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

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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.