Research
Research Focus
My research sits at the intersection of atmospheric and planetary science, environmental sensing, computational sciences. I am particularly interested in combining observations, physics-based models, and machine learning to understand and predict complex planetary environments.
My current research centers on three areas:
AI for Atmospheric Science and Environmental Sensing
I investigate how machine-learning models can be used alongside physics-based atmospheric models for characterization of atmospheric processes and weather prediction.
A major focus of my work is understanding whether AI weather models reproduce the underlying atmospheric dynamics rather than simply achieving low forecast error. This includes idealized dynamical tests, perturbation experiments to evaluate physical consistency and forecast errors.
Complex Interactions within the Earth System
I work on numerical weather prediction and coupled environmental modeling, particularly atmosphere-surface interactions.
My research includes NOAA’s Unified Forecast System, Great Lakes lake-atmosphere coupling, regional forecasting, precipitation processes, and the use of environmental observations to improve predictive models.
Origin and Evolution of Planetary Atmospheres
I study the structure, composition, and evolution of planetary atmospheres using spacecraft observations, and computational models.
My work has included Jupiter’s deep atmosphere and auroral environment using NASA Juno observations, Martian atmospheric remote sensing, planetary reanalysis datasets, and the interaction between planetary atmospheres and their space environments.