一般海洋湍流模式(GOTM)中的深度神经网络(KPP_DNN)增强型 K-轮廓参数化

IF 4.4 2区 地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES Journal of Advances in Modeling Earth Systems Pub Date : 2024-09-19 DOI:10.1029/2024MS004405
Jianguo Yuan, Jun-Hong Liang, Eric P. Chassignet, Olmo Zavala-Romero, Xiaoliang Wan, Meghan F. Cronin
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引用次数: 0

摘要

本研究利用深度神经网络(DNN)改进了海洋表层边界层湍流垂直混合效应的 K-Profile 参数化(KPP)。通过运行帕帕海洋站的大涡模拟模型获得的 11 年湍流解析解训练了深度神经网络,以预测 KPP 中的湍流速度尺度系数和未解析剪切系数。DNN 增强 KPP 方案(KPP_DNN)已在通用海洋湍流模型(GOTM)中实现。KPP_DNN 对于长期集成来说是稳定的,而且比带有波浪效应的现有 KPP 方案变体更有效。目前已开发并训练了三种不同的 KPP_DNN 方案,每种方案的输入和输出变量各不相同。将使用 KPP_DNN 方案的模型性能与使用传统确定性一阶和二阶闭合湍流混合参数的模型性能进行了比较。比较结果表明,当参数化中包含波浪效应时,模拟的混合层变得更冷、更深,更接近观测结果。在 KPP 框架中,用于计算海洋表面边界层深度的未解析切变的速度尺度对模拟混合层的影响比扩散率的大小更大。在 KPP_DNN 中,未解决切变不仅取决于波浪作用力,还取决于混合层深度和浮力作用力。
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The K-Profile Parameterization Augmented by Deep Neural Networks (KPP_DNN) in the General Ocean Turbulence Model (GOTM)

This study utilizes Deep Neural Networks (DNN) to improve the K-Profile Parameterization (KPP) for the vertical mixing effects in the ocean's surface boundary layer turbulence. The deep neural networks were trained using 11-year turbulence-resolving solutions, obtained by running a large eddy simulation model for Ocean Station Papa, to predict the turbulence velocity scale coefficient and unresolved shear coefficient in the KPP. The DNN-augmented KPP schemes (KPP_DNN) have been implemented in the General Ocean Turbulence Model (GOTM). The KPP_DNN is stable for long-term integration and more efficient than existing variants of KPP schemes with wave effects. Three different KPP_DNN schemes, each differing in their input and output variables, have been developed and trained. The performance of models utilizing the KPP_DNN schemes is compared to those employing traditional deterministic first-order and second-moment closure turbulent mixing parameterizations. Solution comparisons indicate that the simulated mixed layer becomes cooler and deeper when wave effects are included in parameterizations, aligning closer with observations. In the KPP framework, the velocity scale of unresolved shear, which is used to calculate ocean surface boundary layer depth, has a greater impact on the simulated mixed layer than the magnitude of diffusivity does. In the KPP_DNN, unresolved shear depends not only on wave forcing, but also on the mixed layer depth and buoyancy forcing.

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来源期刊
Journal of Advances in Modeling Earth Systems
Journal of Advances in Modeling Earth Systems METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
11.40
自引率
11.80%
发文量
241
审稿时长
>12 weeks
期刊介绍: The Journal of Advances in Modeling Earth Systems (JAMES) is committed to advancing the science of Earth systems modeling by offering high-quality scientific research through online availability and open access licensing. JAMES invites authors and readers from the international Earth systems modeling community. Open access. Articles are available free of charge for everyone with Internet access to view and download. Formal peer review. Supplemental material, such as code samples, images, and visualizations, is published at no additional charge. No additional charge for color figures. Modest page charges to cover production costs. Articles published in high-quality full text PDF, HTML, and XML. Internal and external reference linking, DOI registration, and forward linking via CrossRef.
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