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Publication
Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements
Journal Contribution - Journal Article
This paper proposes a multimodal and multi-task deep-learning model for instantaneous precipitation rate estimation. Using both thermal infrared satellite radiometer and automatic rain gauge measurements as input, our encoder–decoder convolutional neural network performs a multiscale analysis of these two modalities to estimate simultaneously the rainfall probability and the precipitation rate value. Precipitating pixels are detected with a Probability Of Detection (POD) of 0.75 and a False Alarm Ratio (FAR) of 0.3. Instantaneous precipitation rate is estimated with a Root Mean Squared Error (RMSE) of 1.6 mm/h.
Journal: Remote Sensing
ISSN: 2072-4292
Issue: 21
Volume: 11
Publication year:2019
Keywords:rain detection, rain rate estimation, QPE, MSG SEVIRI, rain gauge, deep learning, convolutional neural network, semantic segmentation