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SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval

Timely processing of observations from multi-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to rea...

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Autores principales: Masiello, Guido, Serio, Carmine, Venafra, Sara, Poutier, Laurent, Göttsche, Frank-M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479871/
https://www.ncbi.nlm.nih.gov/pubmed/30934876
http://dx.doi.org/10.3390/s19071532
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author Masiello, Guido
Serio, Carmine
Venafra, Sara
Poutier, Laurent
Göttsche, Frank-M.
author_facet Masiello, Guido
Serio, Carmine
Venafra, Sara
Poutier, Laurent
Göttsche, Frank-M.
author_sort Masiello, Guido
collection PubMed
description Timely processing of observations from multi-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to real time processing of infrared radiances. The new radiative transfer model improves computational time by a factor of ≈7 compared to the previous versions and makes it possible to process SEVIRI data at nearly real time. The new forward model has been applied for the retrieval of surface parameters. Although the scheme can be applied for the simultaneous retrieval of temperature and emissivity, the paper mostly focuses on emissivity. The inverse scheme relies on a Kalman filter approach, which allows us to exploit a sequential processing of SEVIRI observations. Based on the new forward model, the paper also presents a validation retrieval performed with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. Furthermore, a comparison with IASI (Infrared Atmospheric Sounder Interferometer) emissivity retrievals has been performed as well. It has been found that the retrieved emissivities are in good agreement with each other and with in situ observations, i.e., average differences are generally well below 0.01.
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spelling pubmed-64798712019-04-29 SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval Masiello, Guido Serio, Carmine Venafra, Sara Poutier, Laurent Göttsche, Frank-M. Sensors (Basel) Article Timely processing of observations from multi-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to real time processing of infrared radiances. The new radiative transfer model improves computational time by a factor of ≈7 compared to the previous versions and makes it possible to process SEVIRI data at nearly real time. The new forward model has been applied for the retrieval of surface parameters. Although the scheme can be applied for the simultaneous retrieval of temperature and emissivity, the paper mostly focuses on emissivity. The inverse scheme relies on a Kalman filter approach, which allows us to exploit a sequential processing of SEVIRI observations. Based on the new forward model, the paper also presents a validation retrieval performed with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. Furthermore, a comparison with IASI (Infrared Atmospheric Sounder Interferometer) emissivity retrievals has been performed as well. It has been found that the retrieved emissivities are in good agreement with each other and with in situ observations, i.e., average differences are generally well below 0.01. MDPI 2019-03-29 /pmc/articles/PMC6479871/ /pubmed/30934876 http://dx.doi.org/10.3390/s19071532 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Masiello, Guido
Serio, Carmine
Venafra, Sara
Poutier, Laurent
Göttsche, Frank-M.
SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval
title SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval
title_full SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval
title_fullStr SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval
title_full_unstemmed SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval
title_short SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval
title_sort seviri hyper-fast forward model with application to emissivity retrieval
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479871/
https://www.ncbi.nlm.nih.gov/pubmed/30934876
http://dx.doi.org/10.3390/s19071532
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