TheDarkTargetaerosolretrievalalgorithm:FromMODIStoVIIRS(andbeyond)

RobertC.Levy(NASA-GSFC)andthe”Dark-Target”retrievalteam

AtGSFCBuilding#33:ShanaMattoo,VirginiaSawyer,RichKleidman (SSAI)Yingxi Shi(USRA),Yaping Zhou(MSU)LorraineRemer(UMBC)

NowatNASA– MarshallPawanGuptaandFalguni Patadia (USRA)

Outsideof#33:FolksatUniversityofAtmos-SIPS(Wisconsin)FolksatMODAPS(GSFC)

ForAerosolOpticalDepth(AOD):

Target metric Target

HorizontalResolution 5-10 km,globally

Accuracy MAX(0.03 or10%)

Stability /bias <0.01 /decade

TimeLength 30+years

TemporalResolution 4h

GCOS Aerosol CDR* Requirements

*CDR = Climate Data Record

CDR=ClimateDataRecord

GlobalClimateObservingSystem

Thesearerequirementsfor“climate”monitoringMaybedifferentrequirementsforotherapplications

(airquality,oceanfertilization,weatherforecasting...)

Dark-Target:A“SingleView”aerosolalgorithm

May4,2001;13:25UTCLevel1“reflectance”

Whatasensorobserves

OCEAN

GLINT

LAND

May4,2001;13:25UTCLevel2“product”

AOD1.0

0.0

Attributedtoaerosol(AOD)

“Established1997”byKaufman,Tanré,Remer,etc)forMODIS“Modified2005,2010,2013,2015”byRemer,Levy,Gupta,etc

3

DT

SeparatelogicoverlandandoceanRetrieve:AODat0.55µm,spectralAOD,etc

Canruninnear-real-time(NRT;takes2minutes)

Sowherearewe?

MODISC6product(ended2017)• ComparebothlandandoceanproductstoAERONET,separately• Validation:66%arewithin

“ExpectedError”(EE)definedas• Land:±(0.15t +0.05)• Ocean:±(0.10t +0.04)

• gettingclosetoCDRaccuracyrequirements!AERONET:Level2(QualityAssured)

0.67

0.22

OCEANGLINTMASK

LAND

May4,2001;13:25UTCLevel2“Granule”

AOT1.0

0.0

C6 Land, Aqua, Mar 2003−Feb 2013

0.0 0.5 1.0 1.5 2.0AERONET 0.55 µm AOD

0.0

0.5

1.0

1.5

2.0

MO

DIS

0.5

5 µm

AO

D

% within EE = 70.78% above EE = 15.85% below EE = 13.38N = 80591; R = 0.890Bias = 0.004, RMSE = 0.103Y = 1.014x−0.002

C6 Land, Aqua, Mar 2003−Feb 2013

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4AERONET 0.55 µm AOD

−0.4

−0.2

0.0

0.2

0.4

MO

DIS−A

ER

ON

ET

0.55

µm

AO

D

1 3 10 30 100 300 1000

Frequency

MODIS-Terra vs MODIS-Aqua

Terra(10:30,Descending) Aqua(13:30,Ascending)

ThetwoMODISinstrumentsareTWINS!Dotheyobservetheworldinthesameway?

Levy, R. C., et al.: Exploring systematic offsets between aerosol products from the two MODIS sensors, Atmos. Meas. Tech., 11, 4073-4092, 2018.

C6:Terra-Aqua(DT)hadglobaloffsetof0.015(13%)

C6:Andtheoffsetdrifted…

• Terra-AquaglobaloffsetofDt =~0.01-0.02• DDt isunphysical• Seasonalpattern/differenceswerelargerinlateryears(post2011).

Using“model”didnotexplainAODoffsetMERRA-2(replay)sampledat12:00UTConMay25,2008

Overpasseswithin±30minutes

Terra– Aqua Modelexpected

Somesimilaritiesin”smoke”regions

Additionalcalibration“C6+”helped(abit)

• Overland,AODoffsetisreduced(by0.005)• Overocean,negligiblechangeinAODoffset

• ForAE,C6+reducesnegativeoffset

WhataboutCollection6.1?ProcessingbeganOctober2017

• C6.1wasprimarilyfocusedonmitigatingthermalinfrareddriftsandimpactoncloudmasking

• ForDTalgorithm,C6.1included:– Correctionforbiasoverurban

surfaces– Improvementofunder-water

sedimentscreening– ”reaction”tochangesinupstream

MxD35cloudmask– Somebugfixesrelatedto

diagnostics

• DT6.1– 6.0:Changesonaglobalscale?=Nada!!!

