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.

天气现象（包括风特征）是多个尺度上发生的大气过程的结果，跨越了行星和区域系统，这些系统在数千公里到数千公里到每隔几米变化的细胞湍流过程而变化。接近表面的大气的性质在不到一小时的时间内以次公公里分辨率而变化，因此，使用现有天气模型来表征它们具有挑战性。这项研究将开发一个统一的多尺度建模框架，以精细分辨率提高大气特性的预测，这对于解决高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，野火风险以及对本地和区域气候的城市化影响。该项目还将通过促进教学，培训和推广活动来广泛影响社会。具体而言，它将创建与大气相关的课程计划套件，以支持当地小学和中学的教育。该项目还将开发新的课程材料，用于环境工程，应用数学和统计学的本科和研究生教育，并将培训一名博士后研究员，一名研究生和一名本科生。陈列着天气模型及其物理参数化是针对特定量表和假设的。执行旨在捕获非常不同的量表的模拟（即，将中量与微尺度模拟耦合）具有挑战性，需要大量的计算时间，因此鉴定精细规模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增强广泛时空尺度的预测和建模能力，从而提高对大气边界层过程的理解。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，以克服实际案例研究的当前理论和建模挑战； 2）探索天气模型对物理方案和模型设置的敏感性，以识别最佳的建模实践并提高对微观流动驱动因素的了解； 3）开发一种混合建模方法，将机器学习模型与基于物理学的模型集成在一起，以改善大气流的模拟。该提案还将利用NSF资助的现场实验的数据，旨在探索地理，地形和概要条件之间的复杂动力学相互作用。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的评估来评估的。