ImprovingQuantitativePrecipitationNowcasting withaLocalEnsembleTransformKalman FilterRadarDataAssimilationSystem:TyphoonMorakot (2009)

Chih‐ChienTsai1,Shu‐ChihYang2,3 andYu‐ChiengLiou21TaiwanTyphoonFloodandResearchInstitute,NationalAppliedResearchLaboratories,Taipei,Taiwan;

2DepartmentofAtmosphericSciences,NationalCentralUniversity,Taoyuan,Taiwan;3RIKENAdvancedInstituteforComputationalScience,Kobe,Japan

16Sep2014,Taipei,Taiwan

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1. Introduction

2. Experimentaldesign

3. Results

1) Benefitofradialvelocityassimilation

2) Limitationofreflectivityassimilation

3) Impactofmixedlocalization

4. Summaryandongoingwork

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TyphoonMorakot (2009)

3High‐resolutionQPNiscrucialtoearlywarningagainstrainfallhazards

before after

Maximum48‐hourrainfall:2361mm (recordofTaiwan) Hsiaolin landslidecausednearly500fatalities

Hsiaolin landslide Collapsed hotel

Quantitativeprecipitationnowcasting (QPN)

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1. Veryshort‐term:Forecastlength<6hours

2. Highresolution:Gridspacing<5kmRadardataarerequired

3. Method1:radarechoextrapolation Useextrapolationmodels(celltracking,variational echotracking) Usereflectivity(Zh)information QPNskillrapidlydissipatesafter1‐2hours

4. Method2:radardataassimilation UseregionalNWPmodels Useradialvelocity(Vr) andreflectivity(Zh)information Newcellsarepredictableviamodeldynamicsandmicrophysics QPNskillcanbelengthenedintime

LocalensembletransformKalman filter(LETKF)

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1. AdeterministicEnKF (Bishopetal.2001;Ott etal.2004;Huntetal.2007)

2. Formulation: Ensemblemean:

Ensembleperturbations:

where

3. Advantages: Flow‐dependentbackgrounderrorcovariancematrix Immunityfrom4DVar’stangentlinearandadjoint models Handleatmosphericnonlinearitieswithanonlinearmodelandoperator Easyparallelcomputing

4. Covariancelocalizationtechniques: Variablelocalization(Kangetal.2011) Mixedlocalization(Tsaietal.2014)Multiplescalesoftyphoons

1 /

1 ⁄

Goalofthisstudy

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WRF RadarVr&ZhLETKF

NWPmodelAssimilationschemeObservation

ImprovingQPNforTyphoonMorakot(multiplescales,strongterraineffect)

Yangetal.(2012,2013) Tsaietal.(2014)

Case:theheaviestrainfallperiod(12Z8–00Z9Aug)

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1-km horizontal wind (vectors) and qv(color) at 18Z 8 from NCEP FNL data

6-hour overland rainfall since 18Z 8 from CWB observations

Reachaminimummovingspeedof2.6m/s Convergencebetweenmoistsouthwestmonsoonandtyphooncirculation Strongterraineffectthatenhancesrainfall

Modelsetup

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RCCGradar

CWB besttrack

WRF‐ARWV3.2.1Domains: Triple‐nested Two‐wayinteractive 28verticaletalevelsPhysics: Microphysics:PurdueLin Cumulus:Kain‐Fritsch LSM:Noah PBL:YSUPrognosticvariables: , , , , , , , ,

, , ,

0900 08180812

Assimilationexperiments

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Assimilatethesuperobservations ofVr andZh fromS‐bandRCCGradar Givenobservationerrors:Vr→3m/s,Zh→5dBZ 2‐hourassimilationperiodwitha15‐mincycleinterval

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Distributionoftheassimilatedobservations

Observationsdecreasewithheight Withawarm‐rainoperator,Zh observationsabove

5km(~meltinglayer)arenotassimilated

ZhVr

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Experiment AssimilatingVr(updatedvariables)AssimilatingZh

(updatedvariables)Horizontallocalizationradius

(updatedvariables)CTRL Yes(all) Yes(all) 12km(all)VR Yes(all) No 12km(all)VZqr Yes(all) Yes (onlyqr) 12km(all)V36 Yes(all) No 36km( , ),12km(others)NoDA NoradardataassimilationSingle Noradardataassimilation

Assimilationstrategies

VZqr appliesvariablelocalization toreflectivityassimilation V36appliesmixedlocalization toradialvelocityassimilation

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Correctionoftheimpingingwesterlyflow

A→SingleandNoDA inheritaweakerwesterlyflowfromNCEPFNLdataat12Z

A→VRlargelycorrectstheimpingingwindspeed

B→SpuriousconvectioncanoccurinSingle,whileNoDAsmooths outallinconsistentconvectionsintakingamean

Vr of RCCG 0.5° PPI at 18Z 8

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ConsequentQPNimprovement

VR provides a more accuraterainfall nowcast than Singleand NoDA, especially at areaC’ (downstream area of A)

3-hour overland rainfall since 18Z 8

Experiment 1‐hour 3‐hour 6‐hourSingle 0.369 0.606 0.636NoDA 0.509 0.644 0.629VR 0.569 0.655 0.654

