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DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations
April 11, 2024, 4:42 a.m. | Jason Stock, Jaideep Pathak, Yair Cohen, Mike Pritchard, Piyush Garg, Dale Durran, Morteza Mardani, Noah Brenowitz
cs.LG updates on arXiv.org arxiv.org
Abstract: 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. The model is trained to probabilistically forecast day-ahead precipitation. Nonetheless, it is stable for multi-month rollouts, which reveal a qualitatively realistic superposition of convectively coupled wave modes in the tropics. Cross-spectral analysis confirms successful generation of low frequency variations associated with the Madden--Julian oscillation, which regulates most …
abstract arxiv autoregressive cs.cv cs.lg daily diagnostics diffusion diffusion model domain evolution forecast forecasting generative global physics.ao-ph physics.comp-ph precipitation product satellite stat.ml type work
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