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Using Neural Networks to Improve the Performance of Radiative Transfer Modeling Used for Geometry Dependent Surface Lambertian-Equivalent Reflectivity CalculationsSurface Lambertian-equivalent reflectivity (LER) is important for trace gas retrievals in the direct calculation of cloud fractions and indirect calculation of the air mass factor. Current trace gas retrievals use climatological surface LER's. Surface properties that impact the bidirectional reflectance distribution function (BRDF) as well as varying satellite viewing geometry can be important for retrieval of trace gases. Geometry Dependent LER (GLER) captures these effects with its calculation of sun normalized radiances (I/F) and can be used in current LER algorithms (Vasilkov et al. 2016). Pixel by pixel radiative transfer calculations are computationally expensive for large datasets. Modern satellite missions such as the Tropospheric Monitoring Instrument (TROPOMI) produce very large datasets as they take measurements at much higher spatial and spectral resolutions. Look up table (LUT) interpolation improves the speed of radiative transfer calculations but complexity increases for non-linear functions. Neural networks perform fast calculations and can accurately predict both non-linear and linear functions with little effort.
Document ID
20180000636
Acquisition Source
Goddard Space Flight Center
Document Type
Presentation
Authors
Fasnacht, Zachary
(Science Systems and Applications, Inc. Lanham, MD, United States)
Qin, Wenhan
(Science Systems and Applications, Inc. Lanham, MD, United States)
Haffner, David P.
(Science Systems and Applications, Inc. Lanham, MD, United States)
Loyola, Diego
(Deutsches Zentrum fuer Luft- und Raumfahrt e.V. Wessling, Germany)
Joiner, Joanna
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Krotkov, Nickolay
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Vasilkov, Alexander
(Science Systems and Applications, Inc. Lanham, MD, United States)
Spurr, Robert
(RT Solutions, Inc. Cambridge, MA, United States)
Date Acquired
January 18, 2018
Publication Date
December 11, 2017
Subject Category
Geosciences (General)
Report/Patent Number
GSFC-E-DAA-TN50513
Meeting Information
Meeting: American Geophysical Union (AGU) Fall Meeting
Location: New Orleans, LA
Country: United States
Start Date: December 11, 2017
End Date: December 15, 2017
Sponsors: American Geophysical Union
Funding Number(s)
CONTRACT_GRANT: GSFC - 613
CONTRACT_GRANT: NNG17HP01C
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Keywords
Geometry Dependent LER (GLER) capture
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