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To overcome this issue, an intelligent technology called \u201cEcho Reconstruction UNet (ER-UNet)\u201d is proposed in this study. It reconstructs radar composite reflectivity (CREF) using observations from Fengyun-4A geostationary satellites with broad coverage. In general, ER-UNet outperforms UNet in terms of root mean square error (RMSE), mean absolute error (MAE), structural similarity index (SSIM), probability of detection (POD), false alarm rate (FAR), critical success index (CSI), and Heidke skill score (HSS). Additionally, ER-UNet provides the better reconstruction of CREF compared to the UNet model in terms of the intensity, location, and details of radar echoes (particularly, strong echoes). ER-UNet can effectively reconstruct strong echoes and provide crucial decision-making information for early warning of severe weather.<\/jats:p>","DOI":"10.3390\/rs16020275","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T05:47:21Z","timestamp":1704865641000},"page":"275","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Intelligent Reconstruction of Radar Composite Reflectivity Based on Satellite Observations and Deep Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Jianyu","family":"Zhao","sequence":"first","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8003-7214","authenticated-orcid":false,"given":"Jinkai","family":"Tan","sequence":"additional","affiliation":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"}]},{"given":"Sheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"}]},{"given":"Qiqiao","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"given":"Liang","family":"Gao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Internet of Things for Smart City, and Department of Ocean Science and Technology, University of Macau, Macau 999078, China"}]},{"given":"Yanping","family":"Li","sequence":"additional","affiliation":[{"name":"Guangxi Meteorological Information Center, Nanning 530022, China"}]},{"given":"Chunxia","family":"Wei","sequence":"additional","affiliation":[{"name":"Guangxi Institute of Meteorological Sciences, Nanning 530022, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8320189","DOI":"10.1155\/2016\/8320189","article-title":"Improving the Predictability of Severe Convective Weather Processes by Using Wind Vectors and Potential Temperature Changes: A Case Study of a Severe Thunderstorm","volume":"2016","author":"Wang","year":"2016","journal-title":"Adv. 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