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Estimation of Tropical Cyclone Intensity Using Satellite ...CAT12 CAT12 CAT12 CAT35 CAT35 CAT35....
Transcript of Estimation of Tropical Cyclone Intensity Using Satellite ...CAT12 CAT12 CAT12 CAT35 CAT35 CAT35....
EstimationofTropicalCycloneIntensityUsingSatellitePassiveMicrowaveObservations:
Year2Update
73rdIHC/2019TCORFMarch12-14,2019
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HaiyanJiang, Yongxian “Isaac”Pei,andXinxi “Lincy”WangDepartmentofEarth&Environment
FloridaInternationalUniversity
Acknowledgements:1)NHCPointsofContact:JackBeven,ChrisLandsea,andDaveRoberts2)CHPCPointofContact:BobBallard3) ThisNOAAJointHurricaneTestbedprojectwasfundedbytheUSWeatherResearchProgram inNOAA/OAR'sOfficeofWeatherandAirQuality.
ProjectOverview
PassiveMicrowaveIntensityEstimation(PMW-IE)Model:
ØTask1:ModeldevelopmentØ Proof-of-Concept&initialmodeldevelopmentusingTRMMdata
Ø Initialmodeldevelopment&implementationusingcurrentavailablePMWsensors:GMI,AMSR2,andSSMIS
ØTask2:Realtimetesting&post-seasonevaluationsØ ALbasin:2018hurricaneseasonØ Post-seasonanalysis&modelrefinementØ ALandEPbasins:2019hurricaneseason
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MotivationoftheProject§ CurrentlyTCintensityisalmostexclusivelyestimatedbyDvoraktechnique (Dvorak1975,1984;ObjectiveVersionODT:Velden etal.1998;ADTOlander andVelden 2007)exceptwhensometimeaircraftrecondataareavailableintheALbasin.
§ TheDvoraktechniqueisbasedonbothvisibleandIRsatelliteimages,whichonlyshowthecloudtopstructureofaTCandcannotmeasurethedetailedrainfallandconvectivestructureatlowerlevels.
§ Theadvantageofpassivemicrowave(PMW)channelsisthattheyallowpenetrationintoprecipitatingclouds,thereforeprovidinginformationaboutprecipitationandconvectivestructurethatarebettercorrelatedwithTCintensity(CecilandZipser 1999).
§ Inrecentyears,inter-calibrationofdifferentPMWradiometershasbeendonebyNASA.TheerahasarrivedwheretimelyobservationsfromPMWsensorscanbeincorporatedintoreal-timeTCmonitoringandforecasting.
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Past, Current,&FuturePassiveMicrowaveSatelliteSensors
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Sensor 85-91GHzFrequency
SpatialResolutionat85-91GHz
SwathWidth Year
SSM/I(F15) 85.5GHz 15x13km2 1400km 1987-presentSSMIS(F16,17,18,19)
91GHz 14x13km2 1700 km 2003-present
AMSR-E 89GHz 6x4km2 1450km 2002-2011AMSR2 89GHz 5x3km2 1450km 2012-presentTMI 85.5GHz 7x5(beforeboost)
/8x6(afterboost)km2
760km (beforeboost)878km/(afterboost)
1997-2014
GMI 89GHz 7.2x4.4km 900km 2014-presentTROPICS 91 GHz 17x17km2 2025km 2020-??
InitialModelDevelopmentUsingTMIdata§ 16-yrTMIdata(1998-2013):2326overpassesover503TCs§ Developedastepwisemultiplelinearregressionmodelusing1998-2010(13-yr)casesasthedependentsample
§ Modelverification:2011-2013(3-yr)casesastheindependentsample§ DevelopALandEP/CPmodelsseparately;EstimatebothVmax &6-hfutureVmax.
§ ThisalgorithmwillbereferredtoasthePassiveMicrowaveIntensityEstimation(PMW-IE)model.
§ Aircraft-recon-basedindependentsamplesyieldaMAEof9.6kt andbesttrack-basedsamplesyieldaMAEof9kt.
§ Resultspublishedin:
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Jiang,H.,C.Tao,andY.Pei,2019:EstimationofTropicalCycloneIntensityintheNorthAtlanticandNorthEasternPacificBasinsUsingTRMMSatellitePassiveMicrowaveObservations.J.Appl.Meteor.Climatol.,58,185–197,https://doi.org/10.1175/JAMC-D-18-0094.1.
