Results from GPM GPROF V4 and Improvements Planned for...
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GPROF V4 Version 4 of the GPM radiometer algorithms were updated and delivered in April 2016. Whereas Version 3 of the algorithm used a-priori databases constructed from a number of proxies such as TRMM, CloudSat, ground based radars and models, Version 4 constitutes the first version constructed from the GPM core satellite itself. Conclusions Changes in GPROF V5 correct implementation errors in the Tb bias correction scheme, as well as dealing with precipitation known to be below the threshold of the GPM radars. Over the remaining regimes, GPROF V5 will continue to rely on V4 of the GPM core satellite products for its a-priori database. Three relatively simple but important changes highlighted above are being implemented for Version 5. More difficult changes related to convective structures over land; Convective/Stratiform separation and improved snowfall retrievals are being deferred to future versions. • . GPROF V4 Validation Over land, a database using the DPR-Ku radar for the a-priori database showed significantly better agreement with surface observations. Based on these early result the Combined Radar/Radiometer product is used as the basis for a-priori rainfall products over oceans, while DPR-Ku is used over land. Early Implementation The first internal version of GPROF used the Combined DPR/GMI algorithm (CMB) as a basis for the a–priori database everywhere. The profiles from the combined algorithm were modified slightly to lead to better agreement with the GMI high frequency channels which were not part of the combined algorithm optimization process. Results from GMI can be seen to follow the Combined algorithm quite well but differences were notices against validation from gauges and MRMS GPCC Gauges MRMS GPROF/CMB GPROF/DPR-Ku -90 -60 -30 0 30 60 90 0 2 4 6 8 10 La#tude mm/day ANNUAL TOTAL PRECIPITATION MERRATotal Precip Sep14-Aug15 ERATotal Precip Sep14-Aug15 Global Land GMI – Blue CMB MS – Green Ku – Red DPR MS - Orange GMI – Blue CMB MS – Green Ku – Red DPR MS - Orange Global GMI – Blue CMB MS – Green Ku – Red DPR MS - Orange Land GMI – Blue CMB MS – Green Ku – Red DPR MS - Orange GPROF GMI V4 follows the Combined algorithm (CMB MS) over oceans and DPR-Ku (Ku) over land quite well . Compared to the pre-launch algorithm (Version 3), Version 4 shows significantly better agreement with GPCC rain gauge accumulations. While V3 was significantly low biased against gauges, Version 4 tends to be within 10-20%. Correlations against instantaneous rain rates from MRMS show artifacts with the Version 4 product that were not there previously. The artifact is due to an error in a bias- adjustment intended to eliminate the bias between computed and observed Tb in the a-priori database. Comparisons against MRMS for temperatures less than 270K (assumed to be snow) also reveal substantial underestimation by GPROF V4. This can be traced back to missing snow in the Ku radar product which is not sensitive to light snow. An underestimation is also seen at high latitude oceans when compared to GPCP or MERRA. The underestimation is again due to the radar’s inability to detect light drizzle known to occur in these locations GPROF V5 Preparation The artifacts in the correlation plot have been resolved by fixing the error in the code. For land pixel, bias against MRMS is +6.8% with correlation of 0.58. Snow is included in this plot. The missing snow not seen by Ku radar has been addressed by using ground based radar (MRMS) over the United States together with coincident satellite overpasses for 2 years to construct a new empirical database as a function of temperature and humidity. It is applied only to snow covered surfaces. Biases are reduced although overall skill of GPROF is still limited. Over oceans, CloudSat probabilities of precipitation have been used to determine a cloud water threshold in a non-raining GMI only retrieval. Precipitation will be added to these pixels to minimize differences between observed and simulated Tb. Rainfall is constrained to be less than that observed by the GPM. This step has not been implemented. Results from GPM GPROF V4 and Improvements Planned for V5 Christian Kummerow, David Randel and Veljko Petkovic Colorado State University, Fort Collins, USA e-mail: [email protected] GPROF V5 with MRMS Snow database Temp < 270K MRMS Precipitation Temp < 270K GPROF 2014 Unified Algorithm Structure PreProcessor (sensor specific) Standard input file Spacecraft position Pixel Location, Tbs Pixel Time, EIA Chan Freqs & Errors GPROF Precipitation Algorithm L1C Sensor Data Surface & Emissivity Classes ECMWF / GANAL T2m, TPW Autosnow Snow Cover Reynolds Sea-Ice Ancillary Info / Datasets Sensor Profile Database Complete HDF5 Output file Post-processor (Binary to HDF5) A-Priori Matched Profiles - GMI/ DPR JMA forecast - NRT GANAL - Standard ECMWF - Climatology Model Preparation Denotes Processes running at the SIPS GPROF V4; Temp < 270K GPROF V5; Temp < 270K
Transcript of Results from GPM GPROF V4 and Improvements Planned for...
