanyplants. consists of contributions from model squared biases
AUGUST2009 XU ET AL. 991
FIG.2. Squaredbias,MSE, squaredSD error, andfractionof squaredbias to MSE (Er)for 2-mtemperature
(T ,8C)forecastsfrom1to31May2006.Resultsareplottedforaveraged24-and48-hforecastsasafunctionofthe
2m
locations(A,B,C,D,E,F,G,H,andI)definedinFig.1.Unitis(8C)2in(a),(b),and(c).
ðX9Þ2 (i.e., systematic error) and the squared standard sultsarefromforecastsmadeduringthe30-dayperiod
deviation (SD)2 error (i.e., nonsystematic error) in the starting1through30May2006.
forecastandobserveddata.Afractionisdefinedtoin-
a. 24- and 48-h forecasts
dicate the ratio of systematic error in the total model
forecasterrorasfollows: 1) TEMPERATUREAT2 M
Squared biases (WRF forecasts 2 GFS analysis) in
E 5[(X9)2/MSE]100%. (5)
r 2-m temperature forecasts vary with terrain elevation
(Fig.2a).Biasesarelargeroverhigh-terrainareas(E,B,
NoteinEq.(5)thatifthemodelbiasislessthan50%,
and D) for the 24- and 48-h forecasts. Meanwhile, the
mostoftheMSEisduetorandom,nonsystematic-type
biasesaresignificantlysmallerinlow-terrainregions(A,
variabilityintheerrors.
C,F,G,andI)orwaterareas(H).
EventhoughthemagnitudeofthesquarederrorSD
5. Forecastserror
(Fig. 2c) in the highest-terrain region (E: western
Inthefollowingsection, WRF-ARWmodelforecast HimalayanMountains)isnearlyequivalenttothatofthe
errorcharacteristicsfor24-and48-h(e.g.,second24h) forecastbias,itisverysmallintheotherareas.However,
forecasts and the diurnal variation are described. Re- the biases and corresponding MSEs are comparable in
992 WEATHER AND FORECASTING VOLUME24
FIG.3.Squaredbias,MSE,squaredSDerror,andfractionofsquaredbiastoMSE(Er)ofT2mfor(a)–(d)24-and
(e)–(h)48-hforecastsfor30-dayaveragesfrom1to31May2006.Unitis(8C)2in(a)–(c)and(e)–(g).
magnitude over most of other mountainous areas To illustrate the above point, squared biases, MSEs,
(Fig.2b).ThefractionofthesquaredbiasestotheMSEs and squared SD errors in the whole of SWA are
(Fig.2d)isgreaterthan50%inmostoftheareas,which depictedinFig.3.Forthe24-hforecasts,thetotalmodel
showed clearly that a large contribution to the total forecast errors are dominated by the model systematic
modelforecastserrorintheselocationsisderivedfrom errors (Figs. 3a–c). The fraction of squared biases to
a systematic model error. The results indicate an ap- theMSEs(Fig.3d)exceeds50%;thedistributionofthe
parent model deficit in the description of surface tem- total model forecast errors is also dependent on the
peratureinhigh-terrainareas. configuration of the terrain (cf. Figs. 3a,b and Fig. 1).
AUGUST2009 XU ET AL. 993
FIG.4.SameasinFig.2butforprecipitation(mmday21)forecasts.Unitsare(mmday21)2in(a)–(c).
The48-hforecasterrorsarealittlehigherthanthe24-h model forecasts error comes from a nonsystematic
forecasterrors(Figs.3e–h). model error. These results indicate an apparent model
problem in the description of the initial conditions or
themodelresolution.The48-hforecasterrorsaremuch
2) PRECIPITATION
higherthanthe24-hforecasterrorsinmostareas.
In contrast, the precipitation MSEs in the 24-h fore- For the whole study domain, the MSEs in the 24-h
casts are dominated by the squared SD error (Fig. 4) forecasts are obviously dominated by the model non-
over all nine selected subregions. The biases are not systematic errors (Figs. 5a–c). The fraction of squared
correlatedtotheheightoftheterrain.Themaximumof biases to the MSEs (Fig. 5d) is under 50% except for
the squared bias (Fig. 4a) over the highest-terrain re- some Himalayan Mountains areas. The distribution of
gion is much smaller than the squared SD error. The the total model forecast errors has nothing to do with
fraction of the squared biases to the MSEs (Fig. 4d) is thestructureofthehigherterrain.Theareasofthe48-h
far less than 50% in all selected subregions, which forecast errors greater than 20 mm2 clearly expanded
showed clearly that a larger contribution to the total (Figs.5e–h).
994 WEATHER AND FORECASTING VOLUME24
FIG.5.SameasinFig.3butforprecipitation(mmday21)forecasts.Unitsare(mmday21)2in(a)–(c)and(e)–(g).
