{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T23:45:03Z","timestamp":1773877503340,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T00:00:00Z","timestamp":1618790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["61775175 ;42076195"],"award-info":[{"award-number":["61775175 ;42076195"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The evaporation duct is a weather phenomenon that often occurs in marine environments and affects the operation of shipborne radar. The most important evaluation parameter is the evaporation duct height (EDH). Forecasting the EDH and adjusting the working parameters and modes of the radar system in advance can greatly improve radar performance. Traditionally, short-term forecast methods have been used to estimate the EDH, which are characterized by low time resolution and poor forecast accuracy. In this study, a novel approach for EDH nowcasting is proposed based on the deep learning network and EDH data measured in the Yellow Sea, China. The factors that affect nowcasting were analyzed. The time resolution and forecast time were 5 min and 0\u20132 h, respectively. The results show that our proposed method has a higher forecast accuracy than traditional time series forecasting methods and confirm its feasibility and effectiveness.<\/jats:p>","DOI":"10.3390\/rs13081577","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T11:21:38Z","timestamp":1618831298000},"page":"1577","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Evaporation Duct Height Nowcasting in China\u2019s Yellow Sea Based on Deep Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3301-9299","authenticated-orcid":false,"given":"Jie","family":"Han","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"China Research Institute of Radiowave Propagation, Qingdao 266107, China"}]},{"given":"Jia-Ji","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Qing-Lin","family":"Zhu","sequence":"additional","affiliation":[{"name":"China Research Institute of Radiowave Propagation, Qingdao 266107, China"}]},{"given":"Hong-Guang","family":"Wang","sequence":"additional","affiliation":[{"name":"China Research Institute of Radiowave Propagation, Qingdao 266107, China"}]},{"given":"Yu-Feng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Beijing Institute of Applied Meteorology, Beijing 100029, China"}]},{"given":"Ming-Bo","family":"Jiang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Applied Meteorology, Beijing 100029, China"}]},{"given":"Shou-Bao","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Research Institute of Radiowave Propagation, Qingdao 266107, China"}]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Oceanographic Instrumentation, Shandong Academy of Sciences, Qilu University of Technology, Qingdao 266061, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1175\/1520-0450(1997)036<0193:ANMOTO>2.0.CO;2","article-title":"A new model of the oceanic evaporation duct","volume":"36","author":"Babin","year":"1997","journal-title":"J. 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