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RETRIEVING RAIN RATES FROM SPACE BORNE MICROWAVE SENSORS USING U-NETS

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1
Nicolas Viltard
Pierre Lepetit
  • Function : Author
  • PersonId : 1039833
Cécile Mallet
Laurent Barthès
  • Function : Author
  • PersonId : 968696
Audrey Martini
  • Function : Author
  • PersonId : 946557

Abstract

Despite a lot of progress over the last decades, rain retrieval from spaceborne measurement has been a challenge since the first launch of a passive microwave radiometers on one of the NOAA Defense Meteorological satellites in the 70s. Deep-learning and convolutional U-Nets might be able to offer a breakthrough on the topic because they do take into account the topology of both the rain field and the measured brightness temperatures. The present paper offers the very first results on the application of such artificial neural networks on the rain retrieval problem.
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insu-02894942 , version 1 (09-07-2020)

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Nicolas Viltard, Pierre Lepetit, Cécile Mallet, Laurent Barthès, Audrey Martini. RETRIEVING RAIN RATES FROM SPACE BORNE MICROWAVE SENSORS USING U-NETS. Climate Informatics 2020. 10th International Conference, Sep 2020, Oxford, United Kingdom. ⟨insu-02894942⟩
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