{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T19:55:08Z","timestamp":1766087708702,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T00:00:00Z","timestamp":1623024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of China under Grants","award":["41975031"],"award-info":[{"award-number":["41975031"]}]},{"name":"National Key R&amp;D Program of China under Grants","award":["2017YFB0502803"],"award-info":[{"award-number":["2017YFB0502803"]}]},{"name":"the Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies","award":["2020B1212060025"],"award-info":[{"award-number":["2020B1212060025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ground-based weather radar data plays an essential role in monitoring severe convective weather. The detection of such weather systems in time is critical for saving people\u2019s lives and property. However, the limited spatial coverage of radars over the ocean and mountainous regions greatly limits their effective application. In this study, we propose a novel framework of a deep learning-based model to retrieve the radar composite reflectivity factor (RCRF) maps from the Fengyun-4A new-generation geostationary satellite data. The suggested framework consists of three main processes, i.e., satellite and radar data preprocessing, the deep learning-based regression model for retrieving the RCRF maps, as well as the testing and validation of the model. In addition, three typical cases are also analyzed and studied, including a cluster of rapidly developing convective cells, a Northeast China cold vortex, and the Super Typhoon Haishen. Compared with the high-quality precipitation rate products from the integrated Multi-satellite Retrievals for Global Precipitation Measurement, it is found that the retrieved RCRF maps are in good agreement with the precipitation pattern. The statistical results show that retrieved RCRF maps have an R-square of 0.88-0.96, a mean absolute error of 0.3-0.6 dBZ, and a root-mean-square error of 1.2-2.4 dBZ.<\/jats:p>","DOI":"10.3390\/rs13112229","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T22:23:00Z","timestamp":1623104580000},"page":"2229","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Deep Learning-Based Radar Composite Reflectivity Factor Estimations from Fengyun-4A Geostationary Satellite Observations"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1812-141X","authenticated-orcid":false,"given":"Fenglin","family":"Sun","sequence":"first","affiliation":[{"name":"Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites (LRCVES\/CMA), National Satellite Meteorological Center, China Meteorological Administration (NSMC\/CMA), Beijing 100081, China"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites (LRCVES\/CMA), National Satellite Meteorological Center, China Meteorological Administration (NSMC\/CMA), Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1519-5069","authenticated-orcid":false,"given":"Min","family":"Min","sequence":"additional","affiliation":[{"name":"Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China"}]},{"given":"Danyu","family":"Qin","sequence":"additional","affiliation":[{"name":"Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites (LRCVES\/CMA), National Satellite Meteorological Center, China Meteorological Administration (NSMC\/CMA), Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1097\/01.qco.0000244044.85393.9e","article-title":"Infectious diseases of severe weather-related and flood-related natural disasters","volume":"19","author":"Ivers","year":"2006","journal-title":"Curr. 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