Validation of cloud optical depth and cloud
fraction retrievals using Meteosat–7
Alessandro Ipe
Royal Meteorological Institute of Belgium
GERB Science Team Meeting, London, February 5 2003
Royal Meteorological Institute of Belgium
Overview
1. RMIB GERB processing
2. Cloud optical depth and cloud fraction retrievals
3. Data description
4. Results & homogenized results
5. Conclusions & perspectives
GERB Science Team Meeting, London, February 5 2003 1
Royal Meteorological Institute of Belgium
1. RMIB GERB processing
• Near–realtime & every 15 min.: delivery of TOA solar and thermal
fluxes in several spatial and temporal resolutions
• Solar TOA radiance–to–flux conversions → CERES ADMs parameterized
according to CERES Scene ID
• RMIB SEVIRI Scene ID with CERES ADM features:. cloud optical depth ◦ cloud phase
. cloud fraction ◦ surface type
=⇒ For best flux estimation, CERES and SEVIRI Scene IDs need to be as
close as possible !
GERB Science Team Meeting, London, February 5 2003 2
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2. Cloud optical depth and cloud fraction retrievals
Near–realtime constraint for SEVIRI Scene ID: global processingshould take less than 15 min
=⇒ Non–iterative cloud properties algorithms with possible correction
scheme to map on CERES values.
Cloud optical depth τ
• STREAMER code → LUT
• Innovative parameterization of LUT
Cloud fraction f based on cloud optical depth retrieval
• Cloudy pixel: τ > τthres
GERB Science Team Meeting, London, February 5 2003 3
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3. Data description
• 8 months from June to December 1998:. CERES SSF VIRS Edition 2A/VIRS–only Edition 2 (10 km at nadir)
. MS–7 visible images → CERES-like footprints (3× 3 pixels)
• Near time–simultaneous (< 5 min.) and similar viewing angles (tilt angle
< 5◦) CERES and MS–7 CERES–like footprints with:. single cloud layer
. pure cloud phase
. pure ground surfaceaccording to CERES Scene ID
• For cloud optical depth comparisons: overcast CERES footprints
GERB Science Team Meeting, London, February 5 2003 4
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4. Results – Cloud optical depth
Ocean with water clouds
0
1
2
3
4
5
Ln τ
GE
RB
0 1 2 3 4Ln τCERES
Fit
Ocean with ice clouds
0
1
2
3
4
5
0 1 2 3 4 5Ln τCERES
Fit
GERB Science Team Meeting, London, February 5 2003 5
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4. Results – Cloud optical depth
Vegetation with water clouds
0
1
2
3
4
5
Ln τ
GE
RB
0 1 2 3 4Ln τCERES
Fit
Vegetation with ice clouds
0
1
2
3
4
5
0 1 2 3 4 5Ln τCERES
Fit
GERB Science Team Meeting, London, February 5 2003 6
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4. Results – Cloud fraction
• τthres , 0.6 → same averaged cloud fraction over all footprints
• Individual comparisons are meaningless due to discrete MS–7 values
GERB Science Team Meeting, London, February 5 2003 7
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4. Homogenized results – Cloud optical depth
Correction scheme based on least square fit model
Vegetation with water clouds
0
1
2
3
4
Ln τ
GE
RB
0 1 2 3 4Ln τCERES
Vegetation with ice clouds
-1
0
1
2
3
4
5
0 1 2 3 4 5Ln τCERES
GERB Science Team Meeting, London, February 5 2003 8
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5. Conclusions
• Simple cloud optical depth and cloud fraction retrieval schemes =adequate for GERB/SEVIRI Scene ID
• Homogenization process can be used to remove functional dependency
on cloud optical depths
• Couple of issues need further work
GERB Science Team Meeting, London, February 5 2003 9
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5. Perspectives
• Extend the amount of data for comparison above desert surfaces
• Improvement when using uniform surface albedos for LUT like CERES ?
• Individual cloud fraction comparisons rather than on average values
• Repeat with SEVIRI when enough data available
GERB Science Team Meeting, London, February 5 2003 10
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GERB instrument
• GERB = Geostationary Earth Radiation Budget is a broadband
radiometer with 2 channels:
. shortwave (0.32− 4) µm . total (0.32− 30) µm
• On board of Meteosat Second Generation (MSG) launched at end of
August with SEVIRI imager
• Built by an European Consortium (UK, Belgium, Italy) leaded by
Rutherford Appleton Laboratory (RAL)
• Ground Segment Software developed by RAL (geolocation, calibration,
radiances) and RMIB (end–user/science products)
GERB Science Team Meeting, London, February 5 2003 11
Royal Meteorological Institute of Belgium
Cloud optical depth scattering for ice clouds
0
10
20
30
40
50
60
Wat
er c
loud
s [%
]
0 10 20 30 40 50 60 70 80 90 100r [µm]
Water
0
5
10
15
20
Ice
clou
ds [%
]
Ice
0.0
0.2
0.4
0.6
0.8
1.0
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1Wavelength [µm]
MS-7SEVIRI 0.6SEVIRI 0.8
GERB Science Team Meeting, London, February 5 2003 12
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5. Results – Cloud optical depth
• Criteria used to select footprints leads to small population, esp. for desert
• Ocean footprints show very good correlation between CERES and MS–7
retrievals
• Vegetation footprints show also good correlation between both retrievals
BUT ln τG = a·ln τC+b which may be coming from only one vegetation
type considered in LUT (TBC)
• Larger dispersion for ice clouds coming from sensitivity of MS–7 retrievals
with cloud particle size
GERB Science Team Meeting, London, February 5 2003 13
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