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NASA NTRS · 20200009562

TPSAS-NF1676L-27237-DND

Bijoy Vengasseril Thampi·Takmeng Wong·Constantine Lukashin
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Abstract

Objective - It is observed that 5.6% of all CERES Terra/Aqua data contains missing cloud cover information or insufficient imager data for a reliable scene identification (some times it can reach up to 50% of data for a specific scene type) - The unavailability of imager data lead to gaps in global radiation budget dataset. - In this study, our objective is to develop a Machine learning methodology for the improved determination of CERES scene type and subsequent clear-sky TOA flux estimation using standalone CERES TOA radiance measurements (without any MODIS/Imager data).

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Bijoy Vengasseril Thampi, Takmeng Wong, Constantine Lukashin. TPSAS-NF1676L-27237-DND. https://ntrs.nasa.gov/citations/20200009562

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