GPROFV4Version 4 of the GPM radiometer algorithms were updated and deliveredin April 2016. Whereas Version 3 of the algorithm used a-prioridatabases constructed from a number of proxies such as TRMM,CloudSat, ground based radars and models, Version 4 constitutes the firstversion constructed from the GPM core satellite itself.
ConclusionsChanges in GPROF V5 correct implementation errors in the Tb bias correctionscheme, as well as dealing with precipitation known to be below the thresholdof the GPM radars. Over the remaining regimes, GPROF V5 will continue to relyon V4 of the GPM core satellite products for its a-priori database. Threerelatively simple but important changes highlighted above are beingimplemented for Version 5. More difficult changes related to convectivestructures over land; Convective/Stratiform separation and improved snowfallretrievals are being deferred to future versions.• .
GPROFV4Validation
Over land, a database using the DPR-Ku radar for the a-priori databaseshowed significantly better agreement with surface observations. Based onthese early result the Combined Radar/Radiometer product is used as thebasis for a-priori rainfall products over oceans, while DPR-Ku is used overland.
EarlyImplementationThefirstinternalversionofGPROFusedtheCombinedDPR/GMIalgorithm(CMB)asabasisforthea–prioridatabaseeverywhere.TheprofilesfromthecombinedalgorithmweremodifiedslightlytoleadtobetteragreementwiththeGMIhighfrequencychannelswhichwerenotpartofthecombinedalgorithmoptimizationprocess.ResultsfromGMIcanbeseentofollowtheCombinedalgorithmquitewellbutdifferenceswerenoticesagainstvalidationfromgaugesandMRMS
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ANNUALTOTALPRECIPITATIONMERRATotalPrecipSep14-Aug15
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Global LandGMI – BlueCMB MS – GreenKu – RedDPR MS - Orange
GMI – BlueCMB MS – GreenKu – RedDPR MS - Orange
GlobalGMI – BlueCMB MS – GreenKu – RedDPR MS - Orange
LandGMI – BlueCMB MS – GreenKu – RedDPR MS - Orange
GPROFGMIV4followstheCombinedalgorithm(CMBMS)overoceansandDPR-Ku(Ku)overlandquitewell.
Comparedtothepre-launchalgorithm(Version3),Version4showssignificantlybetteragreementwithGPCCraingaugeaccumulations.WhileV3wassignificantlylowbiasedagainstgauges,Version4tendstobewithin10-20%.
CorrelationsagainstinstantaneousrainratesfromMRMSshowartifactswiththeVersion4productthatwerenottherepreviously.Theartifactisduetoanerrorinabias-adjustmentintendedtoeliminatethebiasbetweencomputedandobservedTbinthea-prioridatabase.
ComparisonsagainstMRMSfortemperatureslessthan270K(assumedtobesnow)alsorevealsubstantialunderestimationbyGPROFV4.ThiscanbetracedbacktomissingsnowintheKuradarproductwhichisnotsensitivetolightsnow.
AnunderestimationisalsoseenathighlatitudeoceanswhencomparedtoGPCPorMERRA.Theunderestimationisagainduetotheradar’sinabilitytodetectlightdrizzleknowntooccurintheselocations
GPROFV5Preparation
Theartifactsinthecorrelationplothavebeenresolvedbyfixingtheerrorinthecode.Forlandpixel,biasagainstMRMSis+6.8%withcorrelationof0.58.Snowisincludedinthisplot.
ThemissingsnownotseenbyKuradarhasbeenaddressedbyusinggroundbasedradar(MRMS)overtheUnitedStatestogetherwithcoincidentsatelliteoverpassesfor2yearstoconstructanewempiricaldatabaseasafunctionoftemperatureandhumidity.Itisappliedonlytosnowcoveredsurfaces.BiasesarereducedalthoughoverallskillofGPROFisstilllimited.
Overoceans,CloudSatprobabilitiesofprecipitationhavebeenusedtodetermineacloudwaterthresholdinanon-rainingGMIonlyretrieval.PrecipitationwillbeaddedtothesepixelstominimizedifferencesbetweenobservedandsimulatedTb.RainfallisconstrainedtobelessthanthatobservedbytheGPM.Thisstephasnotbeenimplemented.
ResultsfromGPMGPROFV4andImprovementsPlannedforV5ChristianKummerow,DavidRandel andVeljko PetkovicColoradoStateUniversity,FortCollins,USAe-mail: [email protected]
GPROFV5withMRMSSnowdatabaseTemp<270K
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GPROF2014UnifiedAlgorithmStructure
PreProcessor(sensorspecific)
Standardinputfile
SpacecraftpositionPixelLocation,TbsPixelTime,EIAChanFreqs &Errors
GPROFPrecipitationAlgorithm
L1CSensorDataSurface&EmissivityClassesECMWF/GANALT2m,TPWAutosnowSnowCoverReynoldsSea-Ice
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SensorProfileDatabase
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DenotesProcessesrunningattheSIPS
GPROFV4;Temp<270K GPROFV5;Temp<270K