Correlation coefficients of 1-, 3-and 6-hour overland rainfall since18Z 8 compared with OBS

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Limitationofreflectivityassimilation

Experiment 1‐hour 3‐hour 6‐hourNoDA 0.509 0.644 0.629CTRL 0.459 0.594 0.622VR 0.569 0.655 0.654VZqr 0.586 0.666 0.655

Correlation coefficients of 1-, 3-and 6-hour overland rainfall since18Z 8 compared with OBS

1. Needvariablelocalization: Onlyqr hasareliableerrorcovariancewithZh Implications:

Samplingerror inensemblespacedominatespoorerphysicalrelationsbetweentheothervariablesandZh

Highnonlinearity ofZh Modelerror andoperatorerror

2. ShortermemorythanVr assimilation: VRoutperformsNoDA for6hours VZqr outperformsVRforonly3hours

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Benefitofreflectivityassimilation

Zh of RCCG 0.5° PPI at 1730 UTC 8

3-hour overland rainfall since 18Z 8

Rainband structureisretrievedwhereZhobservationsareassimilated(<5km)

Maximumrainfallintensity(areaD’)isbetterforecasted

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Motivationofmixedlocalization

Background error correlation at 18Z 8from VR, between of the black pointand the 1-km fields of , , and

The spatial scale of backgrounderror correlation is larger betweenVr and the horizontal wind

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Furthercorrectionatsparsely‐observedareas

3-hour overland rainfall since 18Z 8

residual of RCCG 0.5° PPI at 18Z 8

H → The impinging windspeed is further corrected(increased) where radarechoes are sparse

C’ (downstream area of H)→ Rainfall nowcast is alsofurther corrected

Summaryandongoingwork

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1. Summary: Vr assimilation can correct the impinging westerly flow and

improve QPN for 6 hours in the case of Typhoon Morakot. Additional assimilation can be used to update only and

helps retrieve rainband structure. However, the model has ashorter memory for the adjustment of than that of winds.

Mixed localization considers the scale difference betweentyphoon circulation and embedded convections and leads tofurther QPN improvement at sparsely‐observed areas.

2. Ongoingwork: Totestdifferentmodelsetupsandreanalysisdata Tocoupletheassimilationofothersynopticobservations Toincreasetheradarnumber(coverage) Totestthe operatorthatconsidersice Totestdifferentweatherevents Tooptimizecomputationalefficiencyforoperationalevaluation

Thankyouverymuchforyourattention!

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Backupslides(realcase)

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Radarobservationoperator

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1. Spatialinterpolation: 8nearestgridpointsObservationpoint(inversedistanceweighting) Surfacecurvatureandatmosphericrefraction(4/3Earthradiusmodel) Terrainblockage

2. Variableconversion:(SunandCrook1997) Radialvelocity:

whereterminalvelocity Reflectivity:

3. Marshall‐PalmerDSDandwarm‐rainmicrophysics

⁄

5.40 ̅⁄ . .43.1 17.5 log

Preparingradardata

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1. RCCGVr andZh: Maximumunambiguousrange:230km Volumescanperiod:7.5min 9PPIsweepsfrom0.5° to19.5° Azimuthandrangegatespacings:1° &250m

2. Loweringspatialresolution: Purpose1:Savecomputationalcost Purpose2:Avoidtheerrordependencebetweenadjacentobservations Approach1:Datathinning Approach2:Superobbing

3. OSSEs:Directlysimulatelower‐resolutionobservations(5° &5km)

4. Realobservationexperiments:Superobservations (5° &5km)fromCWBQPESUMSauto‐QCdata (1° &1km)

5. Observationerrors:3ms‐1 (Vr),5dBZ (Zh)

Statistics:1. Innovation:

2. Meaninnovation:

3. Root‐mean‐squareinnovation:

4. Ensemblespread:

Ifforecastandobservationerrorsareunbiasedandmutuallyuncorrelated:

Idealensemblespread:

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Observation‐spacestatistics

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1

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Diagnosingthemodelbiasandensemblespread

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ForVZqr from1600to1800UTC:(a)Vr and(b)Zh

spread

meaninnovation

idealspread

rms innovation

Modelbias:Excessivewesterlywindandrain

Ensemblespread:Vr isunderdispersed

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NameSCC withCWBraingaugemeasurements

1‐h rain 2‐hrain 3‐h rain 4‐hrain 5‐hrain 6‐h rain

CTRL 0.459 0.422 0.594 0.644 0.632 0.622VR 0.569 0.644 0.655 0.685 0.673 0.654ZH 0.433 0.438 0.569 0.603 0.600 0.606VZqr 0.586 0.653 0.666 0.682 0.668 0.655V36 0.637 0.658 0.712 0.730 0.725 0.705

V36Zqr 0.666 0.637 0.693 0.703 0.691 0.6672kmVR 0.588 0.651 0.687 0.709 0.742 0.770NoDA 0.509 0.638 0.644 0.637 0.639 0.629Single 0.369 0.543 0.606 0.618 0.618 0.636