(modis-atmosphere.gsfc.nasa.gov/documentation/collection-61)

C6.1reducestheglobalT– Adrift!

vs

C6.1

C6

UrbanRetrievalsinMODIS6.1

Surfacereflectancecorrectionasafunctionofurban%

àSignificantreductioninAODbias

ImplementedinC6.1

Islocal,willnotaffectTerra-Aquadifference

(DISCOVER-AQ, Summer2011inMaryland,USA)

Urban%

Guptaetal.,2016,AMT

MODIS-AQUADatafromCONUSRegion

WhataboutFutureMODIS?(“MaintenanceMode”)

• OurMODISworkisnowunderSeniorReviewsincelate2017.

• Wearefundedfor“maintenance”.Underthisumbrellaweare/will:– doacomprehensivevalidationofC6.1.– Ifthereisnewupstreamcalibration,wewilltestifremovesTerra-Aquaoffset

– Continueworkingwithusers– ExtractDTalgorithmfromhistoric‘MODISToolkits’socanberunindependentlyofMODAPS

• TBDwhethernew‘versions’(e.g.C6.2)or‘collections’(e.g.C7).

AerosolOpticalDepth(AOD)fromMODIS6.1:

Target metric Target CurrentwithMODISHorizontalResolution 5-10 km,globally 10kmoverice-freeandcloud-free

scenes(NodesertforDT)

Accuracy MAX(0.03 or10%) ±(0.04+10%):Ocean±(0.05+15%):Land

Stability /bias <0.01 /decade Nearlystabletrends,butoffsetsstill

TimeLength 30+years 20yearsandcounting

TemporalResolution 4h 2+ / day(Terra+Aqua)

GCOS Aerosol CDR* Requirements

*CDR = Climate Data Record

CDR=ClimateDataRecord

GlobalClimateObservingSystem

Key:Black=almostthere,Blue=ontheway,Red=notcloseorunknown

Howdowegetcloser?

BeyondMODIS

16

• Terra(18)andAqua(16)havebothhavewell-exceededtheirplannedmissionlifetimes.

• Withluck,theywilllastuntilearly2020s.• Butforclimate,weneedtocontinuetheMODISrecordover30+years

VIIRS!Visible-InfraredImagerRadiometerSuite

aboardSuomi-NPP(andfutureJPSS)

• TheNOAAoperationalproductsare“toodifferent”fromMODISforclimateresearch.

• BothDTandDBalgorithmsareported

Todevelop“continuity”weportalgorithms!(Example:DTfromMODISàVIIRS)

17

• Dealwithdifferencesinwavelengths(gascorrections/Rayleigh,etc)

• Dealwithdifferencesinresolution,etc.• RetrieveonVIIRS(comparedwithretrievalonMODIS):

0

20

40

60

80

100

% R

efle

ctan

ce

DeciduousConiferSeawater

MODISVIIRSOverlap

0.5 1.0 1.5 2.0Wavelength [µm]

Wavelengthbands&surfacespectra

Levyetal.,2015

Land

Ocean

NASAVIIRSDarkTargetProducts(2015)

MODIS-Terra vs MODIS-Aqua vs SNPP-VIIRSTerra(10:30,Descending) Aqua(13:30,Ascending)

VIIRS(13:30,Ascending)

VIIRS-SNPP hassmalloffsetcomparedtoMODIS-AquabutlessthanTerraAlsonotingseasonalcyclesaredifferent(VIIRSvsAquacomparedtoTerravsAqua)

MODIS-TvsMODIS-AvsVIIRS-SNP

Calibrationishard:“Matchfiles”

Example:0.86µmchannelover“clear”skyCloud Optical Properties: 0.86 µm Channel Radiometry

20 April 2016IRS16, Platnick et al.

Spectral Response FunctionsMODIS B2

VIIRS ~3-4% more reflective than expected

VII

RS

M7

VII

RS

CO

T

MODIS COT MODIS COT

MO

DIS

CO

T w

/b2

+3

% b

ias

RetrievedVIIRS vs MODIS COT scatterplot

MODIS COT w/3% increase in reflectance vs. baseline MODIS COT

0.800.820.840.860.880.90(l:µm)

