Exploring Physical and Machine Learning Approaches for Stochastic Modeling and Ensemble Prediction of Weather and Climate

This course explores physical and Machine Learning approaches for stochastic modeling and ensemble prediction of weather and climate. The learning outcomes include understanding how big data and Machine Learning algorithms can enhance climate system understanding, enabling climate scientists to ask causal questions and create new theories. The course aims to exchange tools and ideas, identify key problems for collaborative efforts, and summarize current understanding in the field. The intended audience includes experts across earth system and computational sciences involved in climate change research. The teaching method involves presentations and discussions at the Machine Learning for Climate KITP conference.