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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers | Copyright
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 07 Nov 2018

Research article | 07 Nov 2018

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

Detecting cloud contamination in passive microwave satellite measurements over land

Samuel Favrichon1, Catherine Prigent1, Carlos Jimenez2, and Filipe Aires1 Samuel Favrichon et al.
  • 1Sorbonne Université, Observatoire de Paris, Université PSL, CNRS, LERMA, Paris, France
  • 2Estellus, Paris, France

Abstract. Multiple geophysical parameters such as land surface temperature, are estimated using Microwave (MW) remote sensed brightness temperature. It is known that clouds do not affect those measurement in the MWs as much as in Visible and Infrared (VIS/IR), but some contamination can still occur when strong cloud formation (i.e. convective towers) or precipitation are present. To limit errors associated to cloud contamination in the estimation of surface parameters, we build an index giving the confidence to have an observation clear from contamination using standalone MW brightness temperature measurements. The method developed uses a statistical neural networks model built upon the Global Precipitation Microwave Imager (GPM-GMI) observations, with cloud presence information taken from Meteosat Third Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI). This index is available over land and ocean, and is developed for multiple frequency ranges to be applicable to successive generations of MW imagers (10 to 40 GHz, 10 to 100GHz, 10 to 200GHz). The index confidence increases with the number of channels available, and performs better over the ocean as expected. In all cases, even with a reduced number of information over land, the model reaches an accuracy >70%, in detecting contaminated observations. Finally an example application of this index to eliminate grid cells unsuitable for land surface temperature estimation is shown.

Samuel Favrichon et al.
Interactive discussion
Status: open (until 02 Jan 2019)
Status: open (until 02 Jan 2019)
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Samuel Favrichon et al.
Data sets

SEVIRI CLAAS-2 Satellite Application Facility on Climate Monitoring

GPM GMI_R Common Calibrated Brightness Temperature Collocated L1C NASA

Samuel Favrichon et al.
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Publications Copernicus
Short summary
Land surface parameters (such as temperature) can be extracted from passive microwave satellite observations, with less cloud contamination than in the infrared. A cloud contamination index is proposed to detect cloud contamination for multiple frequency ranges (from 10 to 190 GHz), to be applicable to the successive generations of MW instruments. Even with a reduced number of low frequency channels over land, the index reaches an accuracy ≥ 70 %, in detecting contaminated observations.
Land surface parameters (such as temperature) can be extracted from passive microwave satellite...