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Aerosol Single Sca.ering Albedo and Layer
Height using VIIRS and OMPS-NM
San$agoGassó,GSFC/ESSICRobertC.Levy,GSFCOmarTorres,GSFC
YingxiShi,GSFC/USRAMODIS/VIIRSScienceTeamMee$ng,October15–19,2018,SilverSpring,MD
• AerosolAbsorp$on(AA)isoneoftheoutstandingchallengesinaerosolresearchandclimateapplica$ons
Mo$va$on:LargeVariabilityinAnnualMeanver$caldistribu$onofAbsorbingAerosolsinModels
• Forcingandhea$ngratecomputa$onsarecri$callylinkedtoaerosolsinglescaWeringalbedo(SSA)andlayerheight(Z)
Kiplingetal.,2016,AeroCom
DustandSmokeMeanVer$calDistribu$on
Thereisaneedtoimprove
globalobserva$onsof
AerosolAbsorp$onandHeight
AnotherMo$va$on:BeWerAAinforma$onwillbenefitCurrentAerosolAlgorithms
RecentStudiesHighlightbiasesinAODretrievalsbecauselackofaerosolabsorp$onandheightinforma$on
3.2. Seasonal Analysis of MODIS and MISRAOD Retrievals[24] Any consistent seasonal shift in SSA has the potential
to impact satellite AOD retrievals and thus result in a season-ally varying bias which could be perceived as random noisein a large verification study. Retrieval of AOD from thestandard MODIS algorithms, for example, is dependent onthe a priori assumption of a value of aerosol absorption[Remer et al., 2005; Levy et al., 2007a, 2007b]. Deviationfrom the assumed values has a compounding effect onretrieval bias as AODs increase into multiple scatteringenvironments. AERONET measured AOD is highly accurate(~0.01 in the visible and near infrared) and therefore compar-ison to satellite retrievals allows for analysis of the likelyreasons for potential biases, primarily either from errors incharacterizing surface reflectance or in allowing for a realistic
enough aerosol model. Given the high AODs found in thesouthern Africa smoke plume, the seasonal variation inAERONET inverted SSA provides a natural laboratory totest the impact of SSA on satellite AOD retrieval error.Indeed, as we found no systematic seasonal microphysicalchanges in the retrievals other than absorption, the Mongusite allows the examination of a rarely occurring “partial de-rivative” of satellite retrieval microphysical models.[25] To begin, comparisons of AOD retrievals from both
Terra and Aqua satellite MODIS sensors against the MonguAERONET site are presented in Figures 11a and 11b, respec-tively. Comparisons of AOD are shown for 550 nm with theAERONET data being interpolated to 550 nm. This matchupwas described in section 2. To observe the influence ofseasonal variation of SSA, data are divided into the threecore months of burning, August, September, and October,
Figure 11. Comparison of MODIS retrievals (dark target algorithm) from both (a) Terra 2001–2010 and (b)Aqua 2003–2010 of AOD at 550nm to AERONET measurements at Mongu, Zambia, with data separated forthe primary burning season months of August, September, and October. Figures 11c and 11d are the same asFigures 11a and 11b but for the NRL data assimilation grade MODIS product of Hyer et al. [2011].
ECK ET AL.: TREND IN PARTICLE SSA IN SOUTHERN AFRICA
6423
(Ecketal.,2013)
Devia$onsfoundinSSAassump$on Similarlybyacustomizedaerosollayer
(Wuetal.,2017)
AtmosphericRadianceisdis$nc$velydifferentintheUV
ExamplefromtheCloudAerosolImager
680nm
ITOA=IATM+ISURFSmoke/dust
