HRRR-AK:StatusandFutureofaHigh-Resolu8onForecastModelforAlaska

TrevorAlco*1,JiangZhu2,DonMorton3,MingHu4,Cur8sAlexander11ESRLGlobalSystemsDivision,Boulder,CO

2GINA/UAF,Fairbanks,AK3BorealScienGficCompuGngLLC,Fairbanks,AK

4CIRA/ColoradoStateUniv.,Boulder,CO

VirtualAlaskaWeatherSymposium–23Aug2017

AUniqueandChallengingEnvironment

Complexterrain

Arc:cclimate

Sparseobserva:ons

Travelbyair

PFYU 182303Z 00000KT 1SM BR CLR M43/ A2962 RMK AO2

6192m

106m

RAP/HRRRWeatherForecastSuite

Ini8al&LateralBoundaryCondi8ons

Initial & Lateral Boundary Conditions

13-km Rapid Refresh (RAP)

3-km High-Resolution Rapid Refresh

750-m HRRR nest Wind Forecast Improvement

Project Experiment (ongoing)

3-km Storm-Scale Ensemble Analysis and

Forecast (HRRRE) 70% CONUS HRRR

Experimental (ongoing)

3-km High-Resolution Rapid Refresh Alaska

(HRRR-AK)

3-km High-Resolution Time Lagged Ensemble (HRRR-TLE)

3-km HRRR-Smoke (VIIRS fire data)

NCEP-GFS

Crossover in forecast skill between Nowcasting/Extrapolation vs Numerical Weather Prediction

Forecast Length (Hours)

Fore

cast

Ski

ll

2013-2014 HRRR 3-km Radar Data Assimilation

2005-2008 Pre-Radar

Data Assimilation

2009-2012 RUC 13-km Radar Data Assimilation

-- Extrapolation -- Persistence

ß L

ess

Skill

Mor

e Sk

ill à

Improving forecast skill and halving crossover

period every ~3-4 years

RAP/HRRR:ImprovingForecastSkill

HRRR-AKModelConfigura8on

•  3-kmresolu8on•  1300x920x51gridpoints•  ini8alizedevery3h•  36-hforecast

•  WRF-ARWv3.8.1•  Noconvec8veparametriza8on•  20-s8mestep•  land-surface(butnot“full”)cycling

00zHRRR-AKHour-1

21zRAP242grid

boundarycondi8ons3-km

interpola8on

Land-surfacefieldsfromrecentHRRR-AKforecast

00zHRRR-AKHour0

00zHRRR-AKHour36

23zRAP242grid

0-hforecast

MRMSAlaskaradarmosaic

GSIhydrometeoranalysis

GSI3DHybrid

conven8onalobserva8ons

conven8onalandsatellite

observa8ons

Pre-Forecast FullForecast

09zGEFSEnKF

HRRR-AKIni8aliza8on

HRRR-AKApplica8ons:AlaskaRangeAvia8on

HRRR-AKApplica8ons:SEMarineInterests

HRRR-AKApplica8ons:InteriorConvec8on

HRRR-AKChallenges

•  modelconfigura8onispronetoinstabilityinsteepterrain

•  areas>24degslopeareselec8velysmoothedtopreventcrashes

•  AlaskaandStEliasRangesespeciallyproblema8c

HRRR-AKChallenges

•  persistentWRFissuewithsimula8ng,maintainingsharptemperatureinversions•  ongoingworkwithincreasingnumberofver8callevels

HRRR-AKChallenges

•  HRRR-AKshowsprovenskillwithphenomenonofdownslopewindstorms•  butdetailsremainhighlyuncertain

HRRR-AKInternalVerifica8on

•  on-demandplotsevaluateHRRR-AKvsMETARandRAOBobserva8ons

•  comparisonwithNAM-Alaska,13-kmRAP,etc.

