Editorial Type:
Article
Article Type:
Research Article

A Land and Ocean Microwave Cloud Classification Algorithm Derived from AMSU-A and -B, Trained Using MSG-SEVIRI Infrared and Visible Observations

Filipe Aires Laboratoire de Météorologie Dynamique/IPSL/CNRS, Université de Paris VI, Jussieu, France

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Francis Marquisseau Laboratoire de Météorologie Dynamique/IPSL/CNRS, Université de Paris VI, Jussieu, France

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Catherine Prigent Laboratoire de l’Etude du Rayonnement et de la Matière en Astrophysique, CNRS, Observatoire de Paris, Paris, France

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Geneviève Sèze Laboratoire de Météorologie Dynamique/IPSL/CNRS, Université de Paris VI, Jussieu, France

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Print Publication:
01 Aug 2011
DOI:
https://doi.org/10.1175/MWR-D-10-05012.1
Page(s):
2347–2366
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Abstract

A statistical cloud classification and cloud mask algorithm is developed based on Advanced Microwave Sounding Unit (AMSU-A and -B) microwave (MW) observations. The visible and infrared data from the Meteosat Third Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) are used to train the microwave classifier. The goal of the MW algorithms is not to fully reproduce this MSG-SEVIRI cloud classification, as the MW observations do not have enough information on clouds to reach this level of precision. The objective is instead to obtain a stand-alone MW cloud mask and classification algorithm that can be used efficiently in forthcoming retrieval schemes of surface or atmospheric parameters from microwave satellite observations. This is an important tool over both ocean and land since the assimilation of the MW observations in the operational centers is independent from the other satellite observations.

Clear sky and low, medium, and opaque–high clouds can be retrieved over ocean and land at a confidence level of more than 80%. An information content analysis shows that AMSU-B provides significant information over both land and ocean, especially for the classification of medium and high clouds, whereas AMSU-A is more efficient over ocean when discriminating clear situations and low clouds.

Additional affiliation: Laboratoire de l’Etude du Rayonnement et de la Matière en Astrophysique, CNRS, Observatoire de Paris, Paris, France.

Corresponding author address: F. Aires, CNRS/IPSL/Laboratoire de Meteorologie Dynamique, Université Pierre et Marie Curie, Case 99, 4, Place Jussieu, F-75252 Paris, CEDEX 05, France. E-mail: filipe.aires@lmd.jussieu.fr

Abstract

A statistical cloud classification and cloud mask algorithm is developed based on Advanced Microwave Sounding Unit (AMSU-A and -B) microwave (MW) observations. The visible and infrared data from the Meteosat Third Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) are used to train the microwave classifier. The goal of the MW algorithms is not to fully reproduce this MSG-SEVIRI cloud classification, as the MW observations do not have enough information on clouds to reach this level of precision. The objective is instead to obtain a stand-alone MW cloud mask and classification algorithm that can be used efficiently in forthcoming retrieval schemes of surface or atmospheric parameters from microwave satellite observations. This is an important tool over both ocean and land since the assimilation of the MW observations in the operational centers is independent from the other satellite observations.

Clear sky and low, medium, and opaque–high clouds can be retrieved over ocean and land at a confidence level of more than 80%. An information content analysis shows that AMSU-B provides significant information over both land and ocean, especially for the classification of medium and high clouds, whereas AMSU-A is more efficient over ocean when discriminating clear situations and low clouds.

Additional affiliation: Laboratoire de l’Etude du Rayonnement et de la Matière en Astrophysique, CNRS, Observatoire de Paris, Paris, France.

Corresponding author address: F. Aires, CNRS/IPSL/Laboratoire de Meteorologie Dynamique, Université Pierre et Marie Curie, Case 99, 4, Place Jussieu, F-75252 Paris, CEDEX 05, France. E-mail: filipe.aires@lmd.jussieu.fr
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