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MODIS/Aqua and PARASOL Observation of Clouds and Aerosols PropertiesMODIS/Aqua and PARASOL Observation of Clouds and Aerosols Properties MODIS Sci. Team Meeting Oct. 2006MODIS Sci. Team Meeting Oct. 2006

Aqua/MODIS and Parasol/POLDER3 Aqua/MODIS and Parasol/POLDER3 Observations of Clouds and Aerosols PropertiesObservations of Clouds and Aerosols Properties

Jérôme RiédiJérôme Riédi, Didier Tanré, Frédéric Parol, Jean-Luc Deuzé, Didier Tanré, Frédéric Parol, Jean-Luc Deuzéand the PARASOL science team.and the PARASOL science team.

Laboratoire d’Optique Atmosphérique, USTL, Lille, FranceLaboratoire d’Optique Atmosphérique, USTL, Lille, France

OUTLINEOUTLINEContextContext

Contribution from POLDER/ParasolContribution from POLDER/ParasolInstruments synergyInstruments synergy

Data and products availabilityData and products availability

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ContextContext

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Instrumental Background : POLDERInstrumental Background : POLDER

– CNES/LOA instrument, Parasol launched Dec. 2004CNES/LOA instrument, Parasol launched Dec. 2004• ~ 705 km polar orbits, ascending (13:30 a.m.)~ 705 km polar orbits, ascending (13:30 a.m.)

– Sensor CharacteristicsSensor Characteristics• 10 spectral bands ranging from 0.443 to 1.020 µm10 spectral bands ranging from 0.443 to 1.020 µm• 3 polarised channels3 polarised channels• Wide FOV CCD Camera with 1800 km swath Wide FOV CCD Camera with 1800 km swath

widthwidth• +/- 43 degrees cross track+/- 43 degrees cross track• +/- 51degrees along track+/- 51degrees along track• Multidirectionnal observations (up to 16 Multidirectionnal observations (up to 16

directions)directions)• Spatial resolution : 6x7 kmSpatial resolution : 6x7 km• No onboard calibration system - Inflight vicarious No onboard calibration system - Inflight vicarious

calibration : calibration : – 2-3% absolute calibration accuracy2-3% absolute calibration accuracy– 1% interband – 0.1% interpixel over clouds1% interband – 0.1% interpixel over clouds

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Contribution of POLDER/PARASOL to the A-TrainContribution of POLDER/PARASOL to the A-Train1.5 year of POLDER3/Parasol data already available1.5 year of POLDER3/Parasol data already available

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Contribution of POLDER/Parasol to the A-TrainContribution of POLDER/Parasol to the A-TrainBenefits from multiangle observations : Application for cloudsBenefits from multiangle observations : Application for clouds

Cloud spherical albedo is retrieved under up to 16 directions Cloud spherical albedo is retrieved under up to 16 directions

Directional product provided at Directional product provided at = 670nm (land) and 865 nm (ocean)= 670nm (land) and 865 nm (ocean)

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Testing cloud models from multiangle observationTesting cloud models from multiangle observation

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Sphere used for liquid clouds Sphere used for liquid clouds Sphere used for ice clouds Sphere used for ice clouds

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But ... We have many other potential culprit for But ... We have many other potential culprit for deviation from Plane Parallel Modeldeviation from Plane Parallel Model

MODIS can help in removing or detecting MODIS can help in removing or detecting effects from particle size, subpixel variability, effects from particle size, subpixel variability,

etc... etc...

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Detection of small broken clouds / pixel heterogeneity within Parasol FOVDetection of small broken clouds / pixel heterogeneity within Parasol FOV

R : 2.1 µmG : 0.86 SDTVB : 0.86 µm

True color RGBMid Atlantique

MODIS Obs.

