Moraux, A., Dewitte, S., Cornelis, B., & Munteanu, A. (2019). Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements. Remote Sensing, 11(21), Article 2463.

@article{84ada66fceab4461a3c87aa07d7816cb,
title = "Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements",
abstract = "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.",
keywords = "rain detection, rain rate estimation, QPE, MSG SEVIRI, rain gauge, deep learning, convolutional neural network, semantic segmentation",
author = "Arthur Moraux and Steven Dewitte and Bruno Cornelis and Adrian Munteanu",
year = "2019",
month = oct,
day = "23",
language = "English",
volume = "11",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "Torrent Valencia: Recent Advances, [2024]-",
number = "21",
}