DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations

no code implementations • 4 Apr 2024 • Jason Stock, Jaideep Pathak, Yair Cohen, Mike Pritchard, Piyush Garg, Dale Durran, Morteza Mardani, Noah Brenowitz

This work presents an autoregressive generative diffusion model (DiffObs) to predict the global evolution of daily precipitation, trained on a satellite observational product, and assessed with domain-specific diagnostics.

A Practical Probabilistic Benchmark for AI Weather Models

no code implementations • 27 Jan 2024 • Noah D. Brenowitz, Yair Cohen, Jaideep Pathak, Ankur Mahesh, Boris Bonev, Thorsten Kurth, Dale R. Durran, Peter Harrington, Michael S. Pritchard

We also reveal how multiple time-step loss functions, which many data-driven weather models have employed, are counter-productive: they improve deterministic metrics at the cost of increased dissipation, deteriorating probabilistic skill.

Weather Forecasting

Residual Diffusion Modeling for Km-scale Atmospheric Downscaling

no code implementations • 24 Sep 2023 • Morteza Mardani, Noah Brenowitz, Yair Cohen, Jaideep Pathak, Chieh-Yu Chen, Cheng-Chin Liu, Arash Vahdat, Karthik Kashinath, Jan Kautz, Mike Pritchard

Predictions of weather hazard require expensive km-scale simulations driven by coarser global inputs.