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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 05 Jun 2019

Submitted as: research article | 05 Jun 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Atmospheric Measurement Techniques (AMT).

Skin temperature from the Thermal Infrared Sounder IASI

Sarah Safieddine1, Ana Claudia Parracho1, Maya George1, Filipe Aires2, Victor Pellet2, Lieven Clarisse3, Simon Whitburn3, Olivier Lezeaux4, Jean-Noel Thepaut5, Hans Hersbach5, Gabor Radnoti5, Frank Goettsche6, Maria Martin6, Marie Doutriaux Boucher7, Dorothee Coppens7, Thomas August7, and Cathy Clerbaux1,3 Sarah Safieddine et al.
  • 1LATMOS/IPSL, Sorbonne Université, UVSQ, CNRS, Paris, France
  • 2LERMA, Observatoire de Paris, Paris, France
  • 3Université libre de Bruxelles (ULB), Atmospheric Spectroscopy, Service de Chimie Quantique et Photophysique, Brussels, Belgium
  • 4Spascia, Toulouse
  • 5ECMWF, Shinfield Park, Reading, Berkshire, RG2 9AX, UK
  • 6Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany
  • 7European Organisation for the Exploitation of Meteorological Satellites, Darmstadt, Germany

Abstract. Skin temperature (Tskin) derived from infrared sensors on board satellites provides a continuous view of Earth’s surface day and night and allows for the monitoring of global temperature changes relevant for climate trends. Tskin from the Infrared Atmospheric Sounding Interferometer (IASI) has not been properly exploited to date to assess its long-term spatio-temporal variability and no current homogenous Tskin record from IASI exists. In this study, we present a fast retrieval method of Tskin based on an artificial neural network from a set of IASI channels selected using the information theory/entropy reduction technique. We compare and validate our IASI Tskin product with that from EUMETSAT Level 2, ECMWF Reanalysis ERA5, SEVIRI land-surface temperature products, as well as ground measurements. Our results show good correlation between the IASI neural network product and the datasets used for validation, with a standard deviation between 1 and 4 °C. This method can be applied to other infrared measurements, and allows for the construction of a robust Tskin dataset, making it suitable for trend analysis.

Sarah Safieddine et al.
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Status: final response (author comments only)
Status: final response (author comments only)
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Sarah Safieddine et al.
Sarah Safieddine et al.
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Publications Copernicus
Short summary
Skin temperature is one of the essential climate variables (ECVs), and is relevant for the current and future understanding of our climate. This work presents a method to retrieve skin temperature from the thermal infrared sounder IASI that provides a global observation of Earth’s surface and atmosphere twice a day. With this method, the first consistent long-term [2007-present] skin temperature record from IASI can be constructed.
Skin temperature is one of the essential climate variables (ECVs), and is relevant for the...