cloud/rain partitioning using modis and amsr-e
DESCRIPTION
Cloud/Rain partitioning using MODIS and AMSR-E. Matt Lebsock. Aqua AMSR- E & MODIS. The world is a drizzly place. Drizzle defined where 750 meter reflectivity exceeds -15 dBZ Area and Low cloud fraction weighted oceanic mean is 19.2%. Physical Basis For Cloud/Rain Separation. - PowerPoint PPT PresentationTRANSCRIPT
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Cloud/Rain partitioning using MODIS and AMSR-E
Matt Lebsock
AquaAMSR-E & MODIS
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•Drizzle defined where 750 meter reflectivity exceeds -15 dBZ•Area and Low cloud fraction weighted oceanic mean is 19.2%
The world is a drizzly place
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• The water path can be partitioned between cloud and Precipitation
• @ Visible/Near-Infrared wavelengths Qext->2
• @ Microwave frequencies– Tb are sensitive to both cloud and precipitation water
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W total =Wcloud +W precip
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τ =3Wcloud
2ρ lre,cloud+3W precip
2ρ lre,precip
Physical Basis For Cloud/Rain Separation
Small
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Spatial Resolution• MODIS
– 1km• AMSR-E
• Use Backus-Gilbert method to resample the AMSR-E footprints to a common resolution @ 23 GHz (31km x 18km)
• Average the MODIS cloud products to the 23 Ghz resolution with the antenna gain function as a weighting parameter.
FREQUENCIES (GHz) 6.925 10.65 18.7 23.8 36.5 89.0
IFOV(km x km) 74 x 43 51 x 30 27 x 16 31 x 18 14 x 8 6 x 4
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Optimal Estimation Retrieval
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Φ= y −F[ ]TSy
−1 y −F[ ] + x − xa[ ]T Sa
−1 x − xa[ ]
Parameter A priori Source Constraint
CWV ECMWF-interim 1.5 kgm-2
SST ECMWF-interim 0.25 K
WIND ECMWF-interim 2.5 ms-1
CWP MODIS 1000 gm-2
6.9 10.7 18.7 23.8 36.5 89.0V H V H V H V H V H V H
Native 0.32 0.34 0.49 0.57 0.55 0.47 0.56 0.54 0.51 0.41 1.18 0.91Re-sampled 0.52 0.55 0.48 0.56 0.27 0.23 0.56 0.54 0.14 0.11 0.19 0.15Assumptions 0.6 0.98 0.8 1.28 1.07 1.74 1.20 1.68 1.21 2.30 1.87 3.47
Observational Uncertainty
A-Priori Uncertainty
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January 2007 Results
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No Cloud Fraction
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Cloud Fraction
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Precipitation Water
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January 2007 Statistics
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Radiative Transfer SimulationsBrightness Temperatures Polarization
There does appear to be a signal in the brightness temperatures
Emission Signal Scattering Signal Depolarization Signal
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Water Vapor?
Land Influence?
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Continued Work
• Add a realistic DSD• Add rain fraction to retrieval• Include an optical depth constraint
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Φ= y −F[ ]TSy
−1 y −F[ ] + x − xa[ ]T Sa
−1 x − xa[ ] +τ − τ a[ ]
2
σ τ2
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x = CWV,SST, WIND,TWP,r[ ]
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Summary
• It appears possible to place bounds on the ratio of precipitation water to cloud water in liquid clouds.– Critical assumptions
• Precipitation DSD• Insensitivity of MODIS to precipitation
• Applications– Improved GPROF database– Studies on the control of precipitation production
• Aerosol indirect effects• Thermo-dynamical controls
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GPROF Algorithm
Radiometer ObsTMI, AMSR-E, SSM/I
Background retrievalSST, TPW
Compare Tb with a-priori database
RadiometerAppropriate
Database
Rainfall Product
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P(R |Tb ) = P(Tb |R)P(R)
Does the A-priori database containThe correct statistics of rain/no-rain
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GPROF 2008 Database Generation
Agreement ?
Add rain below sensitivity of PR&
Recombine and Compute Tb
TRMMRadiometer Obs
Rain?Background Retrieval
SST, Wind, TPW, CLW
Radar Rain Profile
Combine & Compute Tb
TRMMRadar Obs
No
Yes
Compare Tb to Obs
YesNo
Compare Tb to Obs
Agreement ?Make Database
Modify DSD in PR pixels w/o PIA information&
Recombine and Compute Tb
Yes
Compare Tb to Obs
Make Database
No Yes
Make Database
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PR-CloudSat Matchups
10%
50%
Berg et al., 2010
7 Wm-2