Climate-Driven Shifts in Wildfire Dynamics and Transboundary Impacts
Climate change is altering atmospheric dynamics across multiple scales, from large-scale circulation patterns like jet stream meandering and persistent high-pressure ridges to local, terrain-driven wind regimes in mountainous and coastal regions. Together, these shifts intensify extreme fire weather, accelerate fire spread into the wildland-urban interface and urban areas, and drive long-range smoke transport across regional and international borders. In our lab, we use high-resolution regional climate, mesoscale, and atmospheric chemistry models (such as WRF and WRF-Chem) alongside reanalysis and satellite datasets to investigate the physical links between multi-scale atmospheric dynamics, fire behavior, and downwind environmental impacts. The goal of this project is to quantify how shifting circulation and complex terrain flows amplify fire risks while tracking their transboundary consequences on air quality, infrastructure, and public health.
Students will work directly with multi-dimensional climate and atmospheric model outputs alongside observational datasets, including satellite active fire detections, aerosol optical depth records, topographic/land-use layers, and ground-based air quality monitoring networks. Depending on their experience level and interests, students can contribute across several tracks:
- Multi-Scale Circulation & Complex Terrain Fire Weather: Processing gridded model and reanalysis output to identify atmospheric regimes linked to severe wildfire seasons and rapid fire spread.
- Wildland-Urban Interface & Transboundary Smoke Tracking: Combining Lagrangian trajectory modeling, high-resolution topography/urban land-cover data, satellite aerosol products, and surface particulate matter records to trace how fire plumes impact urban centers and cross jurisdictional boundaries.
- Statistical & Machine Learning Risk Modeling: Developing statistical/ machine learning models to quantify climate-change trends in extreme fire weather, evaluate terrain-driven wind hazards, and predict regional smoke exposure risks.
Students will be paired with PhD and postdoctoral researchers, as well as a faculty mentor, to scope a self-contained sub-project matching their coding and analytical background.
You will gain hands-on experience working with massive, real-world climate, mesoscale weather, and atmospheric chemistry datasets to tackle one of the most urgent environmental challenges of our time: how multi-scale weather patterns turn wildfires into urban hazards and transboundary air quality crises.
Our group (the Atmospheric Modeling Group) works at the intersection of atmospheric physics, environmental engineering, high-performance computing, and applied machine learning. Because we maintain an active suite of regional climate, complex-terrain, and air quality simulations, students can jump straight into analysis without waiting weeks to generate data. Beyond wildfire dynamics/risks, and transboundary smoke transport, ongoing research in our lab includes extreme precipitation and flood risk assessment, coupled meso-to-microscale wind energy modeling, and urban aerosol dynamics, offering a collaborative environment where computational methods are applied to high-impact environmental challenges.