{"title":"针对多变风浪条件的动态自适应朗缪尔湍流参数化方案:模型应用","authors":"","doi":"10.1016/j.ocemod.2024.102453","DOIUrl":null,"url":null,"abstract":"<div><div>Langmuir circulations and turbulence (LT) are crucial in the upper ocean mixed layer, significantly affecting the air-sea exchange of momentum, heat, and mass. The development of an appropriate LT parameterization scheme is vital for ocean modeling. This study employed the Large-eddy Simulation (LES) and the Physics-informed Neural Network (PINN) to optimize the KC04 Langmuir turbulence scheme by dynamically adjusting E<sub>6</sub> as a key parameter determined by winds and waves. The LES simulations under different wind wave states indicated the PINN-inferred values for E<sub>6</sub>. Modelling results from GOTM in OCSPapa station demonstrated that the optimized scheme outperformed the original KC04 scheme in simulating the vertical eddy diffusivity and temperature, with an ∼6.24% annual reduction in the root mean square error (RMSE) for the temperature and an ∼8.23% reduction in the RMSE during autumn. Furthermore, the optimized scheme resulted in a thicker mixed layer, reaching 4.9 m. This enhanced LT parameterization scheme exhibited the improved robustness for variable spatiotemporal resolutions, significantly improving the modeling accuracy.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A dynamically adaptive Langmuir turbulence parameterization scheme for variable wind wave conditions: Model application\",\"authors\":\"\",\"doi\":\"10.1016/j.ocemod.2024.102453\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Langmuir circulations and turbulence (LT) are crucial in the upper ocean mixed layer, significantly affecting the air-sea exchange of momentum, heat, and mass. The development of an appropriate LT parameterization scheme is vital for ocean modeling. This study employed the Large-eddy Simulation (LES) and the Physics-informed Neural Network (PINN) to optimize the KC04 Langmuir turbulence scheme by dynamically adjusting E<sub>6</sub> as a key parameter determined by winds and waves. The LES simulations under different wind wave states indicated the PINN-inferred values for E<sub>6</sub>. Modelling results from GOTM in OCSPapa station demonstrated that the optimized scheme outperformed the original KC04 scheme in simulating the vertical eddy diffusivity and temperature, with an ∼6.24% annual reduction in the root mean square error (RMSE) for the temperature and an ∼8.23% reduction in the RMSE during autumn. Furthermore, the optimized scheme resulted in a thicker mixed layer, reaching 4.9 m. This enhanced LT parameterization scheme exhibited the improved robustness for variable spatiotemporal resolutions, significantly improving the modeling accuracy.</div></div>\",\"PeriodicalId\":19457,\"journal\":{\"name\":\"Ocean Modelling\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-10-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ocean Modelling\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1463500324001392\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ocean Modelling","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1463500324001392","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
A dynamically adaptive Langmuir turbulence parameterization scheme for variable wind wave conditions: Model application
Langmuir circulations and turbulence (LT) are crucial in the upper ocean mixed layer, significantly affecting the air-sea exchange of momentum, heat, and mass. The development of an appropriate LT parameterization scheme is vital for ocean modeling. This study employed the Large-eddy Simulation (LES) and the Physics-informed Neural Network (PINN) to optimize the KC04 Langmuir turbulence scheme by dynamically adjusting E6 as a key parameter determined by winds and waves. The LES simulations under different wind wave states indicated the PINN-inferred values for E6. Modelling results from GOTM in OCSPapa station demonstrated that the optimized scheme outperformed the original KC04 scheme in simulating the vertical eddy diffusivity and temperature, with an ∼6.24% annual reduction in the root mean square error (RMSE) for the temperature and an ∼8.23% reduction in the RMSE during autumn. Furthermore, the optimized scheme resulted in a thicker mixed layer, reaching 4.9 m. This enhanced LT parameterization scheme exhibited the improved robustness for variable spatiotemporal resolutions, significantly improving the modeling accuracy.
期刊介绍:
The main objective of Ocean Modelling is to provide rapid communication between those interested in ocean modelling, whether through direct observation, or through analytical, numerical or laboratory models, and including interactions between physical and biogeochemical or biological phenomena. Because of the intimate links between ocean and atmosphere, involvement of scientists interested in influences of either medium on the other is welcome. The journal has a wide scope and includes ocean-atmosphere interaction in various forms as well as pure ocean results. In addition to primary peer-reviewed papers, the journal provides review papers, preliminary communications, and discussions.