VIIRSM7MODISB2

T1.00.80.60.40.20.0

Transmission

Sayeretal.,AMT2016Meyeretal.,thismorning

VIIRS865shouldbemultipliedby:

1.10

1.05

1.00

0.95

0.90

Year‘12’13’14’15‘16

Vicario

usGaintoapp

ly

NASAVIIRSvsMODIS(DT)Parameter MxD04 AERDT_L2_VIIRS_SNPP

Mission length Terra (2000-)10:30LSTAqua(2002-)13:30LST

SNPP (2012-)13:30LSTJPSS1(2017-)13:30LST

Pixel/Productsize(km)nadir(Level2)

0.5 kmà 10km 0.75 kmà 6km

Granulesize(pixels) 5minute(203x135) 6minute(404x400)

File Format HDF4 NetCDF4

Upstreamcloudmask

MODISCloud mask=MxD35 MODIS-VIIRS ContinuityCloudMask(MVCM)

Production LAADS(atGSFC) SIPS(atUWisconsin)

Level3 LAADS(files=MxD08) SIPS(files=TBD,$$$?)

PublicArchive LAADS(atGSFC) ?????*Pending$$$

VIIRS-SNPPDarkTargetschedule/status• Wecurrentlyhavenofundingforthiswork.

ButleveragingMODISmaintenanceandotherprojects.

• PrevioustestingofofVIIRSDThaveusedWisconsin’sIntermediateFileFormat(IFF).

• CurrentdeliveredversionusesNASA’sLevel1B(verified)• This“Version2.0.1”willassume:

– NASAL1B(calibration),– upstreamLevel2(MODIS-VIIRSContinuitycloudmask- MVCCM),– Ancillarydata=sameasMODIS

• Products(AODat0.55µm,FMW,AE,QA-Confidence,inputreflectances,etc.)areidenticaltoMODIS.

• Planforre-processingtheentiremission(2011-present)• Ifthereisrevisedupstreamcalibration,wecantestit.

• UncertainaboutwhattodowithLevel3(L3)

SeePosterbyVirginiaSawyer

VIIRSonSNPP(andbeyond)shouldincludeallupdates(e.g.6.1)forMODIS.

TowardsconsistentglobalaerosolusingDT

ComparedtoGCOSrequirementsForAerosolOpticalDepth

• JPSS-1launched(November2017),andisinSAMEORBITasS-NPP!• JPSS-2,3and4tolaunchbetween2022,2026and2031.

Target metric Target CurrentwithMODISHorizontalResolution 5-10 km,globally ≤10kmoverice-freeandcloud-free

scenes(NodesertforDT)

Accuracy MAX(0.03 or10%) ±(0.04+10%):Ocean±(0.05+15%):Land

Stability /bias <0.01 /decade Nearlystabletrends,butoffsetsstill

TimeLength 30+years CandowithMODIS+VIIRS

TemporalResolution 4h 2+ / day(Terra+Aqua/VIIRS)

What’s still missing? Breaking the Temporal Barrier!

variation of AE is 10–15% with a peak in the late morningfor all seasons.[22] The daytime changes of AOD and AE in Mexico City

are likely a combined effect of emission, photochemistry,and meteorological conditions associated with the complextopography. Mexico City is located within a basin confinedon the east, south, and west sides by mountain ridges ofabout 1000 m in height with a broad opening to the northand the gap in the mountains at the southeast end of thebasin. Local industrial and automobile emissions are twomajor sources of aerosol [Molina et al., 2007]. The precursoremissions of secondary organic aerosol (SOA) are higher inthe morning than in the afternoon. SOA is efficiently formedshortly after sunrise [Molina et al., 2007]. In the morning,the city’s unique topography and frequent atmosphericinversions trap the pollutants within the basin, likely lead-ing to rapid increase of AOD throughout the morning[Whiteman et al., 2000; Fast et al., 2007]. In the afternoon,while the photochemical processes continue to produceaerosols, the basin is efficiently vented by terrain-inducedwinds. For example, the frequently developed strongsoutheasterly flow because of differential atmospheric heat-ing [Raga et al., 1999; Doran and Zhong, 2000] brings inclean air from outside of the basin through the terrain gap inthe southeastern corner and dilutes pollution in the city,resulting in the leveled off or slight decrease of AOD in theafternoon. Photochemical processes generate new particles,which are small in size, at late morning and noon [Salcedoet al., 2006] yielding a large AE. As the afternoon pro-gresses those small particles are joined by large-size dust,kicked up by local winds, causing the AE to decrease.

