Numerical atmospheric modeling and simulation with both simplified models (e.g. barotropic) and state-of-the-art regional (WRF) and global (CAM) models
Development and deployment of earth science applications (e.g., global and regional climate models) to run in geographically distributed data and computing environments (GRID computing).
Analysis of nonlinear spatiotemporal dynamics in the atmosphere using simplified models (Lorenz96, barotropic, etc.), including theoretical aspects of error growth, predictability and ensemble forecasting, control and synchronization.
Statistical and machine learning techniques applied to local weather forecast by adapting the prediction of numerical models using statistical relationships obtained from historical records
Trend analysis in observed climate, climate change scenarios, uncertainty estimation and regional projection using statistical and dynamical techniques