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

Dive into a comprehensive conference talk exploring the intersection of physical and machine learning approaches for stochastic modeling and ensemble prediction in weather and climate science. Examine how recent advancements in theoretical understanding, coupled with exponential growth in observational and modeling data, are reshaping climate research. Discover the potential of big data and machine learning algorithms in providing unprecedented insights into climate systems. Investigate the challenges of informing society about future regional and local climate changes, and learn how data-driven methods can address complex, multi-scale processes. Explore the opportunities for descriptive inference, causal questioning, and theory validation in climate science. Gain insights into collaborative efforts aimed at solving key problems in climate modeling and prediction. Understand the broader implications of this interdisciplinary approach, which brings together experts from earth system and computational sciences to tackle the climate change problem.

Exploring physical & Machine Learning approaches for stochastic modeling and... ▸ Aneesh Subramanian