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RADNET: A Neural Network-based Estimation of the Surface Radiation Budget in the Arctic from TOVS Brightness TemperaturesThis report summarizes the main accomplishments of the project. Specifics are provided in three journal papers which are enclosed with this report. Two of the journal articles are currently in press, one has already been published. Our work focused on two main areas: (1) RadNet. The main objective of the project was the development of a neural network-based method to compute downwelling shortwave and longwave fluxes directly from TOVS HIRS and MSU brightness temperatures. (2) FlaxNet. A second objective of the project involved the development of neural network-based method for the calculation of surface fluxes based on radiative transfer physics.
Document ID
20000021261
Acquisition Source
Headquarters
Document Type
Other
Authors
Schweiger, Axel
(Washington Univ. Seattle, WA United States)
Key, Jeff
(Boston Univ. Boston, MA United States)
Date Acquired
August 19, 2013
Publication Date
January 16, 1998
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Funding Number(s)
CONTRACT_GRANT: NAGw-4169
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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