Thin-cirrus detection from Artificial Neural Network and IASI-NG
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM(2023)
Abstract
This study proposes an Artificial Neural Network approach for the detection of optically thin cirrus using observations from the Infrared Atmospheric Sounding Interferometer New Generation (IASI-NG) and from its predecessor, IASI. The Thin Cirrus Detection Algorithm applies a Feed-forward Neural Network (NN) to IASI/IASI-NG samples previously declared as clear by a cloud detection algorithm. The NN training, test and validation datasets are generated from a set of ECMWF 5-generation reanalysis (ERA5) processed with the sigma-IASI radiative transfer model to simulate IASI/IASING radiances. The IASI and IASI-NG Thin Cirrus detection algorithms were validated against an independent dataset showing better performances for the IASI-NG thin-cirrus-detection algorithm. Moreover, IASI thin-cirrus-detection algorithm outputs were compared against Cloudsat/CPR and SEVIRI cloud products, showing good probability of detection: 0.84 for SEVIRI and 0.77 for CPR/Cloudsat.
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Key words
Thin cirrus,artificial intelligence,hyperspectral data
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