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型非常复杂，但是它们仍然必须对大气过程进行一些普遍的假设，以便在计算上有效。地球表面和大气之间的质量，能量和动量的交换是通过一种总体理论来评估的，该理论适用于平坦和均匀的地形，但对于复杂的地形和可变表面而言，质量和大气的交换效果很好。在这个项目中，研究团队将开发和研究一种新的机器学习模型，以应对能够准确表征表面大气交换的挑战。地球上有70％以上的地面处于复杂的地形，并且在这些地区的天气和气候模型项目的能力提高将对天气预报，野火控制，航空和军事应用有益。此外，该项目还进行了几项活动，旨在使学生能够探索物理与机器学习之间的交集。Monin-Obukhov的相似性理论（大多数）是评估过去几十次在天气预测和气候预测模型中地球表面与大气之间的质量，能量和动量交换的主要方法。但是，当应用于复杂的地形环境时，大多数人都有众所周知的缺陷。该项目将实现物理信息的神经网络（PINN）模型的开发，该模型有望提供更准确的面积聚集表面通量的估计，并使稀疏观测的更直接和物理上的同化更为直接，可以进行参数估计。该项目的首先步骤是通过一组流程解决大型涡流模拟（LES）在复杂地形中生成微观流动的数值数据库。然后，该数据库将用于训练物理知识的神经网络以进行表面通量（PINN-FLUX）。该项目的最终任务是评估Pinn-Flux评估使用稀疏现场观察的复杂地形中表面通量的能力。评估任务将使用与建模和观察的比较进行。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估被认为是宝贵的支持。