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Article

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

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LATMOS/IPSL, Sorbonne Université, UVSQ, CNRS, 75005 Paris, France
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LERMA, Observatoire de Paris, 75014 Paris, France
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Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES), Université Libre de Bruxelles, 1050 Brussels, Belgium
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Spascia, 31520 Ramonville St Agne, France
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ECMWF, Shinfield Park, Reading, Berkshire RG2 9AX, UK
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Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
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European Organisation for the Exploitation of Meteorological Satellites, D-64295 Darmstadt, Germany
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NASA Langley Research Center, Hampton, VA 23666, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(17), 2777; https://doi.org/10.3390/rs12172777
Received: 5 August 2020 / Revised: 18 August 2020 / Accepted: 21 August 2020 / Published: 26 August 2020
(This article belongs to the Special Issue Artificial Intelligence for Weather and Climate)
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. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust Tskin dataset suitable for Tskin long-term spatio-temporal variability and trend analysis. View Full-Text
Keywords: skin temperature; IASI; neural networks; entropy reduction; ERA5; EUMETSAT; SURFRAD skin temperature; IASI; neural networks; entropy reduction; ERA5; EUMETSAT; SURFRAD
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MDPI and ACS Style

Safieddine, S.; Parracho, A.C.; George, M.; Aires, F.; Pellet, V.; Clarisse, L.; Whitburn, S.; Lezeaux, O.; Thépaut, J.-N.; Hersbach, H.; Radnoti, G.; Goettsche, F.; Martin, M.; Doutriaux-Boucher, M.; Coppens, D.; August, T.; Zhou, D.K.; Clerbaux, C. Artificial Neural Networks to Retrieve Land and Sea Skin Temperature from IASI. Remote Sens. 2020, 12, 2777. https://doi.org/10.3390/rs12172777

AMA Style

Safieddine S, Parracho AC, George M, Aires F, Pellet V, Clarisse L, Whitburn S, Lezeaux O, Thépaut J-N, Hersbach H, Radnoti G, Goettsche F, Martin M, Doutriaux-Boucher M, Coppens D, August T, Zhou DK, Clerbaux C. Artificial Neural Networks to Retrieve Land and Sea Skin Temperature from IASI. Remote Sensing. 2020; 12(17):2777. https://doi.org/10.3390/rs12172777

Chicago/Turabian Style

Safieddine, Sarah, Ana C. Parracho, Maya George, Filipe Aires, Victor Pellet, Lieven Clarisse, Simon Whitburn, Olivier Lezeaux, Jean-Noël Thépaut, Hans Hersbach, Gabor Radnoti, Frank Goettsche, Maria Martin, Marie Doutriaux-Boucher, Dorothée Coppens, Thomas August, Daniel K. Zhou, and Cathy Clerbaux. 2020. "Artificial Neural Networks to Retrieve Land and Sea Skin Temperature from IASI" Remote Sensing 12, no. 17: 2777. https://doi.org/10.3390/rs12172777

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