Kavli Institute for Theoretical Physics
Lessons and Outlook for ML Parameterization of Sub Grid Atmospheric Physics From the Vantage of Emulating Cloud Superparameterization - Mike Pritchard
Exploring machine learning for atmospheric physics parameterization, focusing on cloud superparameterization emulation. Discusses challenges, strategies, and lessons learned in applying ML to climate modeling.
YouTube42 minutesSelf-pacedFree video
PCS Institute for Basic Science
Machine Learning and Resampling for Stochastic Parameterization with Memory
Explore machine learning and resampling techniques for stochastic parameterization with memory in multiscale dynamical systems, focusing on overcoming challenges in long-memory scenarios.
YouTube25 minutesSelf-pacedFree video
PCS Institute for Basic Science
Causality Detection and Multi-Scale Decomposition of the Climate System Using Machine Learning
Explore machine learning approaches for detecting causal relationships and meaningful patterns in climate systems, focusing on Reservoir Computing and Multi-Resolution Dynamic Mode Decomposition techniques.
YouTube32 minutesSelf-pacedFree video