{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T05:06:34Z","timestamp":1768971994586,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42375145"],"award-info":[{"award-number":["42375145"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023LRM-A02"],"award-info":[{"award-number":["2023LRM-A02"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["CXFZ2023J008"],"award-info":[{"award-number":["CXFZ2023J008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["CMA2022ZD04"],"award-info":[{"award-number":["CMA2022ZD04"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Grants of China Meteorological Administration Radar Meteorology Key Laboratory","award":["42375145"],"award-info":[{"award-number":["42375145"]}]},{"name":"Open Grants of China Meteorological Administration Radar Meteorology Key Laboratory","award":["2023LRM-A02"],"award-info":[{"award-number":["2023LRM-A02"]}]},{"name":"Open Grants of China Meteorological Administration Radar Meteorology Key Laboratory","award":["CXFZ2023J008"],"award-info":[{"award-number":["CXFZ2023J008"]}]},{"name":"Open Grants of China Meteorological Administration Radar Meteorology Key Laboratory","award":["CMA2022ZD04"],"award-info":[{"award-number":["CMA2022ZD04"]}]},{"name":"China Meteorological Administration Innovation and Development Program","award":["42375145"],"award-info":[{"award-number":["42375145"]}]},{"name":"China Meteorological Administration Innovation and Development Program","award":["2023LRM-A02"],"award-info":[{"award-number":["2023LRM-A02"]}]},{"name":"China Meteorological Administration Innovation and Development Program","award":["CXFZ2023J008"],"award-info":[{"award-number":["CXFZ2023J008"]}]},{"name":"China Meteorological Administration Innovation and Development Program","award":["CMA2022ZD04"],"award-info":[{"award-number":["CMA2022ZD04"]}]},{"name":"China Meteorological Administration Key Innovation Team","award":["42375145"],"award-info":[{"award-number":["42375145"]}]},{"name":"China Meteorological Administration Key Innovation Team","award":["2023LRM-A02"],"award-info":[{"award-number":["2023LRM-A02"]}]},{"name":"China Meteorological Administration Key Innovation Team","award":["CXFZ2023J008"],"award-info":[{"award-number":["CXFZ2023J008"]}]},{"name":"China Meteorological Administration Key Innovation Team","award":["CMA2022ZD04"],"award-info":[{"award-number":["CMA2022ZD04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As a spatial\u2013temporal sequence prediction task, radar echo extrapolation aims to predict radar echoes\u2019 future movement and intensity changes based on historical radar observations. Two urgent issues still need to be addressed in deep learning radar echo extrapolation models. First, the predicted radar echo sequences often exhibit echo-blurring phenomena. Second, over time, the output echo intensities from the model gradually weaken. In this paper, we propose a novel model called the MS-RadarFormer, a Transformer-based multi-scale deep learning model for radar echo extrapolation, to mitigate the two above issues. We introduce a multi-scale design in the encoder\u2013decoder structure and a Spatial\u2013Temporal Attention block to improve the precision of radar echoes and establish long-term dependencies of radar echo features. The model uses a non-autoregressive approach for echo prediction, avoiding accumulation errors during the recursive generation of future echoes. Compared to the baseline, our model shows an average improvement of 15.8% in the critical success index (CSI), an average decrease of 8.3% in the false alarm rate (FAR), and an average improvement of 16.2% in the Heidke skill score (HSS).<\/jats:p>","DOI":"10.3390\/rs16020274","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T05:47:21Z","timestamp":1704865641000},"page":"274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["The MS-RadarFormer: A Transformer-Based Multi-Scale Deep Learning Model for Radar Echo Extrapolation"],"prefix":"10.3390","volume":"16","author":[{"given":"Huantong","family":"Geng","sequence":"first","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"China Meteorological Administration Radar Meteorology Key Laboratory, Nanjing 210023, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4912-5221","authenticated-orcid":false,"given":"Fangli","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Xiaoran","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Jiangsu Meteorological Observatory, Nanjing 210008, China"}]},{"given":"Liangchao","family":"Geng","sequence":"additional","affiliation":[{"name":"School of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Boyang","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1820-5548","authenticated-orcid":false,"given":"Zhanpeng","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1016\/S0167-6105(02)00261-1","article-title":"Numerical Weather Prediction","volume":"90","author":"Kimura","year":"2002","journal-title":"J. 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