A multidisciplinary approach for weather & climate
Research lines
- 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.
- Data mining and machine learning techniques, including Bayesian and neural networks, for diagnosis/prognosis applications in meteorology and climate.
- Statistical and machine learning techniques applied to local weather forecast by adapting the prediction of numerical models using statistical relationships obtained from historical records
Application domains
- Studies of seasonal predictability over the tropics and Europe associated with potential sources of seasonal predictability such as ENSO events.
- Trend analysis in observed climate, climate change scenarios, uncertainty estimation and regional projection using statistical and dynamical techniques
