Evaporation Duct Height Nowcasting in China’s Yellow Sea Based on Deep Learning
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/8/1577 |
| _version_ | 1797537188030709760 |
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| author | Jie Han Jia-Ji Wu Qing-Lin Zhu Hong-Guang Wang Yu-Feng Zhou Ming-Bo Jiang Shou-Bao Zhang Bo Wang |
| author_facet | Jie Han Jia-Ji Wu Qing-Lin Zhu Hong-Guang Wang Yu-Feng Zhou Ming-Bo Jiang Shou-Bao Zhang Bo Wang |
| author_sort | Jie Han |
| collection | DOAJ |
| description | 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 0–2 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. |
| first_indexed | 2024-03-10T12:11:30Z |
| format | Article |
| id | doaj.art-63df683d826a4f118323be8e52412b8e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T12:11:30Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-63df683d826a4f118323be8e52412b8e2023-11-21T16:10:32ZengMDPI AGRemote Sensing2072-42922021-04-01138157710.3390/rs13081577Evaporation Duct Height Nowcasting in China’s Yellow Sea Based on Deep LearningJie Han0Jia-Ji Wu1Qing-Lin Zhu2Hong-Guang Wang3Yu-Feng Zhou4Ming-Bo Jiang5Shou-Bao Zhang6Bo Wang7School of Electronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Electronic Engineering, Xidian University, Xi’an 710071, ChinaChina Research Institute of Radiowave Propagation, Qingdao 266107, ChinaChina Research Institute of Radiowave Propagation, Qingdao 266107, ChinaBeijing Institute of Applied Meteorology, Beijing 100029, ChinaBeijing Institute of Applied Meteorology, Beijing 100029, ChinaChina Research Institute of Radiowave Propagation, Qingdao 266107, ChinaInstitute of Oceanographic Instrumentation, Shandong Academy of Sciences, Qilu University of Technology, Qingdao 266061, ChinaThe 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 0–2 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.https://www.mdpi.com/2072-4292/13/8/1577evaporation duct heightnowcastingdeep learningYellow Sea |
| spellingShingle | Jie Han Jia-Ji Wu Qing-Lin Zhu Hong-Guang Wang Yu-Feng Zhou Ming-Bo Jiang Shou-Bao Zhang Bo Wang Evaporation Duct Height Nowcasting in China’s Yellow Sea Based on Deep Learning Remote Sensing evaporation duct height nowcasting deep learning Yellow Sea |
| title | Evaporation Duct Height Nowcasting in China’s Yellow Sea Based on Deep Learning |
| title_full | Evaporation Duct Height Nowcasting in China’s Yellow Sea Based on Deep Learning |
| title_fullStr | Evaporation Duct Height Nowcasting in China’s Yellow Sea Based on Deep Learning |
| title_full_unstemmed | Evaporation Duct Height Nowcasting in China’s Yellow Sea Based on Deep Learning |
| title_short | Evaporation Duct Height Nowcasting in China’s Yellow Sea Based on Deep Learning |
| title_sort | evaporation duct height nowcasting in china s yellow sea based on deep learning |
| topic | evaporation duct height nowcasting deep learning Yellow Sea |
| url | https://www.mdpi.com/2072-4292/13/8/1577 |
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