Development of a Physics-Data Driven Surface Flux Parameterization for Flow in Complex Terrain

开发物理数据驱动的复杂地形流动表面通量参数化

• 批准号: 2336002
• 负责人: Marco Giometto
• 金额: $ 52.39万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-15 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2336002&HistoricalAwards=false
• 关键词: Development; Physics; Data; Driven; Surface

Numerical models that are used for weather forecasting and atmospheric research are extraordinarily complex, yet they still must make some generalized assumptions about atmospheric processes to be computationally efficient. The exchange of mass, energy, and momentum between the earth’s surface and atmosphere is evaluated by an overarching theory that works well for flat and homogeneous terrain, but less so for complex terrain and variable surfaces. In this project, the research team will develop and investigate a new machine-learning model to tackle the challenge of enabling accurate characterization of surface-atmosphere exchange. More than 70% of Earth’s land surface is in complex terrain, and improving on the ability of weather and climate models projections in these areas will be beneficial for weather forecasting, wildfire control, aviation, and military applications. Additionally, the project has several activities that are intended to provide students with the ability to explore the intersection between physics and machine learning.The Monin-Obukhov Similarity Theory (MOST) has served as the primary method for evaluating the exchange of mass, energy, and momentum between the Earth's surface and the atmosphere in weather forecasting and climate projection models over the past decades. However, MOST has well-known deficiencies when applied to complex terrain environments. This project will enable the development of a physics-informed neural network (PINN) model that is expected to provide more accurate estimates of area-aggregate surface fluxes and enable a more straightforward and physically-justified assimilation of sparse observations for parameter estimation. The initial step in the project is the generation of a numerical database of microscale flow in complex terrain via a suite of process-resolving Large Eddy Simulations (LES). This database will then be used to train the physics-informed neural network for surface fluxes (PINN-FLUX). The final task in the project would be an assessment of PINN-FLUX’s ability to evaluate surface fluxes in complex terrain making use of sparse in-situ observations. The assessment task will be conducted using comparisons to modeling and observations.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.

用于天气预报和大气研究的数值模式非常复杂，但它们仍然必须对大气过程进行一些概括性的假设，以提高计算效率。 地球表面和大气之间的质量、能量和动量的交换是由一个总体理论来评估的，该理论适用于平坦和均匀的地形，但不适用于复杂的地形和可变的表面。 在这个项目中，研究小组将开发和研究一种新的机器学习模型，以应对准确表征表面-大气交换的挑战。 地球70%以上的陆地表面处于复杂地形中，提高这些地区的天气和气候模型预测能力将有利于天气预报、野火控制、航空和军事应用。 此外，该项目还有几项活动，旨在为学生提供探索物理学和机器学习之间交叉点的能力。莫宁-奥布霍夫相似性理论（MOST）在过去几十年中一直是评估天气预报和气候预测模型中地球表面和大气之间质量、能量和动量交换的主要方法。 然而，MOST在应用于复杂地形环境时具有众所周知的缺陷。 该项目将能够开发一个物理信息神经网络（PINN）模型，预计该模型将提供更准确的面积汇总表面通量估计，并能够更直接和物理合理的参数估计稀疏观测同化。 该项目的第一步是通过一套过程解决大涡模拟（LES）生成复杂地形中微尺度流的数值数据库。 然后，该数据库将用于训练用于表面通量的物理信息神经网络（PINN-FLUX）。 该项目的最后一项任务是评估PINN-FLUX利用稀疏的现场观测评估复杂地形地表通量的能力。 该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。