ComparisonofMAE&RMSEwithothermethods
TabletakenfromJiangetal.(2019)
VariablesSelectedforthePMW-IEModel
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Variables Description Units85GHz
1) MEANPCT Mean85GHzPCT K2)FRAC275 Fractionalareacoveredby85GHzPCT≤275K %3)FRAC250 Fractionalareacoveredby85GHzPCT≤250K %4)FRAC225 Fractionalareacoveredby85GHzPCT≤225K %5)FRAC200 Fractionalareacoveredby85GHzPCT≤200K %
Rain1) U_RR Unconditionalmeanrainrate mm/hr2)C_RR Conditionalmeanrainrate mm/hr3)L_RR Meanlightrain(rainratebetween0-5mm/hr)rate mm/hr4)H_RR Meanheavyrain(rainrate≥5mm/hr)rate mm/hr5)RA Fractionalareacoveredbyrain %6)L_RA Fractionalareacoveredbylightrain %7)H_RA Fractionalareacoveredbyheavyrain %
Tablebelow:ListofvariablesintheinnercorehavingcorrelationcoefficientswithVmaxsignificantatthe99.99%level.
2018Real-TimeTestingUsingGPM-constellation1C/2AData
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Ø UseTMI-trainedstepwisemodelforGMI,AMSR2andSSMISdataØ Inreal-time,the85-91GHzTbobservationswillbeavailablethroughtheGPM1C-constelationnear-real-timeproduct,whichincludestheinter-calibratedbrightnesstemperaturesfromGMI,ASMR2,andSSMIS.
Ø ThemicrowaverainretrievalswillbefromtheGPM2A-GPROF-constellationnear-real-timeproduct,whichcontainstherainratesretrievedfromGMI,AMSR2,andSSMISusingtheNASAGPROFalgorithm(Kummerow etal.1996).
Ø Latencies:Ø GMI1C/2A:about20to30minutesØ AMSR2&SSMIS1C/2A:about2to3hoursØ Therefore,inreal-time,therainvariablesmayormaynotbeavailableforestimatingVmaxatthecurrentsynoptictime.ButwecanstillestimatethecurrentVmaxusingtheregressionmodelforthe6-hfutureVmax.
AL(EP/CP)2018Real-TimeTesting
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Ø OnlineOutput:http://tcpf.fiu.edu/JHT/Figures/3 Folders: /AL2018/EP2018/CP2018
Ø HurricaneMicheal2018Example:http://tcpf.fiu.edu/JHT/Figures/AL2018/AL14/
10/10:Btrk Vmax=135kt at18Z;AMSR2@18:37ZEst.Vmax_85GHz=123kt;Est_Vmax_rain_85GHz=116kt
Btrk Vmax=70&75kt at10/0818Z&10/0900Z;GMI@21:40ZEst.Vmax_85GHz=84kt;Est_Vmax_rain_85GHz=73kt
Real-timePMWSatelliteCoveragefor2017-2018TCsinAL&EP/CP
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2017-2018 AL EP/CP Total6-hBestTrackDataPoints
1256 1451 2707
GMIcoverage 396(32%) 399(27%) 795(29%)AMSR2coverage
622(50%) 585(40%) 1207(45%)
TotalHigh-Res.GMI+AMSR2
822(65%) 795(55%) 1617(60%)
Low-Res.SSMIS
1108(88%) 1094(75%) 2202(81%)
OverallTotal 1184(94%) 1213(84%) 2397(89%)
2018Real-timeTestingStatisticalEvaluationAgainstBestTrack
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ErrorAnalysis
• Resultsareslightlybetterwhenusingregularmultiplelinear(non-stepwise)regression.• SubjectiveDvorakMeanAbsoluteError(MAE)is~8kt,RootMeanSquareError(RMSE)
is~10kt (Knaff etal.(2010).• TheAMSU-basedMAEis10.8kt andRMSEis14.0kt (Demuthetal.2006).• TheSSM/I-basedMAEis14-16kt,andRMSEis18.1-19.8kt (BankertandTag2002).• Jiangetal.(2019)PMW-IETMI-basedMAE9kt,RMSE9.6kt.
