Based in the Department of Atmospheric and Oceanic Science at the University of Maryland, College Park
Welcome! We focus on Predictability and Applied Research for the Earth-system with Training and Optimization (PARETO). As such, we use machine learning (e.g., neural networks) and numerical modeling systems (e.g., CESM) to answer pressing questions and address challenges in modeling the Earth system.
Forked from mariajmolina/s2sml
Software associated with the NCAR Accelerated Scientific Discovery (Derecho) project tackling subseasonal bias correction of global predictions.
Jupyter Notebook
Forked from mariajmolina/WR_Predictability
With this repository, the predictability of Weather Regimes is assessed by including different variables from ERA5 and SODA, and creating different CNN models.
Jupyter Notebook
With this repository, the predictability of Weather Regimes is assessed by including different variables from ERA5 and SODA, and creating different CNN models.
Software associated with the NCAR Accelerated Scientific Discovery (Derecho) project tackling subseasonal bias correction of global predictions.
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