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Contribution of POLDER/Parasol to the A-TrainContribution of POLDER/Parasol to the A-TrainBenefits from multiangle observations : Application for aerosolsBenefits from multiangle observations : Application for aerosols

Over Ocean « Cases 1-2-3 »Over Ocean « Cases 1-2-3 »

AccAcc

AccAcc rrAccAcc, m, mAccAcc, , AccAcc rrAccAcc, m, mAccAcc, m, mcoarsecoarse, ,

NSINSI nsns//cscs++cnscns))

GLINTGLINTWestWest EastEast

13 views of the same 13 views of the same targettarget

(Herman et al., J.G.R. 2005)(Herman et al., J.G.R. 2005)

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Contribution of POLDER/Parasol to the A-TrainContribution of POLDER/Parasol to the A-TrainBenefits from polarization observations : Application for cloudsBenefits from polarization observations : Application for clouds

Riedi et alRiedi et al Bréon, Chepfer et alBréon, Chepfer et al

Cloud PhaseCloud Phase Cloud microphysicsCloud microphysics

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Contribution of POLDER/Parasol to the A-TrainContribution of POLDER/Parasol to the A-TrainBenefits from polarization observations : Application for aerosolsBenefits from polarization observations : Application for aerosols

Clear atmosphere (AOT=0.03) : the reflectance Clear atmosphere (AOT=0.03) : the reflectance at TAO is close to the surface valuesat TAO is close to the surface values

AEROSOLAEROSOL

Illustration for Biomass Burning Aerosols (from POLDER2)Illustration for Biomass Burning Aerosols (from POLDER2)

Hazy atmosphere (AOT=0.31) : Hazy atmosphere (AOT=0.31) : large aerosol contributionlarge aerosol contribution

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Clear atmosphere (AOT=0.08) : Clear atmosphere (AOT=0.08) : the reflectance at TAO is again the reflectance at TAO is again

close to the surface valuesclose to the surface values

Illustration for Desert Aerosols (POLDER2)Illustration for Desert Aerosols (POLDER2)

Hazy atmosphere (AOT=0.71) : Hazy atmosphere (AOT=0.71) : no aerosol contributionno aerosol contribution

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Colloque Bilan/Prospective Programme National de Télédétection Spatiale 18-20 Avril 2005 - Orléans

Discrimination between spherical and non-spherical aerosolsDiscrimination between spherical and non-spherical aerosols

Deuzé, Herman et alDeuzé, Herman et al


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Potential Synergy for CloudsPotential Synergy for Clouds

Cloud detectionCloud detectionCloud detection can be tricky under many circumstances (heavy aerosol loading, glint, Cloud detection can be tricky under many circumstances (heavy aerosol loading, glint, bright surfaces : desert, snow/ice)bright surfaces : desert, snow/ice)

Cloud layers heightCloud layers heightDeriving multiple cloud top pressure (O2, Rayleigh, CO2 slicing, H20) to detect Deriving multiple cloud top pressure (O2, Rayleigh, CO2 slicing, H20) to detect multilayer clouds and better describe vertical structure multilayer clouds and better describe vertical structure

Cloud thermodynamic phaseCloud thermodynamic phaseCombination of information on particle shape and absorption properties help Combination of information on particle shape and absorption properties help

Improved cloud retrievalsImproved cloud retrievalsex : ex : Using Size retrieval from MODIS to improve multidirectionnal OT retrievals from Using Size retrieval from MODIS to improve multidirectionnal OT retrievals from POLDERPOLDER

Cloud HeterogeneitiesCloud HeterogeneitiesUsing MODIS 250m information to understand angular behavior in POLDER Using MODIS 250m information to understand angular behavior in POLDER measurements and separate 3D effect from subpixel heterogeneitiesmeasurements and separate 3D effect from subpixel heterogeneities

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Cloud thermodynamic phaseCloud thermodynamic phaseCombination of information on particle shape and absorption properties Combination of information on particle shape and absorption properties

BasisBasis

PolarizationPolarization (Riedi et al) (Riedi et al)mostly single scattering mostly single scattering sensitive to particle shapesensitive to particle shapeTop of cloud but see through it if very thinTop of cloud but see through it if very thin

SWIR (Platnick et al)SWIR (Platnick et al)Differential Water/Ice Absorption Differential Water/Ice Absorption sensitive to particle sizesensitive to particle sizeSome depth in the cloudSome depth in the cloud

Thermal IR (Baum et al)Thermal IR (Baum et al)Diff. Water/Ice, Diff. Water/Ice, also sensitive to surf. emissivity, H2Oalso sensitive to surf. emissivity, H2OSome depth in the cloud except thin cirrusSome depth in the cloud except thin cirrus

Cirrus ? Thin ?Cirrus ? Thin ?

Water ? Mixed ?Water ? Mixed ?

H2O ?H2O ?

Surface spectral albedo ?Surface spectral albedo ?

