EAGER: Generalizing Monin-Obukhov Similarity Theory (MOST)-based Surface Layer Parameterizations for Turbulence Resolving Earth System Models (ESMs)

EAGER：将基于 Monin-Obukhov 相似理论 (MOST) 的表面层参数化推广到湍流解析地球系统模型 (ESM)

• 批准号: 2414424
• 负责人: Marc Calaf
• 金额: $ 23.64万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-15 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2414424&HistoricalAwards=false
• 关键词: EAGER; Generalizing; Monin; Obukhov; Similarity

Today, weather forecasts are an integral part of our daily lives and a pillar of the world’s economy and safety through, for example, timely forecasts for energy production, agricultural planning, and severe storm readiness, to name a few. The accuracy of the model outputs has progressively increased with time as a result of several important factors, such as the availability of more powerful computers, and the continuous ingestion of experimental data during runtime. Nonetheless, there remain significant weaknesses in the representation of some of the characteristic physical processes. One of these is the representation of land-atmosphere interactions, which capture the way the air exchanges heat, moisture, and drag with the Earth’s surface. Traditionally, these processes have been represented using a formulation that was originally developed for canonical flow and surface conditions, and which is known to not perform well under realistic surface configurations (e.g. mountainous terrain, over forests, heterogeneous surfaces, etc.). Nonetheless it remains the workhorse in all numerical weather prediction and climate models given the lack of better alternatives. In this research project a new approach will be developed that facilitate the extension of the existing land-atmosphere interaction formulation to be applicable to all realistic flow and surface configurations. Results from this work will represent a paradigm change in the way near surface processes are represented in numerical weather prediction and climate models, leading to the next leap forward in weather and climate prediction accuracy. This research has the potential to impact everyone, from the local farmer in rural U.S.A, to investors planning for the next offshore wind farm, as well as the regular family that is just checking the weather forecast to plan for the upcoming weekend.Specifically, this project will leverage the generalized Monin-Obukhov Similarity Theory (MOST) that includes the metric of turbulence anisotropy as an additional non-dimensional term. As the first step, a Lagrangian averaging scheme will be implemented in a turbulence resolving Earth System Model such that it is possible to compute turbulence anisotropy on the fly, with the goal of instantaneously correcting MOST scaling relations. As the second step, the robustness of this new framework will be tested by running different Large-Eddy Simulations of different realistic complex flow configurations and comparing the results with existing experimental datasets. In addition, this new framework will also be tested with respect to its dependence with spatial resolution. The goal here is to understand how spatial resolution affects the computation of turbulence anisotropy and its corresponding effect in correcting the momentum, mass, and energy exchanges near the surface. This project represents a very much needed first step, before the anisotropy-based generalized Monin-Obukhov Similarity Theory (MOST) can be implemented in non-resolving turbulence Earth System Models.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.

如今，天气预报员已成为我们日常生活中不可或缺的一部分，也是世界经济和安全的支柱，例如及时的能源生产，农业规划和严重的风暴准备就绪的预报员，仅举几例。由于几个重要因素，例如更强大的计算机的可用性以及在运行时的连续摄入实验数据，模型输出的准确性随时间逐渐增加。尽管如此，在某些特征物理过程的表示中仍然存在重大弱点。其中之一是陆地大气相互作用的代表，它捕获了空气与地球表面交换热，水分和拖动的方式。传统上，这些过程是使用最初用于规范流和表面条件开发的公式来表示的，并且已知在逼真的表面构型（例如山地，森林，异构表面等）下表现不佳。但是，由于缺乏更好的替代方案，在所有数值天气预测和气候模型中，它仍然是主力。在该研究项目中，将开发一种新方法，以促进现有的大气相互作用公式的扩展，以适用于所有现实的流动和表面配置。这项工作的结果将代表在数值的天气预测和气候模型中代表近表面过程的范式变化，从而导致下一个叶子在天气和气候预测准确性中前进。这项研究有可能影响所有人，从美国的本地农民到为下一个海上风电场计划的投资者，以及正规的家庭，他们只是在检查天气预报以计划即将到来的周末计划。具体来说，该项目将利用广义的蒙宁 - 奥布科夫相似性理论（大多数），其中包括其他非湍流式非策略，包括其他持久性的持久性。作为第一步，将在解决地球系统模型的湍流中实现Lagrangian平均方案，以便可以即时计算湍流各向异性，目的是立即纠正大多数缩放关系。作为第二步，将通过运行不同逼真的复杂流程配置的不同大涡模拟并将结果与​​现有实验数据集进行比较来测试该新框架的鲁棒性。此外，该新框架还将根据其依赖性和空间分辨率进行测试。这里的目的是了解空间分辨率如何影响湍流各向异性的计算及其在纠正表面附近的动量，质量和能量交换方面的相应效果。在基于各向异性的广义蒙宁 - 奥布科霍夫相似性理论（大多数）可以在非解决的湍流地球系统模型中实施之前，该项目代表了非常需要的第一步。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力和更广泛影响的评估来审查Criteria来通过评估来通过评估来获得支持的。