We have also expanded the Neurips award-winning ClimSim dataset work with an end-to-end, containerized workflow, enabling any ML researcher to easily develop and evaluate their own hybrid climate simulators.

We built a machine learning parameterization for subgrid atmospheric processes and achieved stable and skillful hybrid physics-ML simulations with near operational-level complexity.