Advanced Data Assimilation for Terrain-Driven and Stratified Boundary Layers

Numerical weather modeling and forecasting in mountainous terrain is made difficult by sharp changes in topography that result in small-scale wind flows and changes in the structure of temperature above the surface. This project intends to improve numerical modeling in these areas of complex terrain through advances in data assimilation, a mathematical technique that is used to mesh numerical models and real-time observations to produce a best estimate of current atmospheric conditions. The research team has a broad goal of improving forecasts of hazards in mountainous regions that are relevant to aviation safety, wildfire management, air-quality forecasting, and ground transportation. The project also involves workforce development at the intersection of boundary-layer meteorology and data assimilation. Terrain geometry, stratification, and turbulence structure fundamentally shape error growth and information propagation in modeling systems, yet these physical constraints remain largely absent from existing data assimilation (DA) formulations. To address these limitations, the research team plans to develop next-generation, physics-aware assimilation strategies tailored to stratified, terrain-driven boundary layers. The project will make use of the NSF NCAR Data Assimilation Research Testbed (DART) coupled with the Advanced Research version of the Weather Research and Forecasting (WRF) model, and ensemble-based data assimilation using the Ensemble Adjustment Kalman Filter (EAKF) and Quantile-Conserving Ensemble Filter Framework (QCEFF) to enable systematic comparison of Gaussian and weakly non-Gaussian formulations under identical flow conditions. The research team will also leverage particle-filter concepts and emerging AI methods as diagnostic and exploratory tools to assess the limits of Gaussian DA in stratified and terrain-driven boundary layers. The framework will be tested using observations from two recent field campaigns, the Cold Fog Amongst Complex Terrain (CFACT) and Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) experiments. 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. NSF Award ID: 2611710 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Zhaoxia Pu | Institution: University of Utah, SALT LAKE CITY, UT | Award Amount: $692,890 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2611710 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2611710.html