Active and passive microwave observations of snowfall from

Activeandpassivemicrowaveobservationsofsnowfallfromspace.

DanieleCasella1,GiuliaPanegrossi1,PaoloSanò1,AnnaCinziaMarra1,StefanoDietrich1,1 Istitute of Atmospheric Sciences andClimate - CNR,Rome,Italy

BenjaminT.Johnson2,2 AtmosphericandEnvironmentalResearch@NOAAJointCenterforSatelliteDataAssimilation(JCSDA),CollegePark,

MD,USA

MarkS.Kulie3.

3 SpaceScienceandEngineeringCenter,UniversityofWisconsin-Madison,Madison,Wisconsin,USA

Motivation• OneofthemaingoalsoftheGPMmissionistoimprove

snowfallretrievalaccuracy

Objectives:• Dual Precipitaion Radar

– AssesstheGPMDPR-basedprecipitationproductsabilitytoestimate andidentify snowfall

– AnalyzetheDPRKu/Karadarsensitivitytosnowfall– ProposeasimplealgorithmtoincreasetheDPR’ssnowfalldetection

capabilityusingmatchedDPRKu/Karadarreflectivityvalues

• MWradiometers– Surface backgroundcharacterization– Analysis of snowfall related signal

CloudSat CPRis used as areference inthis study

8thIPWG5thIWSSMJointWorkshop,Bologna,3-7October 2016

CPRvsDPRGPM DPR GPM DPR CloudSat CPR

Ku Ka

Frequency 13.6GHz 35.55GHz 94.05 GHz

Orbit Drifting 65° Sunsynchronous13:30asc.

Altitude 407km 705km

Sensitivity 12-13dBZ 16.32dBZ -28dBZ

Scanning electronic None

Swath size 245km 125km 1.5km

HorizontalResolution 5km 1.5km

VerticalResolution 250m 250mMS

500mHS 480m

VerticalSpacing 125 m 125mMS

250mHS 240m

DPRKu

DPRKa

CPR


We collected 74750coincidentDPR-CPRsnowfall observationsfrom 2B-CSATGPMproductdataset was enriched with

various CludSat-GPM productsperiodMarch2014toMay2015Selectedcoincidenceswithin:

5minutesand2.5km

Casestudy:extensive frontal snowfallWidespreadfrontalsnowfalleventoccurredoverEasternRussianorthoftheSeaofOkhotskon30April2014

CPR:• TypicalmaximumCPRZ:10-15dBZ• maximumcloudtopheightsbetween~5-8km• shallowercloudstructureswithcloudtopheightslessthan~2km

DPRMeasuredReflectivity(Z)• KuandKa-HSuncorrectedZsomestructurebelow~4kminthedeepersnowfallsegment

• mostoftheeventnorthof60o latitudeandathigheraltitudesismissed

• significantrandomnoisearound12dBZ (KuandKaHS)or18dBZ (KaMS)

• SidelobecluttersignalintheKu

DPRCorrectedReflectivity(Zc)(2A-DPR)• Completesuppressionofrandomnoiseandsidelobe clutter

• Attenuationofcorrectionbelowthefree-clutterlevel

• Partoftheweaksignalrelatedtosnowfallisalsoeliminated


MorecasestudiesShallowconvectivesnowfall

OregonCascademountainRangeIntenseFrontalSnowfall

InteriorAlaska


2A-DPR:Ku andKa Reflectivity values2B-DPRCMB:DPRandGMIcombined

Assesment of snowfall rates inDPRL2products2B-DPR-

CMB Ku

2B-DPR-CMB

Ka MS

2A-DPRKu

2A-DPRKaMS

2A-DPRKa HS

ME*[mm h-1] -0.338 -0.372 -0.1136 -0.167 -0.450

RMSE*[mm h-1] 0.731 0.820 0.674 0.734 0.637

ARMSE [mm h-1] 0.648 0.731 0.665 0.714 0.451

POD 0.0675 0.0757 0.0605 0.0676 0.0486

FAR 0.507 0.542 0.538 0.580 0.636

6.3%

87.5%

6.2% 7.1%

86.9%

6.0% 5.8%

89.3%

4.9% 6.4%

88.9%

4.7% 4.7%

92.6%

2.7%

HIT MISS FALSE

Ku Ka

2A-DPR

KuMSHS

DPR-

CPRfreeClutter

binhe

ight

diffe

rence

Ku Rayindex

DPRProducts Snowfall DetectionCapabilities

2B-DPR-CMB

Ku

2B-DPR-CMB

Ka MS

2A-DPRKu

2A-DPRKa MS

2A-DPRKa HS

Percentage of snowfall

mass detected28.43% 34.09% 27.74% 32.08% 30.33%

DPR-CPRLowLevel Reflectivity Comparison

Mean Reflectivity inthefirst500mover DPRclutter-free level

Proposed algorithm for DPRsnowfall detectionincrease

1. Assuming Ka and Ku coherence -> -0.5 < DFR < 5

2. Assuming cloud structure continuity -> Z > 8 dBZ for 3 or more of the 4 Ku reflectivity

3. Assuming a minimum ->Z dBZ10>Z

DRF: dual frequency ratio (DFR = dBZKu-dBZKa):

