{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:12:23Z","timestamp":1780675943627,"version":"3.54.1"},"reference-count":24,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T00:00:00Z","timestamp":1571788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2013decoder 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.<\/jats:p>","DOI":"10.3390\/rs11212463","type":"journal-article","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T03:20:36Z","timestamp":1571973636000},"page":"2463","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4905-5590","authenticated-orcid":false,"given":"Arthur","family":"Moraux","sequence":"first","affiliation":[{"name":"Royal Meteorological Institute of Belgium, Avenue Circulaire 3, 1180 Brussels, Belgium"},{"name":"ETRO Department, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven","family":"Dewitte","sequence":"additional","affiliation":[{"name":"Royal Meteorological Institute of Belgium, Avenue Circulaire 3, 1180 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bruno","family":"Cornelis","sequence":"additional","affiliation":[{"name":"ETRO Department, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrian","family":"Munteanu","sequence":"additional","affiliation":[{"name":"ETRO Department, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"505","DOI":"10.5194\/hess-20-505-2016","article-title":"Development and verification of a stochastic precipitation nowcasting system for urban hydrology in Belgium","volume":"20","author":"Foresti","year":"2016","journal-title":"Hydrol. 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