•  supportsna8onalmoveto“evidencebased”decisionmaking

HRRR-AKatNCEP:PreliminarySchedule

AccessingHRRR-AKForecasts

h*ps://rapidrefresh.noaa.gov/alaska/

•  FTPaccess(contacttrevor.alcoh@noaa.gov)•  LDM(NWSAlaska)•  FullarchivesinceApr2016ontapestorage,smallrequestsonly•  NCEPsourcebyspring2018

HRRR-AK:CoupledModelingStueferetal.(2012)

HRRR-AK-Smoke

•  real-8mesmokeforecastsduringfireseason

•  feedbackonradia8onandmicrophysicsnowenabled

Na8onalWaterModel

•  real-8me,griddedstreamflowforecastsdrivenbyHRRR-AKprecipita8on

HRRR-AK-Ash

•  real-8mevolcanicashforecaststriggeredbyerup8ons

•  poten8alforfeedback•  collabora8onwithMar8n

SteuferatUAF

ImprovingHRRR-AKforecastswithpolarsatellitedataassimila8on

JiangZhu

GeographicInforma8onNetworkofAlaska(GINA),UAF

Benefitofdataassimila8on

•  Beforeamodelruns,itneedsgoodes8ma8onoftheini8alstate.Theini8alstateises8matedbybackgroundandvariedobserva8ons.Dataassimila8oncombinesobserva8onandbackgroundinforma8ontomodifytheini8alstateandtherebyimprovestheini8alstateofthemodel.

Observa8ons

•  Conven8onalobserva8ons(METAR,RAOB,etc.)•  Satelliteobserva8ons(soundingprofiles,windprofiles)

•  Aircramobserva8ons(AMDAR,etc.)•  Radarobserva8ons(NEXRD,etc.)

Sumi-NPPCriS/ATMSatmosphericprofile(NUCAPS)improvestheWRFmodelshort-termforecast

a)Upperobserva8onsinAlaska b)CrIS/ATMShumidityobserva8onsat850mbar

Figure1.ComparisonofRAOBandsatellitesoundingobserva8ons

a)showsthatthereareonly12conven8onalobserva8onsinAlaska.b)showsthatCrIS/ATMSsoundingdatahavemuchbehercoverageinAlaska(Zhu,2014).Figure1tellsusthattheconven8onalobserva8oninAlaskaistoocoarseandthesatellitesoundingdatamakeuptheweakness.

•  Root-mean-squareerror(RMSE)measuresthedifferencesbetweenforecastandobserva8ondata.RMSEiscomposedofmeanbias(RMSEa)andcenteredpahernRMSdifference(RMSEb),andRMSE^2=RMSEa^2+RMSEb^2(Taylor,2001).RMSEameasurestheoverallbiasandRMSEbmeasuresthevaria8onbetweentheforecastsandtheobserva8ons.

•  Modelrunsevery6hours.TheFigure2showsthesta8s8csofmonthly(e.g.120)analyses(Zhu,2016).Temperature,dewpoint,andwindspeedat300,500,and850mbarpressurelevelsarecalculatedintermsofRMSE.

•  AIRSandNUCAPSdataassimila8onrunsimprovetheanalysesinallthreepressurelevelsandallthreevariables.

Figure2.Performanceofassimila8onofAIRSandNUCAPSsoundingdata

PolarSatelliteWindproduct

hhps://stratus.ssec.wisc.edu/projects/polarwinds/

AVHRRpolarwinds(NOAA)MODISpolarwinds(TERRA,AQUA)VIIRSpolarwinds(NPP)

ReferencesJiangZhu,E.Stevens,B.T.Zavodsky,X.Zhang,T.Heinrichs,andD.Broderson.2014.SatelliteSounderDataAssimila8onforImprovingRegionalNWPForecastsinAlaska,poster,94thAmericanMeteorologicalSocietyAnnualMee8ng,Atlanta,USA.JiangZhu,E.Stevens,T.Heinrichs,J.Cherry,andC.Dierking.2016.AIRS/CrISsoundingprofiledataimprovestheshort-termweatherforecastofAlaska,poster,96thAmericanMeteorologicalSocietyAnnualMee8ng,NewOrleans,USA