85-GHzonly Rainonly Combined Non-stepwiset=0h t=6h t=0h t=6h t=0h t=6h
AL (758samples)
r2 (%) 0.38 0.41 0.53 0.54 0.54 0.55 0.61MAE(kt) 13.96 13.50 11.59 11.34 11.19 10.79 10.43RMSE(kt) 18.91 18.77 16.46 16.40 15.83 15.73 14.56
EP/CP1024samples
r2 (%) 0.45 0.51 0.59 0.62 0.60 0.63 0.70MAE(kt) 19.71 18.33 16.82 16.58 15.94 15.76 13.56RMSE(kt) 25.27 24.34 22.49 22.06 21.42 21.15 18.15
2018Real-timeEvaluation(AL)
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2018Real-timeEvaluation(EP/CP)
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Post-SeasonModelRefinement
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AL EP/CP#oforbits(#ofTCs)
Dependent1998-2017
Independent2018
Dependent1998-2017
Independent2018
High-Res(TMI,GMI,AMSR2)
1980(300) 251(15) 2284(358) 345(23)
Low-Res(SSMIS)
1485(55) 507(15) 2458(95) 697(23)
§ Stepwiseversusnon-stepwiseregressions§ Separatehigh(GMI/AMSR2)andlow-resolution(SSMIS)sensors§ 4(ALhigh-res;ALlow-res;EP/CPhigh-res;EP/CPlow-res)X6(85GHz,rain,andcombinedforVmax att=0and6h)=24separatemodels
SampleSize
RefinedModelVerificationAgainstBestTrack
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ErrorAnalysisfor2018IndependentSample85-GHzonly Rainonly Combined No
stepwiset=0h t=6h t=0h t=6h t=0h t=6h
ALHighr2 (%) 0.40 0.44 0.63 0.66 0.64 0.67 0.68
MAE(kt) 13.27 12.61 9.94 9.30 9.87 9.24 9.09RMSE(kt) 17.46 16.96 13.37 12.77 13.23 12.67 12.37
ALLowr2 (%) 0.44 0.48 0.60 0.61 0.60 0.61 0.60
MAE(kt) 12.70 12.12 10.49 10.24 10.39 10.14 10.29RMSE(kt) 16.86 16.13 14.19 13.86 14.07 13.84 14.08
EP/CPHighr2 (%) 0.53 0.60 0.64 0.69 0.66 0.69 0.72
MAE(kt) 17.62 16.19 14.61 13.35 14.31 13.29 12.70RMSE(kt) 22.22 20.78 18.90 17.97 18.55 17.87 16.99
EP/CPlowr2 (%) 0.49 0.54 0.60 0.62 0.60 0.62 0.70
MAE(kt) 18.73 17.40 15.75 14.66 15.70 14.72 14.15RMSE(kt) 24.57 23.18 21.86 20.87 21.89 20.91 18.90
IndependentVerification(ALHigh-Res)
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SummaryØ ThePMW-IEmodel’sperformanceissimilartootherobjectivesatellite-basedTCintensityestimatemethods(allworsethanthesubjectiveDvorakTechnique).
Ø However,thePMW-IEestimatesareindependentofvisible,IR,andmicrowavesounderobservations.Becauseofthisindependence,thePMW-IEmethodwillbeabletoprovideadditionalinformationforTCforecasterswhocanutilizedifferentmethodstoachievemoreaccurateintensityestimates.
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Ø Year2:Ø Mar-Jun,2019:Implementtherefinedmodelforreal-timetestingforAL&EP/CPbasinsin2019season
Ø Year3(no-costextension):Ø Jun-Nov,2019:real-timetestingtobecontinuedØ Nov2019-Jun2020:post-seasonanalysis
Ø Year4(moreno-costextension??):Ø ApplythemethodtoJTWCbasins
Next-StepWork
FutureWork:EnhancementofSHIPSUsingPassiveMicrowaveRainfallFeatures
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Ø AddrainfallstructuralparametersØ Modeldevelopmentusing16-yrTMIsamples
Max.improvementis~7%at24-hfutureagainstSHIPS-Base.
Thanksforyourattention!
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IndependentVerification(ALLow-Res)
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IndependentVerification(EP/CPHigh-Res)
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IndependentVerification(EP/CPLow-Res)
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