DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations
This work addresses subseasonal and climate prediction for meteorology and climate science, though it is incremental as it builds on existing diffusion models applied to new observational data.
The authors tackled global precipitation forecasting by developing DiffObs, an autoregressive generative diffusion model trained on satellite data, which successfully generated realistic tropical wave modes and captured key atmospheric oscillations like the Madden-Julian oscillation with correct dispersion relationships.
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 subseasonal to seasonal predictability in the observed atmosphere, and convectively coupled moist Kelvin waves with approximately correct dispersion relationships. Despite secondary issues and biases, the results affirm the potential for a next generation of global diffusion models trained on increasingly sparse, and increasingly direct and differentiated observations of the world, for practical applications in subseasonal and climate prediction.