文献类型：期刊文献

英文题名：An Improved Mg Model for Turbulent Mixing Parameterization in the Northern South China Sea

作者：Hu, Minghao[1]; Xie, Lingling[1,2,3]; Li, Mingming[1,2,3]; Zheng, Quanan[4]; Zeng, Feihong[1]; Chen, Xiaotong[1]; Guan, Zhuoqiang[1]; Liu, Simeng[1]; Wang, Yulin[1]

机构：[1] Laboratory of Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang, 524088, China; [2] Key Laboratory of Climate, Resources and Environments in Continent Shelf Sea and Deep Ocean, Zhanjiang, 524088, China; [3] National Satellite Ocean Application Service, Ministry of Natural Resources, Beijing, 100081, China; [4] Department of Atmospheric and Oceanic Science, University of Maryland, College Park, 20742, United States

年份：2024

外文期刊名：SSRN

收录：EI(收录号：20240297206)

语种：英文

外文关键词：Energy dissipation - Machine learning - Mean square error - Mixing - Shock tubes - Storms - Turbulent flow

外文摘要：Using in-situ microstructure observations from 2010 to 2018, this study assesses the applicability of turbulent mixing parameterization schemes and improves the MG model with machine learning method in the northern South China Sea (NSCS) . The results indicate that in NSCS, the estimation error of the MG model still exceeds one order of magnitude. Meanwhile, the key parameter Ε0 in the MG model is difficult to be determined. The parameter importance derived from machine learning shows that the normalized depth (D) is one of the most relevant parameters to turbulence energy dissipation rate Ε. Therefore, this study introduced D into the MG model and obtained an improved MG model (IMG). The mean correlation (r) between the estimated and observed dissipation rates of the IMG model is 0.79, which is at least 39% higher than the MG model, and the mean root mean square errors (RMSE) is 0.25, which is at least 36% lower than the MG model. The IMG models can accurately estimate the multi-year turbulent mixing observed in NSCS, including mixing before and after the tropical cyclone passages. This provides a new perspective for studying the physical principle and space-time distribution of turbulent mixing. ? 2024, The Authors. All rights reserved.