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.

今天，天气预报是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，对能源生产、农业规划和严重风暴准备等进行及时预报。模型输出的准确性随着时间的推移而逐渐增加，这是由于几个重要因素，例如更强大的计算机的可用性，以及在运行期间连续摄取实验数据。尽管如此，在一些典型的物理过程的表现方面仍然存在重大缺陷。其中之一是陆地-大气相互作用的表示，它捕获了空气与地球表面交换热量，水分和阻力的方式。传统上，这些过程已经表示使用的配方，最初开发的规范流和表面条件，这是已知的，不执行以及现实的表面配置下（如山区地形，森林，异质表面等）。尽管如此，由于缺乏更好的替代品，它仍然是所有数值天气预报和气候模型的主力。在这个研究项目中，将开发一种新的方法，促进现有的陆-气相互作用公式的扩展，以适用于所有现实的流和表面配置。这项工作的结果将代表近地面过程在数值天气预报和气候模式中的代表方式的范式变化，导致天气和气候预测准确性的下一个飞跃。这项研究有可能影响每个人，从美国农村的当地农民到计划下一个海上风电场的投资者，以及只是查看天气预报以计划即将到来的周末的普通家庭。具体来说，该项目将利用广义Monin-Obukhov相似性理论（MOST），其中包括湍流各向异性作为额外的无量纲项的度量。作为第一步，将在湍流分辨地球系统模型中实施拉格朗日平均方案，以便能够在飞行中计算湍流各向异性，目的是瞬时校正MOST标度关系。作为第二步，这个新的框架的鲁棒性将通过运行不同的大涡模拟不同的现实复杂的流配置，并将结果与现有的实验数据集进行比较进行测试。此外，这一新框架还将测试其与空间分辨率的依赖性。这里的目标是了解空间分辨率如何影响湍流各向异性的计算及其相应的效果，在校正表面附近的动量，质量和能量交换。在基于各向异性的广义Monin-Obukhov相似性理论（MOST）能够在无分辨率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的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。