不确定性感知的端到端AI天气预报：解耦观测与模型的贡献

摘要

端到端天气预报系统直接从原始地球观测生成高技能的全域格点与站点预报，取代了包括数据同化在内的数值天气预报流程，而其成本仅为后者的一小部分。这些系统是确定性的，不提供不确定性信息。在此，我们通过为Aardvark天气模型的每个组件附加一种随机机制，使其成为概率模型：在观测编码器处引入学习得到的、依赖输入的噪声，以捕获继承自观测系统的偶然不确定性；在处理器中引入蒙特卡洛丢弃法，以捕获学习动力学中的认知不确定性。由此产生的嵌套集合通过全方差定律分解将预报离散度归因于上述两个来源，并通过剔除观测流进行交叉验证。概率微调显著改善了平均预报，在变量和预报时效上的平均改进幅度为4.2%。该集合在中长期预报范围内相对ERA5具有良好校准（离散度-技能比率为0.98），其站点均方根误差保持在确定性模型的2.4%以内，同时在每个预报时效上的连续排序概率分数均优于确定性模型，但落后于业务化ECMWF集合预报。编码器分支表现为观测驱动的不确定性。分量归因的不确定性使端到端预报更加透明，这是迈向观测驱动的大气数字孪生的一步。

English

End-to-end weather forecasting systems produce skillful global gridded and station forecasts directly from raw Earth observations, replacing the numerical weather prediction pipeline, including data assimilation, at a fraction of its cost. These systems are deterministic and issue no uncertainty. Here we render the Aardvark Weather model probabilistic by attaching one stochastic mechanism to each component: learned, input-dependent noise at the observation encoder, capturing aleatoric uncertainty inherited from the observing system, and Monte Carlo dropout in the processor, capturing epistemic uncertainty in the learned dynamics. The resulting nested ensemble attributes forecast spread to the two sources through a law-of-total-variance decomposition, cross-checked by withholding observation streams. Probabilistic finetuning significantly improves the mean forecast, by 4.2% on average across variables and lead times. The ensemble is calibrated against ERA5 through the medium range (spread-skill ratio 0.98), keeps station RMSE within 2.4% of the deterministic model while beating it in CRPS at every lead time, and trails the operational ECMWF ensemble. The encoder branch behaves as observation-driven uncertainty. Component-attributed uncertainty makes end-to-end forecasts more transparent, a step toward observation-driven digital twins of the atmosphere.