4.3. Biomass Burning Aerosols in South America[23] In the dry and dry-to-wet transition season (typically

from August to October or ASO) of the central and southernAmazon, land clearing and pasture maintenance practicesgenerate a large amount of carbonaceous aerosols [Andreaeand Crutzen, 1997; Schafer et al., 2008]. Typically aerosolfrom biomass burning smoke accounts for !90% of the fineparticles and !50% of the coarse particles [Martin et al.,2010]. Figure 7 shows daytime variations of AOD for four

Figure 7. Same as Figure 3 but for sites over Amazonregion during the dry season (August–October, ASO).

Figure 6. Percentage deviations of hourly average aerosol optical depth (AOD at 440 nm, using lefty axis) and Ångström exponent (AE over 440–870 nm range, using right y axis) relative to the daily meanin four seasons in Mexico City. The vertical bar represents the standard error of measurements in eachhour. Seasonal mean AOD and AE are also shown in the figure.

ZHANG ET AL.: AEROSOL DAYTIME VARIATIONS FROM AERONET D05211D05211

9 of 13

% deviation in hourly AOD and AE relative to the daily means in Mexico City.

From: Zhang, Y., Yu, H., Eck, T. F., et al, (2012). Aerosol daytime variations over North and South America derived from multiyear AERONET measurements, J. Geophysical Research.

Global/Regional/TemporalsynergywithAconsistentDTalgorithm?

StatisticsofUTC(comparewithmodel)StatisticsofLST(understandlocaldiurnalcycle)

2018

SubjectofarecentlyfundedNASA– MEaSUREs project(withCo–Is=MinOo,JenniferWei,ShobhaKondragunta,LorraineRemer,PawanGupta)

28

PortDTalgorithmtoGEO!Spectral/Spatial:AHI/ABI≈MODIS/VIIRS

BlueGreenRedNIRNIR

CirrusSWIRSWIR

Somedetailsneedtobeworkedout(e.g.lackof“cirrus”bandonAHI);Greenband:MODIS/VIIRS@0.55µm,AHI@0.51µm,ABI@none

Intheend,wewillreportAODat0.55 µmforeveryone!SameproductsasMODIS,includingspectralAOD,cloud-clearedreflectance,etc

DT:RGBandAODfromABIforSep4,2017B.C.CanadianFiresandsmoketransport

AOD

1.0

0.8

0.6

0.4

0.2

0.0

DiurnalCycleofAODsfromAHI(fromKORUS-AQ,2016)

AERONETAHI

9 11 13 15 17

(UTCandKoreaLocalTime)

KORUS_Taehwa KORUS_Olympic_ParkXiangHe

9 11 13 15 17 9 11 13 15 17

-à GEOdoeshavesensitivitytoDiurnalCycle!!

XXX

PawanGupta

AODfromLEO+GEOwithin±30minsSept7,2017@2030UTC

Towardssynergyofaerosolobservations

LEO

SuborbitalHigh-SpatialResolution

GEO

Beyond?

ForAerosolOpticalDepth(AOD)fromLEO+GEO!

Target metric Target withLEO+GEO

HorizontalResolution 5-10 km,globally ≤10kmoverice-freeandcloud-freescenes

Accuracy MAX(0.03 or10%) ±(0.04+10%):Ocean±(0.05+15%):Land

TimeLength 30+years 30+years(MODIS+VIIRSon JPSSx)

Stability /bias <0.01 /decade Notthereyet,butpossible?

TemporalResolution 4h 20+/day(daylightonly)whereGEo

By2021therewillbemoreGEOsensors(Europe,China,etc)

Nowweneedtoworkonimprovingalgorithm,coveragetoicesurfaces.

