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AMT | Articles | Volume 12, issue 3
Atmos. Meas. Tech., 12, 1531–1543, 2019
https://doi.org/10.5194/amt-12-1531-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 12, 1531–1543, 2019
https://doi.org/10.5194/amt-12-1531-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 12 Mar 2019

Research article | 12 Mar 2019

Detecting cloud contamination in passive microwave satellite measurements over land

Samuel Favrichon et al.

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Cited articles

Aires, F., Prigent, C., Rossow, W. B., and Rothstein, M.: A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations, J. Geophys. Res.-Atmos., 106, 14887–14907, https://doi.org/10.1029/2001JD900085, 2001. a, b
Aires, F., Marquisseau, F., Prigent, C., and Sèze, G.: A Land and Ocean Microwave Cloud Classification Algorithm Derived from AMSU-A and -B, Trained Using MSG-SEVIRI Infrared and Visible Observations, Mon. Weather Rev., 139, 2347–2366, https://doi.org/10.1175/MWR-D-10-05012.1, 2011. a, b, c, d
Berg, W.: GPM GMI_R Common Calibrated Brightness Temperatures Collocated L1C 1.5 h 13 km V05, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), available at: https://doi.org/10.5067/GPM/GMI/R/1C/05, 2016. 
Bridle, J. S.: Probabilistic Interpretation of Feedforward Classification Network Outputs with Relationships to Statistical Pattern Recognition, NATO ASI Series in Systems and Computer Science, 227–236, https://doi.org/10.1007/978-3-642-76153-9_28, 1989. a
Buehler, S. A., Kuvatov, M., Sreerekha, T. R., John, V. O., Rydberg, B., Eriksson, P., and Notholt, J.: A cloud filtering method for microwave upper tropospheric humidity measurements, Atmos. Chem. Phys., 7, 5531–5542, https://doi.org/10.5194/acp-7-5531-2007, 2007. a, b
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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 of ≥ 70 % in detecting contaminated observations.
Land surface parameters (such as temperature) can be extracted from passive microwave satellite...
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