DPR Algorithmsversion 4

2B-DPR-CMB

Ku

2A-DPRKu

2B-DPR-CMB

Ka MS

2A-DPRKa MS

2A-DPRKa HS

% snowfall mass

detected

28.43

%

27.74

%

34.09

%

32.08

%

30.33

%Proposed

algorthmKu Ka

% snowfall mass

detected57.16% 52.86%

Reddots:detected by thenew algorithm

Crosses :detected by2A-DPR

Section 2:MWradiometers

D.Casella,G.Panegrossi,P.Sanò,A.C.Marra,S.Dietrich,B.T.Johnson,M.S.Kulie,EvaluationoftheGPM-DPRSnowfallDetectionCapability:ComparisonwithCloudSat-CPR,JGR- Atmosphere,submitted

ATMSradiometer

• Somepreliminary results from theanalisysof ATMSradiometr compared to CPRwillbe shown

• ATMSobserve highlatitudes with 5channels inthe183GHz WVabsorptionband

• General approach for Passivemicrowave– Lowfreq.channels used for surface

classification– Highfreq.for snowfall-related signal

• Dataset of coincident CPR-ATMSobservations size:2.300.000ATMSpixels

Centralfrequency(GHz)

Bandwidth(MHz)

Quasi-polarisation NEΔT

23.8 270 QV 0.90K31.4 180 QV 0.90K50.3 180 QH 1.20K51.76 400 QH 0.75K52.8 400 QH 0.75K

53.596± 0.115 170 QH 0.75K54.4 400 QH 0.75K54.94 400 QH 0.75K55.5 330 QH 0.75Kf0=

57.290344 330 QH 0.75Kf0± 0.217 78 QH 1.20K

f0± 0.3222 ±0.048 36 QH 1.20K

f0± 0.3222 ±0.022 16 QH 1.50K

f0± 0.3222 ±0.010 8 QH 2.40K

f0± 0.3222 ±0.0045 3 QH 3.60K89.5 5000 QV 0.50K165.5 3000 QH 0.60K

183.31± 7.0 2000 QH 0.80K183.31± 4.5 2000 QH 0.80K183.31± 3.0 1000 QH 0.80K183.31± 1.8 1000 QH 0.80K183.31± 1.0 500 QH 0.90K

Surface Classification

Casestudy Ice Class Test

ATMSclassifierOceanisBlue,newiceiscyanandyellowisfirstyear/multilayerice(inbothpanels).

OSISAF(productOSI-403-a)

MODISObservationovertheoceanbetweenGreenlandandSvalbardIslandson01/05/2015at10:05UTC,reflectancefromchannel2(Visible841-876nm)isshown.AlargestripeofbrokeniceisclearlyvisibleinthissectionofoceanfromMODIS.

ATMSWVchannelsNoCloud

Columnar WaterVapor[kgm-2]

183.31GHz

183GHz ΔTbCloud Free



183GHz ΔTbSurface Snowfall >0

ConclusionsDPR

• DPRprecipitationproductsmissaverylargefractionofsnowfallprecipitationevents (92.5- 95.2%ofmisses)

• fractionofsnowfallmasscorrectlydetectedbyDPRisnotnegligible(27-34%)• ThemostimportantissuesoftheDPRinsensingsnowfallproducingcloudsare

relatedtothelimitedsensitivityandtothepresence(intheKuprofiles)ofsidelobeclutterechoes.

• thecluttermitigationtechniqueseliminatealarge fractionoftheweaksignalrelatedtosnowfall

• AsimpleprototypealgorithmhasbeendevelopedandtestedforimprovingDPRsnowfalldetectioncapability:thepercentageoftheestimatedsnowfallmassdetectedincreasessignificantlyto57.16%(52.86%)fortheKu(Ka)radar

ATMS• Only two lowfrequencychannels(23.8GHz31.4GHz),togetherwithsome

ancillarydata,canbesuccessfullyusedtoclassifythebackgroundsurfaceeveninpresenceofprecipitatingclouds.

• The183GHz channeldifferencesaresensibletodeep/intensesnowclouds.• The characterizationofthebackgroundsurfaceisessential,oversomesurface

class(SnowA-B,Coast)cloudsnowdetectionisreallydifficult,overice-freeoceanandsnow-freelandrelativelyeasier

• Columnarwater vaporsignalisstrongerthancloud-snowsignal

Thank you

Thanks to Joe Turk, Norm Wood, Guosheng Liu and Grant Petty for the valuable discussions and suggestions.