3) WINDSPEEDAT10 M overthewesternHimalayanMountainsregion,eastern
Saudi Arabia, and western India are almost zero. The
Similartoprecipitation,theMSEsin10-mwindspeed fractionsofthesquaredbiasestotheMSEs(Fig.5d)are
are largely associated with the nonsystematic errors in under50%overalloftheselectedareas.
most of the subregions (Figs. 6a–c). The largest model Over the SWA domain, the MSEs of the 10-m wind
biasoccursovernorthwesternIran(Fig.6a).Thebiases speedinthe24-hforecastscorrespondfairlywelltothe
AUGUST2009 XU ET AL. 995
FIG.6.SameasinFig.2butfor10-mwindspeed(ms21)and24-hforecastsonly.Unitsare(ms21)2in(a)–(c).
low SD errors (Figs. 7b and 7c). The forecast errors differenceamongthetemperature,precipitation,andwind
from the systematic error in the western mountains of speed.Theinaccuraciesinthe2-mtemperatureforecasts
Iranarerelativelylargevalues(Fig.7a).The10-mwind comemainlyfromsystematicerrors,whicharecontrolled
speed statistical fields are quite different from the 2-m largelybythephysicalrepresentationwithinthemodel.In
temperaturefields,andthenonsystematicmodelerrors contrast, the inaccuracies in the precipitation and 10-m
compose a much larger portion of the total forecast windspeedforecastsaredominatedmorebynonsystem-
errorsfor2-mtemperatureforecasts(Fig.7d).The48-h aticerrors,whichwepostulatearederivedfromtheran-
forecasterrorsaresimilartothe24-hforecasterrorsin dominadequaciesoftheinitialconditions.
mostoftheareas(notshown).
4) TEMPERATUREAT500 HPA
TheaboveresultssuggestthattheMSEsnearthesurface
containasubstantialspatialheterogeneity,ascanseenby The squared bias is very small except that the Hi-
therelativelylargererrorsinthehighermountainousareas. malayanMountainsregion(E)getsupto178Cfor24-h
However, the source of the errors indicates a significant forecasts(Fig.8a)andthereisaslightlylargervaluefor
996 WEATHER AND FORECASTING VOLUME24
48-h forecasts (not shown). The larger magnitudes of
the MSEs is randomly distributed over central Saudi
Arabia, southeastern Iraq, northwestern Iran, and the
western Himalayan Mountains (Fig. 8b). The corre-
spondingSDerror(Fig.8c)revealsthatnonsystematic
errorscomposeasubstantialportionofthetotalerror.
The fraction of squared biases to the MSE (Fig. 8d) is
far less than 50% except for the western Himalayan
Mountains region (E), which showed clearly that a
larger contribution to the MSEs comes from a nonsys-
tematic total model forecast error. Compared to 24-h
forecasts,the48-hforecasts’biasishigherovermostof
studyareas(notshown).
5) WINDSAT200HPA
Similar to theupper-level temperature forecasts,the
wind forecasts (the first 24-h forecasts shown only) at
200hPa(Fig.9)indicatethattheMSEsaredominated
bynonsystematicerrorsineitherthezonalormeridio-
nal wind component or both. For the zonal wind fore-
casts,thelargeMSEoverHimalayanMountainsregion
is consistent with nonsystematic error; in addition, the
Arabian Sea also has a strong nonsystematic error sig-
nature.Forthemeridionalwindcomponent,thelarger
forecast errors occur over a different place from the
zonal wind forecasts. The larger MSEs for the zonal
windforecastsintheHimalayanMountainsregiondis-
appearinthemeridionalwindfield.
Tosummarize,the2-mtemperatureforecasterroris
typicallycausedbysystematicerrorandismostclosely
associated with the elevated terrain; by contrast, pre-
cipitation, 10-m wind speed, and upper level forecast
errors are dominated by the nonsystematic errors,
whichdonotappeartobecorrelatedwithterrain.
b. Diurnal cycle variation
Basedonmodelforecasts,thesouthwestAsiandomain-
widemeanofthe2-mtemperatureexhibitsaminimum
near0000UTCfollowedbyasharpincreasetoamax-
imum near 1200 UTC (not shown). The differences in
the variables (temperature, and wind speed) at maxi-
mum(1200UTC)andminimum(0000UTC)timesare
definedasthediurnalcyclevariationinthisstudy.
ThesouthwestAsiaarea’smeandiurnalcycleof2-m
temperature over the 30-day study period (Fig. 10a)
shows that the amplitude of the temperature diurnal
cycleformodelforecastsisconsiderablylowerthanthe
value in the WMO GTS observations. Note a slight
decrease in temperature diurnal cycle on 2, 7, and 17
JanuaryinGTSobservationsthatisnotobservedinthe
model forecasts. These two points indicate that the
near-surface temperature diurnal cycle in the model
FIG.7.SameasFig.3butfor10-mwindspeedand24-hforecasts
only.Unitsare(ms21)2in(a)–(c). forecastshasseriousproblems.