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Cloud thermodynamic phaseCloud thermodynamic phase

LIQUIDLIQUIDICEICE

Scattering AngleScattering AngleTyphoon NabiTyphoon Nabi2 Sept. 20052 Sept. 2005

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LIQUIDLIQUIDICEICE

Scattering AngleScattering AngleTyphoon NabiTyphoon Nabi2 Sept. 20052 Sept. 2005

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SWIR + VIS SWIR + VIS RGB Composite RGB Composite (MODIS bands 1, 2 and 7)(MODIS bands 1, 2 and 7)

BTD 8 – 11 µmBTD 8 – 11 µm

- 6- 6 + 6+ 6

Typhoon NabiTyphoon Nabi2 Sept. 20052 Sept. 2005

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Cloud thermodynamic phaseCloud thermodynamic phaseCombination of information on particle shape and absorption properties help Combination of information on particle shape and absorption properties help

ICEICE LIQUIDLIQUIDUNKOWNUNKOWN

POLARIZATIONPOLARIZATION SWIR/VIS RatioSWIR/VIS Ratio IR BispectralIR Bispectral

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ICEICE

LIQUIDLIQUID

MIXEDMIXED

Results from the combined Results from the combined POLDER/MODIS phase POLDER/MODIS phase algorithmalgorithm

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Cloud layers heightCloud layers heightDeriving multiple cloud top pressure (ODeriving multiple cloud top pressure (O22, Rayleigh, CO, Rayleigh, CO22 slicing) to detect multilayer slicing) to detect multilayer clouds and better describe vertical structure clouds and better describe vertical structure

Basis Basis We do expect differences in pressure due to resp. sensitivities and we also expect We do expect differences in pressure due to resp. sensitivities and we also expect increasing differences in case of multilayer situationsincreasing differences in case of multilayer situations

COCO22

OO22

RayleighRayleigh

Single LayerSingle Layer Two LayersTwo Layers

COCO22RayleighRayleigh

OO22

OO22 : Oxygen band differential absorption : Oxygen band differential absorptionRayleigh : Polarization Rayleigh Scattering absorptionRayleigh : Polarization Rayleigh Scattering absorption

COCO22 : CO : CO22 Slicing (IR) Slicing (IR)

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Cloud layers heightCloud layers heightDeriving multiple cloud top pressure (ODeriving multiple cloud top pressure (O22, Rayleigh, CO, Rayleigh, CO22 slicing) to detect multilayer slicing) to detect multilayer clouds and better describe vertical structure clouds and better describe vertical structure

Example of retrieved Cloud Top Pressure Histograms for Ice cloudsExample of retrieved Cloud Top Pressure Histograms for Ice clouds

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PARASOL and CALIOP Cloud Top Pressure PARASOL and CALIOP Cloud Top Pressure Preliminary ComparisonPreliminary Comparison

15 June 200615 June 2006POLDER Cloud Cover > 95% POLDER Cloud Cover > 95% CALIOP Alt. Stdev < 0.5 kmCALIOP Alt. Stdev < 0.5 km

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PARASOL and CALIOP Cloud Top Pressure PARASOL and CALIOP Cloud Top Pressure Preliminary ComparisonPreliminary Comparison

15 June 200615 June 2006POLDER Cloud Cover > 95% POLDER Cloud Cover > 95% CALIOP Alt. Stdev < 0.5 kmCALIOP Alt. Stdev < 0.5 km

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Example : aerosols over cloudExample : aerosols over cloud

24 Septembre 200524 Septembre 2005

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Example : aerosols over cloudExample : aerosols over cloud

OO22 Pressure Pressure Rayleigh P.Rayleigh P. COCO22 Pressure Pressure

Usually with single layer : Usually with single layer : OO22 > Rayleigh > CO > Rayleigh > CO22 with small differences with small differences

And here we have : And here we have : OO22 > CO > CO22 >> Rayleigh >> Rayleigh due to presence of aerosol in due to presence of aerosol in the upper layerthe upper layer

100 hPa100 hPa

1000 hPa1000 hPa

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POLDER3/Parasol Products availabilityPOLDER3/Parasol Products availability

Data available since March 2005. Data available since March 2005.

