on the development of goes-r longwave earth radiation budget products
DESCRIPTION
On the Development of GOES-R Longwave Earth Radiation Budget Products. Hai-Tien Lee (1) & Istvan Laszlo (2) (1) CICS/ESSIC-NOAA, University of Maryland (2) NOAA/NESDIS/STAR. - PowerPoint PPT PresentationTRANSCRIPT
On the Development of GOES-R Longwave Earth Radiation
Budget ProductsHai-Tien Lee(1) & Istvan Laszlo(2)
(1)CICS/ESSIC-NOAA, University of Maryland(2)NOAA/NESDIS/STAR
GOES-R 2007 Annual Meeting Lansdowne, VA, May 15-18, 2007
Acknowledgments: Nicolas Clerbaux, Steven Dewitte (RMI, Belgium), Jacqueline Russell (Imperial College, UK), GERB and SEVIRI Teams, Fred Rose & CERES Team, and NASA Langley Data Center. GOES-R Risk Reduction and AWG Projects.
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LW Earth Radiation Budget Parameters
• Top of the atmosphere Outgoing Longwave Radiation (OLR) - Physical-based multi-spectral regression model
• Surface Downward Longwave Radiation (DLW) - Physical-based multi-spectral non-linear regression model, physical-based statistical parameterization
• Surface Upward Longwave Radiation (ULW) - physical method
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Algorithm Heritage
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HIRS OLR Climate Data Record
1979 - 2003 (Lee et al., 2007)
HIRS OLR Climate Data Record in excellent agreement with broadband measurements.
HIRS OLR Validation (Ellingson et al., 1994)
HIRS OLR is Operational since 1998.
Tropical Mean
HIRS Multi-spectral OLR Algorithm
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OLR = a0(θ) + ai(θ)⋅N ii
∑ (θ) Ni = channel i radiance = local zenith angleai = regression coefficient
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SEVIRI OLR Validation
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Methods and Issues for
Measurements Collocation
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OLR Validation ReferenceCERES SSF OLR
Daytime Nighttime June 2004
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SEVIRI-CERES OLR DifferencesJune 2004
Negative Differences Positive Differences
Day
Night
Highly variable cloud, e.g., ITCZ, is a scene that the measurements were typically “mismatched” - producing both large positive and negative random errors.
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Space and Time Collocation
• Observation Time in 15 min. window• View zenith angle matched (azimuth is not!)
• Spatial Homogeneity
Homogeneity Index =Max−Min
Ave
SEVIRI radiances averaged for a 3x3-pixel target (red, 9km at nadir) collocates with a CERES footprint (yellow, 20km at nadir).
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Collocated, VZA-matched SEVIRI and CERES OLRJune 2004 Night-time
How do the observation time differences and the varying spatial homogeneity affect the validation results?
Mean OLR Diff Std OLR Diff
Homogeneity Index Bins Homogeneity Index Bins
Thre
shol
d of
Tim
e di
ff (
sec)
Thre
shol
d of
Tim
e di
ff (
sec)
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Threshold and Absolute Homogeneity
Statistics for points < thresholdStatistics for points for index bins
LimitRange
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Validation Results
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SEVIRI Model Comparison
•June 21-27 and Dec. 11-17, 2004 Meteosat8 full disk domain•CERES SSF FM1/2 (Ed2b), FM3/4 (Ed1b)•View zenith angle matched (<1°), homogeneous scenes (index<0.01), both day & night
SEVIRI OLR Model BChannels 5, 6, 7, 11
SEVIRI OLR Model AChannels 6, 9, 11
Y=-12.21+16.16*X
Y=-6.66+6.17*X
Effects of Upper Tropospheric HumiditySEVIRI OLR Limb Darkening Biases
Day/Night, Homog<0.01, vza matched, No Desert
UTH variation is important in determining LW broadband radiance angular variation.
Inclusion of 6.2 m channel (Ch5) significantly reduced the limb darkening biases.
Model A: Ch 6, 9, 11
Model B: Ch 5, 6, 7, 11
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GERB - CERES OLR
Dewitte et al., 2006
Empirical correction for limb darkening errors:
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SEVIRI-CERES
• June 21-27 and Dec. 11-17, 2004 Meteosat8 full disk domain• CERES SSF FM1/2 (Ed2b), FM3/4 (Ed1b)• View zenith angle matched (<1°), homogeneous scenes (index<0.01), both day & night
Mean Diff Std Diff
-6 -3 3 6 Wm-2 3 6 Wm-20
Model B1°x1°
Summary• Preliminary SEVIRI/ABI OLR algorithms were validated against CERES SSF data. The full-disk domain mean OLR difference is within 1%, with rms differences of about 5 Wm-2 and nearly one-to-one relationship.
• Angular dependence and Regional biases– Empirical correction possible, but not desired!
– New OLR model accounted for UTH variation, producing much better limb darkening property, but erred for desert (need to devise different regression technique)
• GERB OLR validation showed similar limb darkening problems - thin cirrus identification problems.
• Scene identification may improve regional accuracy - particularly for extreme conditions (desert) and semi-transparent cirrus scenes.
• Adaptation of CERES LW ADM is under evaluation.
END
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Validation Summary• June 21-27 and Dec. 11-17, 2004• Validated with CERES SSF FM1/2 (Ed2b), FM3/4 (Ed1b)
• For zenith angle matched, homogeneous scenes (index<0.01), day & night: SEVIRI - CERES SEVIRI/CERES
N Mean Wm-2
Std Dev Wm-2
RMS Wm-2
Ratio at 95% confidence
-1.0 3.8 4.0 0.9964±0.0001 Night 50468 -1.9 3.0 3.5 0.9931±0.0001 0.7 4.8 4.9 1.0022±0.0001 Day 48655 -1.1 4.2 4.4 0.9965±0.0001 -0.1 4.5 4.5 0.9993±0.0001 All 99123 -1.5 3.7 4.0 0.9948±0.0001
SEVIRI OLR Model B: Channels 5, 6, 7, 11
SEVIRI OLR Model A: Channels 6, 9, 11
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Spatial HomogeneityInfrared vs. Visible
VIS
IR
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