This study has been carried out within:

EUMETSAT H-SAF/UW-SSEC/UMBC-JTEC Federated Activity “Cooperation on the use ofcombined spaceborne active and passive MW observations for precipitation retrieval”

Programma Nazionale di Ricerche in Antartide (PNRA) through the project “Characterization ofprecipitation in the Antarctic region based on satellite observations”

[email protected]

DPRKu Sidelobe clutter

Furukawa et al.2014

Kubota et al.2014


Kubota, et al. (2014), Evaluation of precipitation estimates by at-launch codes of GPM/DPR algorithms using synthetic data from TRMM/PR Observations, IEEE J. Sel.Top. Appl. Earth Obs. Remote Sens., 7(9), 3931–3944

Furukawa,Kinji,et al."Theorbital checkoutstatusof thedual-frequency precipitation radarontheglobalprecipitation measurement core spacecraft."2014IEEEGeoscienceandRemoteSensing Symposium.IEEE,2014.

Detectionof Ice over Ocean

Partitioningoftheoveroceanobservationsintoocean-iceandwarm-ocean.Reddotsrepresenticeoveroceanandno-iceoveroceanasrepresentedintotheSurface_type variableintothe2C-PRECIP-COLUMNCloudsat product.Theblackcurverepresentsthediscriminant functionappliedbythealgorithm.ThiscategorizationshowsaPODof94%(93%)andaFARof0.3%(1%)consideringthecloudfree(precipitation)dataset.

Detectionof Snow over Land

Snowoverlandidentification.ThecolorscalerepresentsthesnowdepthliquidwaterequivalentascalculatedbytheECMWFERAInterimreanalysis.Theblackcurverepresentsthediscriminant functionappliedbythealgorithm.ThiscategorizationshowsaPODof97%(93%)andaFARof7%(10%)consideringthecloudfree(precipitation)datasetandathresholdforsnowcoveredsurfaceof0.1mofLWEsnowdepth.

Ice over ocean classificationexample of classification for Ice over ocean. The 2 dimensional spaceof the 23 GHz channel divided by the 2 meter temperature (em 23GHz in the x axis) and 31 GHz channel divided by the 2 metertemperature (em 31 GHz in the y axis) is divided into several classeswith a k means clustering approach (the circles represents thecluster centroids) then the various clusters are grouped into 3 mainclasses: blue for New ice characterized by high emissivity almostconstant with the frequency. Red for multilayer Ice, with highlyvariable emissivity stronger at 23 GHz than at 31 GHz; and lightgreen for broken ice with a variable emissivity constant with thefrequency. Figure 5 show the results of the airborne retrieval ofemissivity fron Hewison and English 1999 in supporting theclassification operated from the ATMS surface classifier.

NadirEmissivityasreportedfromtheAirborneretrival ofHewison andEnglish1999

Statistics for theIce ClassesNEWICE MULTILAYERICE BrokenICE

NEWICE MULTILAYERICE BrokenICE

Peaksbeforewinterseason

Peaksinsummer

Noclearpeaks

NEWICE

MULTILAYERICE

BrokenICE

Snow over Land ClassificationTbrat =Tb23 /Tb31

ECMWFERA-ISnow Depth

Cloud Free With Clouds

Tbrat >1.1

Tbrat <1.1

NoSnow NoSnow

NoSnow NoSnow

Statistics for theSnow Classes

SnowAandBonlyoverAntarcticaandcontinentalGreenland,TypeBinparticularispresentonlyovertheAntarcticaPlateu (andoverinnerGreenland)duringwinter.

Snow A Snow B

Snow C

Globaltrends

Summary of DPR-CPRComparison• DPRprecipitationproductsmissaverylargefractionofsnowfall

precipitationevents (92.5- 95.2%ofmisses)• fractionofsnowfallmasscorrectlydetectedbyDPRisnotnegligible(27-

34%)• snowfallratesestimatedbytheDPRlevel2productssufferfrom

underestimation• allfalsealarmsinourdatasetareduetodifferencesintheestimated

phaseofprecipitation,thatisrelatedprimarilytothedifferentnearsurfacebinconsideredintheCPRandDPRproducts

• ThemostimportantissuesoftheDPRinsensingsnowfallproducingcloudsarerelatedtothelimitedsensitivityandtothepresence(intheKuprofiles)ofsidelobeclutterechoes.

• thecluttermitigationtechniqueseliminatealargefractionoftheweaksignalrelatedtosnowfall

• AsimpleprototypealgorithmhasbeendevelopedandtestedforimprovingDPRsnowfalldetectioncapability:thepercentageoftheestimatedsnowfallmassdetectedincreasesdramaticallyto57.16%(52.86%)fortheKu(Ka)radar

Documents

Active and passive microwave observations of snowfall from