Dan Fu

Research Interests

I am an atmospheric dynamist and numerical modeler working at the interface of weather and climate. My primary research interests focus on how modes of climate variability and climate change can influence the variability and predictability of high-impact extreme weather and climate events. Extreme weather and climate events, such as tropical cyclones, atmospheric rivers, heatwaves, and severe droughts, pose substantial risks to both humans and the environment. My research aims to improve seasonal-to-decadal predictions and future projections of these extreme events by understanding the physical drivers of their variability and changes. Given the complexity of factors influencing these events and the limited duration of high-quality observations, I employ both ensembles of high-resolution weather (e.g., WRF and MPAS) and climate (e.g., CESM) model simulations, as well as machine learning data-driven approaches, to uncover causal relationships.

• Tropical cyclone dynamics and predictability 
• Temperature and hydrological weather extremes in climate change
• High-resolution global and regional climate modeling
• Seasonal-to-decadal climate predictions
• Deep learning/AI in weather and climate applications

Opportunities

Prof. Fu is looking to recruit graduate students in weather and climate extreme events (e.g., tropical cyclones, extreme precipitation, heat waves, and severe drought) with a target start date in January/August 2025. Focused on weather and climate extreme events, our lab uses ensembles of high-resolution weather (WRF) and climate (CESM) model simulations and machine learning data-driven models to understand the effects of natural climate variability and anthropogenic climate change on extreme events and the physical processes through which they are linked.

Specific projects include:

• Weather-climate continuum;
• Extreme events in the global high-resolution (~25km atmosphere/land and ~10km ocean/sea-ice nominal resolution) climate and regional/global convection-permitting (<4km) simulations;
• Develop, design, and apply Machine Learning/AI algorithms and workflows for improving weather, seasonal-to-decadal forecasts, and climate model projections;

Interested students should contact Dr. Fu for more information.