ISURF
VisibletoNear-IR
• Surface:difficultto
modelorassume
• SignificantSurfacecontribu$on
• MinorSensi$vitytoAerosolabsorp$on
AtmosphericRadianceisdis$nc$velydifferentintheUV
382nm
• AtmosphericSignalisdominatedbyRayleighscaWering(easytomodel)
• MinorSurfacecontribu$on
• SignificantSensi$vitytoAerosolAbsorp$on
UV
IATM~IRAY+IAEROSOL
ITOA=IATM+ISURF
RayleighScaWering
Smoke/dust
ExamplefromtheCloudAerosolImager
680nm
ITOA=IATM+ISURFSmoke/dust
ISURF
VisibletoNear-IR
• Surface:difficultto
modelorassume
• SignificantSurfacecontribu$on
• MinorSensi$vitytoAerosolabsorp$on
80’sand90’s:AVHRR/GOESandTOMS
SimultaneousSpa$alResolu$onUV-NearIR
VisibleNear-IR
5008002100
Near-UV
330400
~50x50km
TOMS
~1km
AVHRR/GOES
It’sbeenalongwaytoachieveUVtonear-IRaerosolretrievals
Pastmissionsdidnothavetherightfeatures
It’sbeenalongwaytoachieveUVtonear-IRaerosolretrievals
Pastmissionsdidnothavetherightfeatures
80’sand90’s:AVHRR/GOESandTOMS
SimultaneousSpa$alResolu$onUV-NearIR
VisibleNear-IR
5008002100
Near-UV
330400
~50x50km
TOMS
~1km
AVHRR/GOES
SimultaneousSpa$alResolu$onü UV-NearIR
2000’s:AquaandAura
Near-UV
330400~20x13km
Aura-OMI
Visible Near-IR
4005008002100
0.5kmAqua-MODIS
~7–15min
Near-UVVisibleNear-IR
0.5km
~50x50km
VIIRS&OMPS-NM
ü SimultaneousSpa$alResolu$onü UV-NearIR
Near-UVVisNear-IR
3805002100
~1km
PACE
ü Simultaneousü Spa$alResolu$onü UV-NearIR
ThePresentisgoodandtheFuturebodesverywell
IntheUV,aerosol τ,ωoandZmodulatetheatmosphericpathradiance
ITOA=IATM(τ,ωo,z)
AerosolUVTop-of-atmosphereRadianceisanUnconstrainedRetrieval
AerosolUVretrievalsrelyonobserva$onsattwobandsandtheITOAismodulatedby
3parameters(τ,ωo,z)
IntheUV,aerosol τ,ωoandZmodulatetheatmosphericpathradiance
ITOA=IATM(τ,ωo,z)=IATM(τ ,ωo,z)
AerosolUVTop-of-atmosphereRadianceisanUnconstrainedRetrieval
AerosolUVretrievalsrelyonobserva$onsattwobandsandtheITOAismodulatedby
3parameters(τ,ωo,z)
IntheUV,aerosol τ,ωoandZmodulatetheatmosphericpathradiance
ITOA=IATM(τ,ωo,z)=IATM(τ ,ωo,z)=IATM(τ ,ωo,z)
AerosolUVTop-of-atmosphereRadianceisanUnconstrainedRetrieval
AerosolUVretrievalsrelyonobserva$onsattwobandsandtheITOAismodulatedby
3parameters(τ,ωo,z)
Allthesescenarioshavesameupwelling
radiance
IntheUV,aerosol τ,ωoandZmodulatetheatmosphericpathradiance
ITOA=IATM(τ,ωo,z)=IATM(τ ,ωo,z)=IATM(τ ,ωo,z)
AerosolUVTop-of-atmosphereRadianceisanUnconstrainedRetrieval
AerosolUVretrievalsrelyonobserva$onsattwobandsandtheITOAismodulatedby
3parameters(τ,ωo,z)
DeriveAerosolOp$calDepthwithaVISsensor(MODIS/VIIRS)anduseitto
constraintheUVretrieval(2obsand2unknowns,ωoandz)
Allthesescenarioshavesameupwelling
radiance
MODIS:DarkTarget Aerosolmodelsw/variablemodeconc.&MixedSSA
Outputs τVIS,τNIR
OMI: DeriveSSAandZ
Aerosolmodelsw/Mix
modeconc.&variableSSA Outputsz, τUV,ωUV
ExtrapolateMODISτtothenear-UV Input τNUV=f(τVIS,τNIR)
MODIS OMI-AerosolIndex
Exis$ngMethodologyusingMODISandOMIsynergy
MODIS:DarkTarget Aerosolmodelsw/variablemodeconc.&MixedSSA
Outputs τVIS,τNIR
OMI: DeriveSSAandZ
Aerosolmodelsw/Mix
modeconc.&variableSSA Outputsz, τUV,ωUV
ExtrapolateMODISτtothenear-UV Input τNUV=f(τVIS,τNIR)
MODIS OMI-AerosolIndex
OMI-MODIS
GassóandTorres,Theroleofcloudcontamina/on,aerosollayerheightand
aerosolmodelintheassessmentoftheOMInear-UVretrievalsovertheocean(2016)
Sa/sfactoryresultswhencomparedwithLidar
