2011
DOI: 10.1175/mwr-d-10-05012.1
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A Land and Ocean Microwave Cloud Classification Algorithm Derived from AMSU-A and -B, Trained Using MSG-SEVIRI Infrared and Visible Observations

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 precis… Show more

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Cited by 24 publications
(20 citation statements)
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“…It is not unexpected that the HIRS sensor outperforms the counterpart MW sensors in detecting ice clouds. The measurements at IR channels are known to be strongly sensitive to thin ice clouds, as reported by many authors (Aires et al 2011;Stein, Delanoë, and Hogan 2011;Kahn et al 2014). Therefore, the information content obtained from IR-based measurements can add valuable skills towards ice cloud detection.…”
Section: Resultsmentioning
confidence: 95%
“…It is not unexpected that the HIRS sensor outperforms the counterpart MW sensors in detecting ice clouds. The measurements at IR channels are known to be strongly sensitive to thin ice clouds, as reported by many authors (Aires et al 2011;Stein, Delanoë, and Hogan 2011;Kahn et al 2014). Therefore, the information content obtained from IR-based measurements can add valuable skills towards ice cloud detection.…”
Section: Resultsmentioning
confidence: 95%
“…Remote sensing under cloudy conditions would also certainly benefit from the synergy between the VIS, IR, and MW domains. First, multi‐wavelength observations will benefit the retrieval of cloud characteristics [ Aires et al , 2011a]. Second, with clouds better constrained, the atmospheric retrieval will be facilitated.…”
Section: Discussionmentioning
confidence: 99%
“…In this paper, it is not possible to list all the ways to define a rigorous distance on each one of the relationship diagnostics that have been presented: Euclidean distance can be used on the regression parameters or the sensitivity coefficients, or two weather regime frequencies can be measured using confusion matrices (e.g. Aires et al, 2011). The distance needs to be adapted to the relationship diagnostic.…”
Section: Pattern-oriented Approachesmentioning
confidence: 99%