{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T13:57:39Z","timestamp":1784555859711,"version":"3.55.0"},"reference-count":32,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,5,2]],"date-time":"2021-05-02T00:00:00Z","timestamp":1619913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key-Area Research and Development Program of Guangdong Province;Key project of monitoring, early warning and prevention of major natural disasters of China ;Scientific Research Projects of Weather Modification in Northwest China","award":["2020B1111200001;2019YFC1510304;RYSY201905"],"award-info":[{"award-number":["2020B1111200001;2019YFC1510304;RYSY201905"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD\/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5\u00b0 elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5\u00b0 elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.<\/jats:p>","DOI":"10.3390\/rs13091779","type":"journal-article","created":{"date-parts":[[2021,5,2]],"date-time":"2021-05-02T08:05:21Z","timestamp":1619942721000},"page":"1779","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm"],"prefix":"10.3390","volume":"13","author":[{"given":"Xiaoyan","family":"Yin","sequence":"first","affiliation":[{"name":"School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqun","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China"},{"name":"State Key Lab of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5836-6839","authenticated-orcid":false,"given":"Jiafeng","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boyong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Zuo","sequence":"additional","affiliation":[{"name":"School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,2]]},"reference":[{"key":"ref_1","unstructured":"Yu, X.D., Yao, X.P., Xiong, T.N., Zhou, X.G., Wu, H., Deng, B.S., and Song, Y. 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