CAREER: A Unified Multiscale Modeling Approach for Processes in the Atmospheric Boundary Layer

职业：大气边界层过程的统一多尺度建模方法

• 批准号: 2236504
• 负责人: Paola Crippa
• 金额: $ 54.1万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2028-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236504&HistoricalAwards=false
• 关键词: CAREER; Unified; Multiscale; Modeling; Approach

Weather phenomena, including wind characteristics, are the result of atmospheric processes occurring at multiple scales, spanning from planetary and regional systems that vary across thousands of kilometers to fine turbulent processes that vary every few meters. The properties of the atmosphere close to the surface vary at sub-kilometer resolution in less than one hour, thus they are challenging to characterize using existing weather models. This research will develop a unified multi-scale modeling framework to improve predictions of atmospheric properties at fine resolution that are critical to address issues of high environmental and societal relevance, such as assessment of renewable energy resources, air pollution health risks, wildfire risks and urbanization impacts on the local and regional climate. The project will also broadly impact society by promoting teaching, training and outreach activities. Specifically, it will create atmospheric science-related lesson plan kits to support education in local elementary and middle schools. The project will also develop new course material for undergraduate and graduate education in Environmental Engineering, Applied Mathematics and Statistics, and will train one postdoctoral researcher, one graduate and one undergraduate student.Existing weather models and their physical parameterizations are formulated for specific scales and assumptions. Performing simulations designed to capture very different scales (i.e., coupling meso- to micro-scale simulations) is challenging and requires substantial computing time, thus identification of the key drivers of fine scale atmospheric processes is critical to optimize the simulation design. This research aims to improve understanding of atmospheric boundary layer processes by developing new physics-based and data-driven approaches to enhance predictive and modeling capabilities across a wide range of spatio-temporal scales. Specifically, it will address the following objectives: 1) to develop a unified multi-scale framework to overcome current theoretical and modeling challenges for real case studies; 2) to explore the sensitivity of weather model output to physics schemes and model setup to identify optimal modeling practices and improve understanding of drivers of microscale flows; and 3) to develop a hybrid modeling approach integrating a machine learning model with a physics-based model to improve simulations of atmospheric flows. This proposal will also leverage data from NSF-funded field experiments designed to explore the complex dynamical interactions between geographical, terrain and synoptic conditions.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.

包括风特征在内的天气现象是多尺度大气过程的结果，从数千公里范围内变化的行星和区域系统到每隔几米变化的精细湍流过程。在不到一小时的时间内，接近地表的大气特性以亚公里分辨率变化，因此使用现有的天气模型来描述它们是具有挑战性的。这项研究将开发一个统一的多尺度建模框架，以提高对大气特性的预测精度，这对于解决高度环境和社会相关性的问题至关重要，例如评估可再生能源资源，空气污染健康风险，野火风险和城市化对当地和区域气候的影响。该项目还将通过促进教学、培训和外联活动对社会产生广泛影响。具体而言，它将创建与大气科学相关的课程计划包，以支持当地中小学的教育。该项目还将为环境工程、应用数学和统计学的本科生和研究生教育编制新的教材，并将培训一名博士后研究员、一名研究生和一名本科生，现有的天气模式及其物理参数化是为具体的尺度和假设制定的。执行旨在捕获非常不同尺度的模拟（即，将中尺度模拟与微尺度模拟相结合）是具有挑战性的，并且需要大量的计算时间，因此，识别精细尺度大气过程的关键驱动因素对于优化模拟设计至关重要。这项研究旨在通过开发新的基于物理和数据驱动的方法来提高对大气边界层过程的理解，以增强在广泛的时空尺度上的预测和建模能力。具体而言，它将解决以下目标：1）开发一个统一的多尺度框架，以克服当前真实的案例研究的理论和模拟挑战; 2）探索天气模式输出对物理方案和模式设置的敏感性，以确定最佳的模拟实践和提高对微尺度流驱动因素的理解;以及3）开发将机器学习模型与基于物理的模型相结合的混合建模方法，以改进大气流动的模拟。该提案还将利用NSF资助的实地实验数据，这些实验旨在探索地理、地形和天气条件之间复杂的动力学相互作用。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。