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The evolution of convective systems over a very short term can be foreseen according to the extrapolated reflectivity images. Recently, deep neural networks have been widely applied to radar echo extrapolation and have achieved better forecasting performance than traditional approaches. However, it is difficult for existing methods to combine predictive flexibility with the ability to capture temporal dependencies at the same time. To leverage the advantages of the previous networks while avoiding the mentioned limitations, a 3D-UNet-LSTM model, which has an extractor-forecaster architecture, is proposed in this paper. The extractor adopts 3D-UNet to extract comprehensive spatiotemporal features from the input radar images. In the forecaster, a newly designed Seq2Seq network exploits the extracted features and uses different convolutional long short-term memory (ConvLSTM) layers to iteratively generate hidden states for different future timestamps. Finally, the hidden states are transformed into predicted radar images through a convolutional layer. We conduct 0\u20131 h convective nowcasting experiments on the public MeteoNet dataset. Quantitative evaluations demonstrate the effectiveness of the 3D-UNet extractor, the newly designed forecaster, and their combination. In addition, case studies qualitatively demonstrate that the proposed model has a better spatiotemporal modeling ability for the complex nonlinear processes of convective echoes.<\/jats:p>","DOI":"10.3390\/rs15061529","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T03:03:57Z","timestamp":1678676637000},"page":"1529","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["3D-UNet-LSTM: A Deep Learning-Based Radar Echo Extrapolation Model for Convective Nowcasting"],"prefix":"10.3390","volume":"15","author":[{"given":"Shiqing","family":"Guo","sequence":"first","affiliation":[{"name":"The College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410005, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7562-8626","authenticated-orcid":false,"given":"Nengli","family":"Sun","sequence":"additional","affiliation":[{"name":"The College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410005, China"}]},{"given":"Yanle","family":"Pei","sequence":"additional","affiliation":[{"name":"The College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410005, China"}]},{"given":"Qian","family":"Li","sequence":"additional","affiliation":[{"name":"The College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410005, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1175\/BAMS-D-11-00263.1","article-title":"Use of NWP for Nowcasting Convective Precipitation: Recent Progress and Challenges","volume":"95","author":"Sun","year":"2014","journal-title":"Bull. 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