QPN

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TerrainheightforVRand2kmVR

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Backupslides(OSSE)

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Naturerun

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24‐hour40‐memberensembleforecastsstartingat0000UTC8Aug,withIC/BCgeneratedfromFNLdata andperturbedbyWRF‐3DVAR

Pickoutthememberthathasthemostrealistictrackand6‐hourrainfallcomparedwithCWBobservations

From1800UTC8to0000UTC9

DesignofOSSEs

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Toalleviatetheidentical‐twinproblem,CTRLandNoDA areinitializedlaterthanthenatureruntoobtainadifferentsynoptic‐scalecondition

AssumeFNLdatainherentlycontainsynoptic‐scaleinformationfromassimilatingconventionalandsatelliteobservations

Ensemblespin‐up

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Allvariablesexceptu representconvective‐scalebackgrounderrorstructureafterthe4‐hourspin‐up

NoDAmeanerror

NoDA spread

Exceptionofu

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u (color)andhorizontalwind(vectors)at1‐kmaltitudeat1200UTC

Initialensemble:Eastdeviationoftheeyeandtooweakwesterlywind

NoDA seriouslyunderestimatesrainfall(shownlater)

Analysiscycles

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Ensemblemeanerror(solid)Ensemblespread(dashed)

Directlyrelatedtotheobservationvariables:u,v,w,qr Robustlyrelatedtotheobservationvariables:qc Dynamicallyadjusted:θ’,qv

NoDAmeanerror

NoDA spread

CTRLmeanerror

CTRLspread

Analysisandnowcast ofspiralrainbands

32w at1‐kmaltitude(color)and

maximumqr atalllevels(contoursat1gkg‐1)

Spiralrainbandstructure oftheensemblemean:CTRL>NoDA

Analysisoftherainbands:Accuracy A>B

Nowcast oftherainbands:Hit:AccuracyA>B

Miss: C Falsealarm:D

StatisticalperformanceofQPN

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RMSE SCC ETS (15 mm) Bias (15 mm)

CTRL

NoDA

CTRLoutperformsNoDA ateachofthe6hours

Radardataassimilationcansubstantiallycorrectthemodelstatewithinthelimitedradarcoverage,butcannotaltertheevolutiontrenddrivenbysynoptic‐scaleconditions

Hourlyrainfallfrom1800UTC8to0000UTC9

SpatialperformanceofQPN

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Improvementofpeakrainfallintensity isapotentialbenefittoearlywarningsystems

Improvementiswidespreadalthoughthefalserainband Dleadstoasouthwarddeviationoftheheaviestrainfallarea

Rainfallaccumulationsince1800UTC

Sensitivitiestoindividualassimilationstrategies

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NameAssimilationstrategies

Assimilatedobservations

Assimilationperiod

Cycleinterval

Horizontalcovariancelocalizationradius

CTRL , 2hours 15min 12km(allvariables)VR * * *ZH * * *VZ0 , ,0‐dBZ * * *KM , (addKinmen) * * *P1 * 1hour * *P3 * 3hours * *I7.5 * * 7.5min *I30 * * 30min *UV24 * * * 24km( , ),12km(others)UV36 * * * 36km( , ),12km(others)NoDA Noradardataassimilation

Thesymbol*denotesthesamesettingasCTRL

Analysisperformance

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NameImprovementpercentagescomparedwithNoDA

CTRL 36 29 21 4 7 11 45VR 30 13 7 7 1 6 8ZH 11 19 12 ‐5 1 7 39VZ0 35 31 26 6 7 13 51KM 49 42 31 9 11 19 57P1 27 17 17 6 2 8 41P3 40 32 23 7 9 12 47I7.5 40 30 27 0 1 11 52I30 29 19 14 5 4 7 32UV24 42 31 23 4 6 10 46UV36 43 27 22 3 3 9 41

Red BetterthanCTRL

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NameImprovementpercentagescomparedwithNoDA

1‐h rain 2‐hrain 3‐h rain 4‐hrain 5‐hrain 6‐h rain

CTRL 39 38 39 35 31 32VR 5 14 17 19 19 19ZH 32 29 27 23 19 18VZ0 44 37 35 32 29 29KM 49 45 43 45 44 40P1 26 29 33 32 28 26P3 41 35 37 34 32 32I7.5 43 40 39 34 29 30I30 29 30 35 32 28 28UV24 42 39 38 33 29 31UV36 40 40 38 33 30 30

Red BetterthanCTRL

QPNperformance

Summary:OSSEs

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The3Dwindsandrainmixingratio arethemostimprovedprognosticvariablesbecauseoftheirdirectrelationstoradarobservations.

QPNimprovementisavailablefortheentire6hours.BothDAandNoDAhavesimilarevolutiontrendsdrivenbysynoptic‐scaleconditions.

Theimprovementof peakrainfallintensity isapotentialbenefittoearlywarningsystems.

QPNrespondstoZh assimilationmorequicklythanVr assimilation.Assimilatingboth andincreasingtheobservationcoverage overupstreamconvectionsaresuggested.

AmixedlocalizationmethodisproposedandfoundbeneficialforQPNinthismulti‐scalecase.