High-Frequency Surface Wave Radar Current Measurement Corrections via Machine Learning and Towed Acoustic Doppler Current Profiler Integration
This paper proposes an algorithm based on the long short-term memory (LSTM) network to improve the quality of high-frequency surface wave radar current measurements. In order to address the limitations of traditional high-frequency radar inversion algorithms, which solely rely on electromagnetic inv...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/5/2105 |
| _version_ | 1797264758099935232 |
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| author | Zhaomin Xiong Chunlei Wei Fan Yang Langfeng Zhu Rongyong Huang Jun Wei |
| author_facet | Zhaomin Xiong Chunlei Wei Fan Yang Langfeng Zhu Rongyong Huang Jun Wei |
| author_sort | Zhaomin Xiong |
| collection | DOAJ |
| description | This paper proposes an algorithm based on the long short-term memory (LSTM) network to improve the quality of high-frequency surface wave radar current measurements. In order to address the limitations of traditional high-frequency radar inversion algorithms, which solely rely on electromagnetic inversion and disregard physical oceanography, this study incorporates a bottom-mounted acoustic Doppler current profiler (ADCP) and towed ADCP into LSTM training. Additionally, wind and tidal oceanography data were included as inputs. This study compared high-frequency radar current data before and after calibration. The results indicated that both towed and bottom-mounted ADCP enhanced the quality of HF radar monitoring data. By comparing two methods of calibrating radar, we found that less towed ADCP data input is required for the same high-frequency radar data calibration effect. Furthermore, towed ADCP has a significant advantage in calibrating high-frequency radar data due to its low cost and wide calibration range. However, as the location of the calibrated high-frequency radar data moves further away from the towing position, the calibration error also increases. This article conducted sensitivity studies on the times and different positions of using towed ADCP to calibrate high-frequency radar data, providing reference for the optimal towing path and towing time for future corrections of high-frequency radar data. |
| first_indexed | 2024-04-25T00:33:59Z |
| format | Article |
| id | doaj.art-08c61cdcfed5451781e0d19dd1c9fb62 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-25T00:33:59Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-08c61cdcfed5451781e0d19dd1c9fb622024-03-12T16:40:08ZengMDPI AGApplied Sciences2076-34172024-03-01145210510.3390/app14052105High-Frequency Surface Wave Radar Current Measurement Corrections via Machine Learning and Towed Acoustic Doppler Current Profiler IntegrationZhaomin Xiong0Chunlei Wei1Fan Yang2Langfeng Zhu3Rongyong Huang4Jun Wei5Southern Marine Science and Engineering Guangdong Laboratory, School of Resources, Environment and Materials, Guangxi University, Nanning 530004, ChinaZhuhai Marine Environmental Monitoring Central Station of the State Oceanic Administration, Zhuhai 519000, ChinaZhuhai Marine Environmental Monitoring Central Station of the State Oceanic Administration, Zhuhai 519000, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, ChinaGuangxi Laboratory on the Study of Coral Reefs in the South China Sea, Coral Reef Research Center of China, School of Marine Sciences, Guangxi University, Nanning 530004, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, ChinaThis paper proposes an algorithm based on the long short-term memory (LSTM) network to improve the quality of high-frequency surface wave radar current measurements. In order to address the limitations of traditional high-frequency radar inversion algorithms, which solely rely on electromagnetic inversion and disregard physical oceanography, this study incorporates a bottom-mounted acoustic Doppler current profiler (ADCP) and towed ADCP into LSTM training. Additionally, wind and tidal oceanography data were included as inputs. This study compared high-frequency radar current data before and after calibration. The results indicated that both towed and bottom-mounted ADCP enhanced the quality of HF radar monitoring data. By comparing two methods of calibrating radar, we found that less towed ADCP data input is required for the same high-frequency radar data calibration effect. Furthermore, towed ADCP has a significant advantage in calibrating high-frequency radar data due to its low cost and wide calibration range. However, as the location of the calibrated high-frequency radar data moves further away from the towing position, the calibration error also increases. This article conducted sensitivity studies on the times and different positions of using towed ADCP to calibrate high-frequency radar data, providing reference for the optimal towing path and towing time for future corrections of high-frequency radar data.https://www.mdpi.com/2076-3417/14/5/2105Pearl River EstuaryLSTMHF radarbottom-mounted ADCPtowed ADCPmachine learning |
| spellingShingle | Zhaomin Xiong Chunlei Wei Fan Yang Langfeng Zhu Rongyong Huang Jun Wei High-Frequency Surface Wave Radar Current Measurement Corrections via Machine Learning and Towed Acoustic Doppler Current Profiler Integration Applied Sciences Pearl River Estuary LSTM HF radar bottom-mounted ADCP towed ADCP machine learning |
| title | High-Frequency Surface Wave Radar Current Measurement Corrections via Machine Learning and Towed Acoustic Doppler Current Profiler Integration |
| title_full | High-Frequency Surface Wave Radar Current Measurement Corrections via Machine Learning and Towed Acoustic Doppler Current Profiler Integration |
| title_fullStr | High-Frequency Surface Wave Radar Current Measurement Corrections via Machine Learning and Towed Acoustic Doppler Current Profiler Integration |
| title_full_unstemmed | High-Frequency Surface Wave Radar Current Measurement Corrections via Machine Learning and Towed Acoustic Doppler Current Profiler Integration |
| title_short | High-Frequency Surface Wave Radar Current Measurement Corrections via Machine Learning and Towed Acoustic Doppler Current Profiler Integration |
| title_sort | high frequency surface wave radar current measurement corrections via machine learning and towed acoustic doppler current profiler integration |
| topic | Pearl River Estuary LSTM HF radar bottom-mounted ADCP towed ADCP machine learning |
| url | https://www.mdpi.com/2076-3417/14/5/2105 |
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