{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:17:38Z","timestamp":1760149058253,"version":"build-2065373602"},"reference-count":64,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:00:00Z","timestamp":1687910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFA0606002","61010101221"],"award-info":[{"award-number":["2018YFA0606002","61010101221"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fund Program of State Key Laboratory of Hydroscience and Engineering","award":["2018YFA0606002","61010101221"],"award-info":[{"award-number":["2018YFA0606002","61010101221"]}]},{"name":"Colorado State University","award":["2018YFA0606002","61010101221"],"award-info":[{"award-number":["2018YFA0606002","61010101221"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Weather radar plays an important role in accurate weather monitoring and modern weather forecasting, as it can provide timely and refined weather forecasts for the public and for decision makers. Deep learning has been applied in radar nowcasting tasks and has exhibited a better performance than traditional radar echo extrapolation methods. However, current deep learning-based radar nowcasting models are found to suffer from a spatial \u201cblurry\u201d effect that can be attributed to a deficiency in spatial variability representation. This study proposes a Spatial Variability Representation Enhancement (SVRE) loss function and an effective nowcasting model, named the Attentional Generative Adversarial Network (AGAN), to alleviate this blurry effect by enhancing the spatial variability representation of radar nowcasting. An ablation experiment and a comparison experiment were implemented to assess the effect of the generative adversarial (GA) training strategy and the SVRE loss, as well as to compare the performance of the AGAN and SVRE loss function with the current advanced radar nowcasting models. The performances of the models were validated on the whole test set and inspected in two storm cases. The results showed that both the GA strategy and SVRE loss function could alleviate the blurry effect by enhancing the spatial variability representation, which helps the AGAN to achieve better nowcasting performance than the other competitor models. Our study provides a feasible solution for high-precision radar nowcasting applications.<\/jats:p>","DOI":"10.3390\/rs15133306","type":"journal-article","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T01:15:47Z","timestamp":1688001347000},"page":"3306","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Enhancing Spatial Variability Representation of Radar Nowcasting with Generative Adversarial Networks"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6830-3946","authenticated-orcid":false,"given":"Aofan","family":"Gong","sequence":"first","affiliation":[{"name":"Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0199-9069","authenticated-orcid":false,"given":"Ruidong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3409-5568","authenticated-orcid":false,"given":"Baoxiang","family":"Pan","sequence":"additional","affiliation":[{"name":"Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9795-3064","authenticated-orcid":false,"given":"Haonan","family":"Chen","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA"}]},{"given":"Guangheng","family":"Ni","sequence":"additional","affiliation":[{"name":"Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China"}]},{"given":"Mingxuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,28]]},"reference":[{"key":"ref_1","unstructured":"WMO (2017). 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