The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate...
Transcript of The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate...
![Page 1: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/1.jpg)
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)
![Page 2: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/2.jpg)
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...)
![Page 3: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/3.jpg)
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)
![Page 4: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/4.jpg)
Sowherearewe?
![Page 5: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/5.jpg)
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
![Page 6: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/6.jpg)
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.
![Page 7: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/7.jpg)
C6:Terra-Aqua(DT)hadglobaloffsetof0.015(13%)
![Page 8: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/8.jpg)
C6:Andtheoffsetdrifted…
• Terra-AquaglobaloffsetofDt =~0.01-0.02• DDt isunphysical• Seasonalpattern/differenceswerelargerinlateryears(post2011).
![Page 9: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/9.jpg)
Using“model”didnotexplainAODoffsetMERRA-2(replay)sampledat12:00UTConMay25,2008
Overpasseswithin±30minutes
Terra– Aqua Modelexpected
Somesimilaritiesin”smoke”regions
![Page 10: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/10.jpg)
Additionalcalibration“C6+”helped(abit)
• Overland,AODoffsetisreduced(by0.005)• Overocean,negligiblechangeinAODoffset
• ForAE,C6+reducesnegativeoffset
![Page 11: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/11.jpg)
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)
![Page 12: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/12.jpg)
C6.1reducestheglobalT– Adrift!
vs
C6.1
C6
![Page 13: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/13.jpg)
UrbanRetrievalsinMODIS6.1
Surfacereflectancecorrectionasafunctionofurban%
àSignificantreductioninAODbias
ImplementedinC6.1
Islocal,willnotaffectTerra-Aquadifference
(DISCOVER-AQ, Summer2011inMaryland,USA)
Urban%
Guptaetal.,2016,AMT
MODIS-AQUADatafromCONUSRegion
![Page 14: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/14.jpg)
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).
![Page 15: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/15.jpg)
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?
![Page 16: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/16.jpg)
BeyondMODIS
16
• Terra(18)andAqua(16)havebothhavewell-exceededtheirplannedmissionlifetimes.
• Withluck,theywilllastuntilearly2020s.• Butforclimate,weneedtocontinuetheMODISrecordover30+years
VIIRS!Visible-InfraredImagerRadiometerSuite
aboardSuomi-NPP(andfutureJPSS)
• TheNOAAoperationalproductsare“toodifferent”fromMODISforclimateresearch.
• BothDTandDBalgorithmsareported
![Page 17: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/17.jpg)
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
![Page 18: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/18.jpg)
Land
Ocean
NASAVIIRSDarkTargetProducts(2015)
![Page 19: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/19.jpg)
MODIS-Terra vs MODIS-Aqua vs SNPP-VIIRSTerra(10:30,Descending) Aqua(13:30,Ascending)
VIIRS(13:30,Ascending)
![Page 20: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/20.jpg)
VIIRS-SNPP hassmalloffsetcomparedtoMODIS-AquabutlessthanTerraAlsonotingseasonalcyclesaredifferent(VIIRSvsAquacomparedtoTerravsAqua)
MODIS-TvsMODIS-AvsVIIRS-SNP
![Page 21: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/21.jpg)
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
![Page 22: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/22.jpg)
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,$$$?)
PublicArchive LAADS(atGSFC) ?????*Pending$$$
![Page 23: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/23.jpg)
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
![Page 24: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/24.jpg)
VIIRSonSNPP(andbeyond)shouldincludeallupdates(e.g.6.1)forMODIS.
TowardsconsistentglobalaerosolusingDT
![Page 25: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/25.jpg)
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)
![Page 26: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/26.jpg)
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.
![Page 27: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/27.jpg)
Global/Regional/TemporalsynergywithAconsistentDTalgorithm?
StatisticsofUTC(comparewithmodel)StatisticsofLST(understandlocaldiurnalcycle)
2018
SubjectofarecentlyfundedNASA– MEaSUREs project(withCo–Is=MinOo,JenniferWei,ShobhaKondragunta,LorraineRemer,PawanGupta)
![Page 28: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/28.jpg)
28
PortDTalgorithmtoGEO!Spectral/Spatial:AHI/ABI≈MODIS/VIIRS
BlueGreenRedNIRNIR
CirrusSWIRSWIR
Somedetailsneedtobeworkedout(e.g.lackof“cirrus”bandonAHI);Greenband:MODIS/[email protected]µm,[email protected]µm,ABI@none
Intheend,wewillreportAODat0.55 µmforeveryone!SameproductsasMODIS,includingspectralAOD,cloud-clearedreflectance,etc
![Page 29: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/29.jpg)
DT:RGBandAODfromABIforSep4,2017B.C.CanadianFiresandsmoketransport
AOD
1.0
0.8
0.6
0.4
0.2
0.0
![Page 30: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/30.jpg)
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
![Page 31: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/31.jpg)
AODfromLEO+GEOwithin±30minsSept7,2017@2030UTC
![Page 32: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/32.jpg)
Towardssynergyofaerosolobservations
LEO
SuborbitalHigh-SpatialResolution
GEO
Beyond?
![Page 33: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/33.jpg)
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
![Page 34: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/34.jpg)
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
![Page 35: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/35.jpg)
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.
![Page 36: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/36.jpg)
ConclusionII:Longandwideaerosolclimatology
• ManyfolksarecurrentlyusingorproposingtouseDTproductsfromMODISand/orVIIRS,however,ourteamisfundedonlyforMODIS“maintenance”.
• Fornow,weareleveragingGEOfundingtocontinuedeliveryof1st versionofVIIRSDTalgorithmandproducts.(Wehopethiscanchange!)
• WearegratefulfortheWisconsinSIPStocontinuesupportingourefforts.
• TherearestillsignificantimprovementsthatarepossibleforDTalgorithmandproducts.
THANKYOU!
PleaseseeVirginiaSawyer’sposter
![Page 37: The Dark Target aerosol retrieval algorithm: From MODIS to VIIRS … · 2018-10-24 · Separate logic over land and ocean ... [Raga et al., 1999; Doran and Zhong, 2000] brings in](https://reader033.fdocuments.in/reader033/viewer/2022042012/5e72dccb3e11ec7cd704665b/html5/thumbnails/37.jpg)
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]