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.

本研究提出了一种自回归生成扩散模型（DiffObs），用于预测全球日降水的演变，该模型基于卫星观测产品进行训练，并通过领域特定的诊断进行评估。该模型被训练用于概率性地预测一天前的降水。尽管存在次要问题和偏见，结果证实了在实际的亚季节和气候预测应用中，通过对日益稀疏，直接和差异化的观测进行训练的全球扩散模型的潜力。

DiffObs: 卫星观测全球预测的生成式扩散