Validation of cloud optical depth and cloud

fraction retrievals using Meteosat–7

Alessandro Ipe

Royal Meteorological Institute of Belgium

gerb@oma.be

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

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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 !

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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

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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

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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

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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

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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

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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

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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)

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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

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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