Level1 : calibrated georeferenced dataLevel1 : calibrated georeferenced dataLevel2 : daily products – one file per orbit swathLevel2 : daily products – one file per orbit swathLevel3 : monthly products Level3 : monthly products Joint Atmosphere product (selected daily and monthly products)Joint Atmosphere product (selected daily and monthly products)

Data processed with collection 2 algorithms (Data processed with collection 2 algorithms (heritage from heritage from POLDER1 and POLDER2 missionPOLDER1 and POLDER2 mission))

L1 data and products (also including MODIS/Aqua, Caliop, ...) L1 data and products (also including MODIS/Aqua, Caliop, ...) available online from the ICARE data center available online from the ICARE data center

http://www.icare.univ-lille1.fr/http://www.icare.univ-lille1.fr/

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SummarySummary

PARASOL/MODIS combination is a real opportunity to improve many PARASOL/MODIS combination is a real opportunity to improve many existing parameters and can help design a next generation sensorexisting parameters and can help design a next generation sensor

PARASOL/MODIS open perspectives to extend the active sensors PARASOL/MODIS open perspectives to extend the active sensors observation to the full swath, increasing statisticsobservation to the full swath, increasing statistics

PARASOL/MODIS offer a test suite for definition of future cloud/aerosol PARASOL/MODIS offer a test suite for definition of future cloud/aerosol missionsmissions

1.5 year of POLDER3/Parasol data are available through the ICARE data 1.5 year of POLDER3/Parasol data are available through the ICARE data center : center : http://www.icare.univ-lille1.fr/http://www.icare.univ-lille1.fr/

Get involved : we provide users with POLDER/MODIS joint dataset and Get involved : we provide users with POLDER/MODIS joint dataset and software to merge data and try ideas.software to merge data and try ideas.

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Additional Material for DiscussionAdditional Material for Discussion

Cloud detectionCloud detection

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Cloud detectionCloud detectionCloud detection can be tricky under many circumstances (heavy aerosol loading, glint, Cloud detection can be tricky under many circumstances (heavy aerosol loading, glint, bright surfaces : desert, snow/ice)bright surfaces : desert, snow/ice)

BasisBasis

MODIS multispectral total reflectance measurements are not always sufficient to perform MODIS multispectral total reflectance measurements are not always sufficient to perform perfectly under all conditionsperfectly under all conditions- hard time under glint condition, heavy smoke/dust, snow/ice surface, ...- hard time under glint condition, heavy smoke/dust, snow/ice surface, ...

POLDER can also get into troubles due to lower resolution and limited spectral rangePOLDER can also get into troubles due to lower resolution and limited spectral range- hard time with thin cirrus, low broken clouds, snow/ice surface, ...- hard time with thin cirrus, low broken clouds, snow/ice surface, ...

Taken advantages of the combined high resolution, multispectral, multiangle and Taken advantages of the combined high resolution, multispectral, multiangle and polarisation measurement increases greatly the chance to get a correct cloud detection polarisation measurement increases greatly the chance to get a correct cloud detection (though you're still stuck in the mud when you need to settle on the definition of a cloud)(though you're still stuck in the mud when you need to settle on the definition of a cloud)

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Cloud detectionCloud detectionCloud detection can be tricky under many circumstances (heavy aerosol loading, glint, bright Cloud detection can be tricky under many circumstances (heavy aerosol loading, glint, bright surfaces : desert, snow/ice). Even worse when mixture of those ...surfaces : desert, snow/ice). Even worse when mixture of those ...

Example : Glint/DustExample : Glint/Dust

DustDust

A2005.249.1330A2005.249.1330

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Example : Glint/DustExample : Glint/Dust

GlintGlint

Conf. ClearConf. Clear

Prob. ClearProb. Clear

Prob.. CloudProb.. Cloud

Conf.. CloudConf.. Cloud

MODIS MYD35 Col. 5 MODIS MYD35 Col. 5

A2005.249.1330A2005.249.1330

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Example : Glint/Dust : Example : Glint/Dust :

Aerosol detection is easier when looking off glint which is always possible with POLDERAerosol detection is easier when looking off glint which is always possible with POLDER

heavy aerosolheavy aerosol clearclear more or less conf. cloudymore or less conf. cloudy

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Example : Smoke over land mixed with cloudsExample : Smoke over land mixed with clouds


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Example : Smoke over land Example : Smoke over land mixed with cloudsmixed with clouds


heavy aerosolheavy aerosol clearclear more or less conf. cloudymore or less conf. cloudy

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