GCOS Aerosol CDR* Requirements

*CDR = Climate Data Record

CDR=ClimateDataRecord

GlobalClimateObservingSystem

Key:Black=almostthere,Blue=ontheway,Red=notcloseorunknown

EtceteraImprovementstoDTalgorithm/products

• Improvedcoverageforheavyaerosolevents(Indonesianfires,Beijingsmoke,etc)=Yingxi Shi

• Improveddustdetectionanddustopticalpropertiestoreducebiasfordustclimatology=Yaping Zhou

• Improvedretrievals(andcoverage)overcoastalenvironments=YiWang/JunWang (U-Iowa)

• Alternativesforaerosolretrievaloverbrightersurfaces?• Retrievalsonhigherresolutiondata(eMAS,Landsat,Sentinel)=Shana

Mattoo

UseofDTproductsbyScienceTeam• SynergywithUVradiances(e.g.OMPS/VIIRS)=SantiagoGásso• Correctingfor3Deffectsinaerosolnearclouds=Tamás Varnai• UsingDTandotherproductstolookatfiresinIndia=PawanGupta• UsingDTandotherproductstolookatdustandradiation=HongbinYu

ConclusionI:Longandwideaerosolclimatology

• AODisanEssentialClimateVariable,canberetrievedwiththeDark-Targetalgorithm,fromanysensorthathassufficientobservationsofmulti-spectral(VIS/NIR/SWIR)reflectance.

• ValidationshowsthatDTonMODISnearlymeets2outof5requirementsofaClimateDataRecord:Spatialresolutionandaccuracy.

• MODISC6.1isimprovementoverC6duetonewurbanretrieval,andupstreamcorrectionsthatreducerelativedriftingofTerraversusAqua.

• C6.1onMODISstillshowsunexplained10-15%globaloffsetbetweenTerraandAqua.Withcontinuedupdatesincalibration/stabilityofsensorobservations,wemaymeet3rd CDRrequirementofconsistency.

• DTisportedtoVIIRS,andtheproductsarealmostconsistentenoughtocontinuetimeseriestobeyond30years,meeting4th CDRrequirement.

• WithDTretrievalonGEOsensors,andmorecomingonline,wearegettingclosertomeeting5th CDRrequirementoftemporalresolution.

ConclusionII:Longandwideaerosolclimatology

• ManyfolksarecurrentlyusingorproposingtouseDTproductsfromMODISand/orVIIRS,however,ourteamisfundedonlyforMODIS“maintenance”.

• Fornow,weareleveragingGEOfundingtocontinuedeliveryof1st versionofVIIRSDTalgorithmandproducts.(Wehopethiscanchange!)

• WearegratefulfortheWisconsinSIPStocontinuesupportingourefforts.

• TherearestillsignificantimprovementsthatarepossibleforDTalgorithmandproducts.

THANKYOU!

PleaseseeVirginiaSawyer’sposter

Somerecentpublications1. Levy,R.C.,Mattoo,S.,Sawyer,V.,Shi,Y.,Colarco,P.R.,Lyapustin,A.I.,Wang,Y.,Remer,L.A.

2018.ExploringsystematicoffsetsbetweenaerosolproductsfromthetwoMODISsensors, AtmosphericMeasurementTechniquesDiscussions:1-37

2. Gupta,P.,Remer,L.A.,Levy,R.C.,Mattoo,S.2018.ValidationofMODIS3 kmlandaerosolopticaldepthfromNASA'sEOSTerraandAquamissions, AtmosphericMeasurementTechniques, 11(5):3145-3159

3. Patadia,F.,Levy,R.,Mattoo,S.2018.Correctingfortracegasabsorptionwhenretrievingaerosolopticaldepthfromsatelliteobservationsofreflectedshortwaveradiation, AtmosphericMeasurementTechniquesDiscussions, 2018:1—45

4. Shi,Y.R.,Levy,R.C.,Eck,T.F.,Fisher,B.,Mattoo,S.,Remer,L.A.,Slutsker,I.,andZhang,J.:Characterizingthe2015IndonesiaFireEventUsingModifiedMODISAerosolRetrievals,Atmos.Chem.Phys.Discuss.,https://doi.org/10.5194/acp-2018-468,[inreview,2018.

5. Wang,Y.,J.Wang,R.C.Levy,X.Xu,andJ.S.Reid.2017."MODISRetrievalofAerosolOpticalDepthoverTurbidCoastalWater."RemoteSensing,9(6):595[10.3390/rs9060595]