{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T11:23:32Z","timestamp":1768562612451,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T00:00:00Z","timestamp":1669248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2019YFE0110100"],"award-info":[{"award-number":["2019YFE0110100"]}]},{"name":"National Key Research and Development Program of China","award":["2018YFC1506905"],"award-info":[{"award-number":["2018YFC1506905"]}]},{"name":"National Key Research and Development Program of China","award":["BK20202006"],"award-info":[{"award-number":["BK20202006"]}]},{"name":"National Key Research and Development Program of China","award":["BE2019052"],"award-info":[{"award-number":["BE2019052"]}]},{"name":"National Key Research and Development Program of China","award":["BE2017076"],"award-info":[{"award-number":["BE2017076"]}]},{"name":"Natural Science Foundation of Jiangsu Province of China","award":["2019YFE0110100"],"award-info":[{"award-number":["2019YFE0110100"]}]},{"name":"Natural Science Foundation of Jiangsu Province of China","award":["2018YFC1506905"],"award-info":[{"award-number":["2018YFC1506905"]}]},{"name":"Natural Science Foundation of Jiangsu Province of China","award":["BK20202006"],"award-info":[{"award-number":["BK20202006"]}]},{"name":"Natural Science Foundation of Jiangsu Province of China","award":["BE2019052"],"award-info":[{"award-number":["BE2019052"]}]},{"name":"Natural Science Foundation of Jiangsu Province of China","award":["BE2017076"],"award-info":[{"award-number":["BE2017076"]}]},{"name":"Zhishan Youth Scholar Program of Southeast University","award":["2019YFE0110100"],"award-info":[{"award-number":["2019YFE0110100"]}]},{"name":"Zhishan Youth Scholar Program of Southeast University","award":["2018YFC1506905"],"award-info":[{"award-number":["2018YFC1506905"]}]},{"name":"Zhishan Youth Scholar Program of Southeast University","award":["BK20202006"],"award-info":[{"award-number":["BK20202006"]}]},{"name":"Zhishan Youth Scholar Program of Southeast University","award":["BE2019052"],"award-info":[{"award-number":["BE2019052"]}]},{"name":"Zhishan Youth Scholar Program of Southeast University","award":["BE2017076"],"award-info":[{"award-number":["BE2017076"]}]},{"name":"Key R&amp;D Program of Jiangsu Province","award":["2019YFE0110100"],"award-info":[{"award-number":["2019YFE0110100"]}]},{"name":"Key R&amp;D Program of Jiangsu Province","award":["2018YFC1506905"],"award-info":[{"award-number":["2018YFC1506905"]}]},{"name":"Key R&amp;D Program of Jiangsu Province","award":["BK20202006"],"award-info":[{"award-number":["BK20202006"]}]},{"name":"Key R&amp;D Program of Jiangsu Province","award":["BE2019052"],"award-info":[{"award-number":["BE2019052"]}]},{"name":"Key R&amp;D Program of Jiangsu Province","award":["BE2017076"],"award-info":[{"award-number":["BE2017076"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Short-term rainfall prediction by radar echo map extrapolation has been a very hot area of research in recent years, which is also an area worth studying owing to its importance for precipitation disaster prevention. Existing methods have some shortcomings. In terms of image indicators, the predicted images are not clear enough and lack small-scale details, while in terms of precipitation accuracy indicators, the prediction is not accurate enough. In this paper, we proposed a two-stage model (two-stage UA-GAN) to achieve more accurate prediction echo images with more details. For the first stage, we used the Trajectory Gated Recurrent Unit (TrajGRU) model to carry out a pre-prediction, which proved to have a good ability to capture spatiotemporal movement of rain field. In the second stage, we proposed a spatiotemporal attention enhanced Generative Adversarial Networks (GAN) model with a U-Net structure and a new deep residual attention module in order to carry out the refinement and improvement of the first-stage prediction. Experimental results showed that our model outperforms the optical-flow based method Real-Time Optical Flow by Variational Methods for Echoes of Radar (ROVER), and some well-known Recurrent Neural Network (RNN)-based models (TrajGRU, PredRNN++, ConvGRU, Convolutional Long Short-Term Memory (ConvLSTM)) in terms of both image detail indexes and precipitation accuracy indexes, and is visible to the naked eye to have better accuracy and more details.<\/jats:p>","DOI":"10.3390\/rs14235948","type":"journal-article","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T03:00:13Z","timestamp":1669345213000},"page":"5948","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Two-Stage UA-GAN for Precipitation Nowcasting"],"prefix":"10.3390","volume":"14","author":[{"given":"Liujia","family":"Xu","sequence":"first","affiliation":[{"name":"School of Automation, Southeast University, Nanjing 210096, China"}]},{"given":"Dan","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Automation, Southeast University, Nanjing 210096, China"},{"name":"Key Laboratory of Measurement and Control of CSE, Ministry of Education Research Laboratory, Nanjing 210096, China"}]},{"given":"Tianbao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation, Southeast University, Nanjing 210096, China"}]},{"given":"Pengju","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, Southeast University, Nanjing 210096, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6906-2654","authenticated-orcid":false,"given":"Xunlai","family":"Chen","sequence":"additional","affiliation":[{"name":"Shenzhen Key Laboratory of Severe Weather in South China, Shenzhen Meteorological Bureau, Shenzhen 518040, China"}]},{"given":"Yinghao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation, Southeast University, Nanjing 210096, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1126\/science.1115255","article-title":"Weather Forecasting with Ensemble Methods","volume":"310","author":"Gneiting","year":"2005","journal-title":"Science"},{"key":"ref_2","unstructured":"Schmid, F., Wang, Y., and Harou, A. 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