{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T16:19:09Z","timestamp":1762273149001,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T00:00:00Z","timestamp":1695168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Laoshan Laboratory science and technology innovation projects","award":["LSKJ202201202","ZDYF2023SHFZ089","202212016","122CXTD519"],"award-info":[{"award-number":["LSKJ202201202","ZDYF2023SHFZ089","202212016","122CXTD519"]}]},{"name":"Hainan Key Research and Development Program","award":["LSKJ202201202","ZDYF2023SHFZ089","202212016","122CXTD519"],"award-info":[{"award-number":["LSKJ202201202","ZDYF2023SHFZ089","202212016","122CXTD519"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["LSKJ202201202","ZDYF2023SHFZ089","202212016","122CXTD519"],"award-info":[{"award-number":["LSKJ202201202","ZDYF2023SHFZ089","202212016","122CXTD519"]}]},{"name":"Hainan Provincial Natural Science Foundation of China","award":["LSKJ202201202","ZDYF2023SHFZ089","202212016","122CXTD519"],"award-info":[{"award-number":["LSKJ202201202","ZDYF2023SHFZ089","202212016","122CXTD519"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Severe convective weather is hugely destructive, causing significant loss of life and social and economic infrastructure. Based on the U-Net network with the attention mechanism, the recurrent convolution, and the residual module, a new model is proposed named ARRU-Net (Attention Recurrent Residual U-Net) for the recognition of severe convective clouds using the cloud image prediction sequence from FY-4A data. The characteristic parameters used to recognize severe convective clouds in this study were brightness temperature values TBB9, brightness temperature difference values TBB9\u2212TBB12 and TBB12\u2212TBB13, and texture features based on spectral characteristics. This method first input five satellite cloud images with a time interval of 30 min into the ARRU-Net model and predicted five satellite cloud images for the next 2.5 h. Then, severe convective clouds were segmented based on the predicted image sequence. The root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and correlation coefficient (R2) of the predicted results were 5.48 K, 35.52 dB, and 0.92, respectively. The results of the experiments showed that the average recognition accuracy and recall of the ARRU-Net model in the next five moments on the test set were 97.62% and 83.34%, respectively.<\/jats:p>","DOI":"10.3390\/rs15184612","type":"journal-article","created":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T01:32:50Z","timestamp":1695173570000},"page":"4612","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Recognition of Severe Convective Cloud Based on the Cloud Image Prediction Sequence from FY-4A"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0439-8173","authenticated-orcid":false,"given":"Qi","family":"Chen","sequence":"first","affiliation":[{"name":"Faculty of Information Science and Engineering, College of Marine Technology, Ocean University of China, Qingdao 266100, China"}]},{"given":"Xiaobin","family":"Yin","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Engineering, College of Marine Technology, Ocean University of China, Qingdao 266100, China"},{"name":"Laoshan Laboratory, Qingdao 266237, China"},{"name":"Sanya Oceanographic Institution, Ocean University of China, Sanya 572024, China"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Engineering, College of Marine Technology, Ocean University of China, Qingdao 266100, China"}]},{"given":"Peinan","family":"Zheng","sequence":"additional","affiliation":[{"name":"PLA Unit 31016, Beijing 100081, China"}]},{"given":"Miao","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Science and Technology on Operational Oceanography, Chinese Academy of Sciences, Guangzhou 510301, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0214-744X","authenticated-orcid":false,"given":"Qing","family":"Xu","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Engineering, College of Marine Technology, Ocean University of China, Qingdao 266100, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"114","DOI":"10.30574\/gjeta.2021.6.2.0022","article-title":"Tropical Convective Cloud Growth Models for Hydrometeorological Disaster Mitigation in Indonesia","volume":"6","author":"Gernowo","year":"2021","journal-title":"Glob. 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