An Improved MG Model for Turbulent Mixing Parameterization in the Northwestern South China Sea
Using in situ microstructure observations from 2010 to 2018, this study assesses the applicability of turbulent mixing parameterization schemes in the northwestern South China Sea (NSCS) and improves the MG model proposed by MacKinnon and Gregg in 2003 using machine learning methods. The results sho...
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/1/46 |
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| author | Minghao Hu Lingling Xie Mingming Li Quanan Zheng Feihong Zeng Xiaotong Chen |
| author_facet | Minghao Hu Lingling Xie Mingming Li Quanan Zheng Feihong Zeng Xiaotong Chen |
| author_sort | Minghao Hu |
| collection | DOAJ |
| description | Using in situ microstructure observations from 2010 to 2018, this study assesses the applicability of turbulent mixing parameterization schemes in the northwestern South China Sea (NSCS) and improves the MG model proposed by MacKinnon and Gregg in 2003 using machine learning methods. The results show that the estimation error of the MG model is still more than one order of magnitude in the NSCS. Also, the importance of parameters obtained from machine learning indicates that the normalized depth (<i>D</i>) is one of the most relevant parameters to the turbulent kinetic energy dissipation rate <i>ε</i>. Therefore, in this study, <i>D</i> is introduced into the MG model to obtain an improved MG model (IMG). The IMG model has an average correlation (<i>r</i>) between the estimated and observed log<sub>10</sub><i>ε</i> of 0.79, which is at least 49% higher than the MG model, and an average root mean square error (RMSE) of 0.25, which is at least 42% lower than that of the MG model. The IMG model accurately estimates the multi-year turbulent mixing observed in the NSCS, including before and after tropical cyclone passages. This provides a new perspective to study the physical principles and spatial and temporal distribution of turbulent mixing. |
| format | Article |
| id | doaj-art-6ad66d2aede44bb39497dd4ef002b32d |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-6ad66d2aede44bb39497dd4ef002b32d2025-01-24T13:36:40ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011314610.3390/jmse13010046An Improved MG Model for Turbulent Mixing Parameterization in the Northwestern South China SeaMinghao Hu0Lingling Xie1Mingming Li2Quanan Zheng3Feihong Zeng4Xiaotong Chen5Laboratory of Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, ChinaLaboratory of Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, ChinaLaboratory of Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, ChinaDepartment of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USALaboratory of Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, ChinaLaboratory of Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, ChinaUsing in situ microstructure observations from 2010 to 2018, this study assesses the applicability of turbulent mixing parameterization schemes in the northwestern South China Sea (NSCS) and improves the MG model proposed by MacKinnon and Gregg in 2003 using machine learning methods. The results show that the estimation error of the MG model is still more than one order of magnitude in the NSCS. Also, the importance of parameters obtained from machine learning indicates that the normalized depth (<i>D</i>) is one of the most relevant parameters to the turbulent kinetic energy dissipation rate <i>ε</i>. Therefore, in this study, <i>D</i> is introduced into the MG model to obtain an improved MG model (IMG). The IMG model has an average correlation (<i>r</i>) between the estimated and observed log<sub>10</sub><i>ε</i> of 0.79, which is at least 49% higher than the MG model, and an average root mean square error (RMSE) of 0.25, which is at least 42% lower than that of the MG model. The IMG model accurately estimates the multi-year turbulent mixing observed in the NSCS, including before and after tropical cyclone passages. This provides a new perspective to study the physical principles and spatial and temporal distribution of turbulent mixing.https://www.mdpi.com/2077-1312/13/1/46turbulent mixing parameterizationMG modelnorthwestern South China Seamachine learning |
| spellingShingle | Minghao Hu Lingling Xie Mingming Li Quanan Zheng Feihong Zeng Xiaotong Chen An Improved MG Model for Turbulent Mixing Parameterization in the Northwestern South China Sea Journal of Marine Science and Engineering turbulent mixing parameterization MG model northwestern South China Sea machine learning |
| title | An Improved MG Model for Turbulent Mixing Parameterization in the Northwestern South China Sea |
| title_full | An Improved MG Model for Turbulent Mixing Parameterization in the Northwestern South China Sea |
| title_fullStr | An Improved MG Model for Turbulent Mixing Parameterization in the Northwestern South China Sea |
| title_full_unstemmed | An Improved MG Model for Turbulent Mixing Parameterization in the Northwestern South China Sea |
| title_short | An Improved MG Model for Turbulent Mixing Parameterization in the Northwestern South China Sea |
| title_sort | improved mg model for turbulent mixing parameterization in the northwestern south china sea |
| topic | turbulent mixing parameterization MG model northwestern South China Sea machine learning |
| url | https://www.mdpi.com/2077-1312/13/1/46 |
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