Satheeshetal.,Improvedassessmentofaerosolabsorp1onusingOMI-MODISjointretrieval(2009)
Exis$ngMethodologyusingMODISandOMIsynergy
However,OMIandMODISoverlapsarenotquiteadequateforasystema$cretrievalapproach
Both,$medifference(cloud
forma$on)andviewing
geometryareanimpediment
forasystema$cUVtoNear-IR
retrieval
Yellownumbersingrid:AerosolIndex(len)
MODISimagewithOMIgridoverlap
OMIAODretrieved(right)
Near-UVVisibleNear-IR
0.5km
~50x50km
VIIRS&OMPS-NM
ü SimultaneousSpa$alResolu$onü UV-NearIR
Near-UVVisNear-IR
3805002100
~1km
PACE
ü Simultaneousü Spa$alResolu$onü UV-NearIR
OMPS-NMHighResolu$onmodeandVIIRSareanimprovement
Near-UVVisibleNear-IR
0.5km
~50x50km
VIIRS&OMPS-NM
ü SimultaneousSpa$alResolu$onü UV-NearIR
Near-UVVisNear-IR
3805002100
~1km
PACE
ü Simultaneousü Spa$alResolu$onü UV-NearIR
Near-UVVisibleNear-IR
0.5km~12x5km~12x12km
VIIRS&OMPS-NM-HighResMode
ü Simultaneousü Spa$alResolu$onü UV-NearIR
OMPS-NMHighResolu$onModeandVIIRSareanimprovement
OMPS-NMhastwoHighResolu$onModes
~12x12km~12x5km
• ~4000orbitsspanning4years(1day/week)
• “High”res~12x12km,similartoOMI
• “Superhigh”res~12x5km,BeWerthanOMI!!
ASynerge$cUVtoNear-IRAlgorithm:FusionDarkTarget–OMAERUVapproach
AconsistentmethodologyforretrievalsofAOD,SSAandZusing
Near-UVtonear-IRradiances
ExtrapolateOMIωototheVIS
Input:Z&ωVIS=f(ωUV,AE)
VIIRS:DarkTarget Aerosolmodelsw/variablemodeconc.&MixedSSA
Outputs:τVIS,τNIR
OMPS: DeriveSSAandZ
Aerosolmodelsw/Mixmodeconc.&variableSSA
Outputs:z, ωUV
ExtrapolateMODISτtothenear-UV
Input:τNUV=f(τVIS,τNIR)
ASynerge$cUVtoNear-IRAlgorithm:FusionDarkTarget–OMAERUVapproach
Methodologyalreadydeveloped(GassóandTorres,2016)
ToBedevelopedinthisproject
AconsistentmethodologyforretrievalsofAOD,SSAandZusing
Near-UVtonear-IRradiances
ExtrapolateOMIωototheVIS
Input:Z&ωVIS=f(ωUV,AE)
VIIRS:DarkTarget Aerosolmodelsw/variablemodeconc.&MixedSSA
Outputs:τVIS,τNIR
OMPS: DeriveSSAandZ
Aerosolmodelsw/Mixmodeconc.&variableSSA
Outputs:z, ωUV
ExtrapolateMODISτtothenear-UV
Input:τNUV=f(τVIS,τNIR)
ExpectedOutcomeandSummary
• GlobalMapsofSSAandZfromcombinedOMPS-NMandVIIRS
• UnifiedMethodologyforAerosolretrievalsofAOD,SSAandZ
fromtheUVtoNearIR
Misc
Figure 3:Illustration of the standard (or operational, Torres et al., 2013) and hybrid (Satheesh et al., 2009) retrieval schemes. The standard algorithm computes the pair of AOD and SSA at five assumed aerosol heights for the pixel’s viewing geometry (blue solid lines and circles). In a prior step, it selected an aerosol model and surface albedo used in the computation. Each triplet (Z, SSA and AOD) has a corresponding upwelling radiance matching the observed radiance by OMI. To select the final (or retrieved) AOD and SSA for the pixel,the operational algorithm uses a climatological height (Zc-clm, red arrow and red dashed lines). The hybrid method uses a VIS AOD (extrapolated to 388nm) as entry point (black arrow and black dashed lines) to determine the Z and SSA using the triplets from the lookup table.
ZC-CLM
ZHYB τMOD
τOMI
ωHYB ωOMI
ExampleofUVRadiancesaggregatedatdifferentpixelsizeovertheNevadaDesert(fromCAIBand1)
0.5-1kmx0.5-1km20kmx13km40-60kmx40-60km
TOMS,OMPS OMI CAI-2,PACE,ACE?
TEMPO,OMPS(JPS1)TropOMI
3-10kmx3-10km
1970s-1990s 2000s 2017> 2020s>
Spa$alresolu$oninnearUVsensorshasbeenimprovingsteadily