Collaborative Proposal: Harnessing simulation data to characterize transition layer mixing rates and mechanisms

协作提案：利用模拟数据来表征过渡层混合速率和机制

• 批准号: 2123204
• 负责人: Alexis Kaminski
• 金额: $ 36.61万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2123204&HistoricalAwards=false
• 关键词: Collaborative; Proposal; Harnessing; simulation; data

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This project will examine entrainment processes in the ocean transition layer (TL), the vertical region between the lower part of the turbulent near-surface mixed layer and the stably stratified ocean interior. The project will use a novel combination of approaches, including further analysis of existing high-resolution profile data through the upper ocean layers, high-resolution modeling of entrainment and internal wave processes in the transition layer, and machine learning techniques. These processes help determine ocean mixed layer depth and temperature and ultimately mediate air-sea exchange effects on the global ocean, yet they remain poorly understood or represented in numerical model parameterizations. Outcomes will be directly relevant to improving physical and biogeochemical ocean models. Additionally, the methods developed will be applicable to interpreting and analyzing data from a variety of geophysical flows, and the analysis scripts will be made publicly available. The work will support an early career investigator, the training of a graduate student, contributions to local outreach and educational programs, and will form the basis for a project for a graduate student in the WHOI summer program in Geophysical Fluid Dynamics. A suite of high-resolution direct numerical simulations will be generated covering a range of expected TL mechanisms, including Kelvin-Helmholtz and Holmboe instabilities and interfacial waves. Using standard fluid dynamical analyses, including characterization of the linear instabilities and a detailed analysis of the flow energetics, this comprehensive library of flow fields will be used to determine how well a given stratified mixing event can be characterized from limited measurements. The simulations will then be used as training data for a neural network-based flow classification method, allowing for input profiles of temperature and velocity to be classified in terms of the underlying waves or instabilities. After the classification method is validated using simulation data, it will be applied to the observations, allowing for identification of the relevant mechanisms driving entrainment in the TL. Knowledge of the mixing associated with each mechanism can thus be used to describe the mixing efficiency and turbulent fluxes in the observational record.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项是根据2021年《美国救援计划法》的全部或部分资助的（公共法117-2）。该项目将检查海洋过渡层（TL）的夹带过程，这是动荡的下部之间的垂直区域近地面混合层和稳定的分层海洋内部。该项目将使用新颖的方法组合，包括通过上海层对现有高分辨率概况数据的进一步分析，过渡层中夹带和内部波过程的高分辨率建模以及机器学习技术。这些过程有助于确定海洋混合层深度和温度，并最终介导对全球海洋的空气海交换影响，但它们在数值模型参数中仍然知之甚少或表示。结果将与改善物理和生物地球化学海洋模型直接相关。此外，开发的方法将适用于解释和分析来自各种地球物理流的数据，并且将公开提供分析脚本。这项工作将支持早期的职业调查员，研究生的培训，对当地外展和教育计划的贡献，并将为WHOI夏季地球物理动力学的研究生的项目奠定基础。将生成一套高分辨率直接数值模拟的套件，其中涵盖了一系列预期的TL机制，包括开尔文 - 赫尔姆霍尔茨和霍尔姆博的不稳定性和界面波。使用标准流体动力学分析，包括对线性不稳定性的表征和流动能量的详细分析，将使用该全面的流场库来确定从有限的测量值中可以表征给定的分层混合事件的表现。然后，该模拟将用作基于神经网络的流动分类方法的训练数据，从而使温度和速度的输入曲线可以根据基本波或不稳定性进行分类。使用仿真数据验证了分类方法后，将应用于观测值，从而识别驱动TL中夹带的相关机制。因此，可以使用与每种机制相关的混合的知识来描述观测记录中的混合效率和动荡的通量。此奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛的影响评估标准来通过评估来支持的。 。