on the development of goes-r longwave earth radiation budget products

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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 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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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 Presentation

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Page 1: On the Development of GOES-R Longwave Earth Radiation  Budget Products

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.

Page 2: On the Development of GOES-R Longwave Earth Radiation  Budget Products

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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

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

Page 14: On the Development of GOES-R Longwave Earth Radiation  Budget Products

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

Page 15: On the Development of GOES-R Longwave Earth Radiation  Budget Products

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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°

Page 17: On the Development of GOES-R Longwave Earth Radiation  Budget Products

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.

Page 18: On the Development of GOES-R Longwave Earth Radiation  Budget Products

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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