26–27 Feb 2020
RMIB
Europe/Brussels timezone

Deep learning for satellite precipitation estimation

Not scheduled
20m
Conference Room (RMIB)

Conference Room

RMIB

Ringlaan 3 B-1180 Brussels Belgium

Speaker

Arthur Moraux (RMIB,VUB)

Description

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.

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