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Artificial Neural Networks to Retrieve Land and Sea Skin Temperature from IASI

Safieddine, Sarah; Parracho, Ana Claudia; George, Maya; Aires, Filipe; Pellet, Victor; Clarisse, Lieven; Whitburn, Simon; Lezeaux, Olivier; Thépaut, Jean-Noël; Hersbach, Hans; Radnoti, Gabor; Goettsche, Frank ORCID iD icon; Martin, Maria; Doutriaux-Boucher, Marie; Coppens, Dorothée; August, Thomas; Zhou, Daniel K.; Clerbaux, Cathy


Surface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving Tskin based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI Tskin product (i.e., TANN) is evaluated against Tskin from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI TANN and the Tskin from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e., [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [−1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000122992
Veröffentlicht am 28.08.2020
DOI: 10.3390/rs12172777
Zitationen: 12
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung – Atmosphärische Spurenstoffe und Fernerkundung (IMK-ASF)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2072-4292
KITopen-ID: 1000122992
HGF-Programm 12.01.01 (POF III, LK 01) Clouds in a pertubed atmosphere
Erschienen in Remote sensing
Verlag MDPI
Band 12
Heft 17
Seiten Article: 2777
Vorab online veröffentlicht am 26.08.2020
Schlagwörter skin temperature; IASI; neural networks; entropy reduction; ERA5; EUMETSAT; SURFRAD
Nachgewiesen in Dimensions
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