发表机构
Fraunhofer Heinrich-Hertz Institute; Technische Universität Berlin; BIFOLD - Berlin Institute for the Foundations of Learning and Data（弗劳恩霍夫海因里希·赫兹研究所; 柏林工业大学; 柏林学习与数据基础研究所（BIFOLD））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究为Aardvark天气模型添加随机机制实现概率预报，解耦观测与模型的不确定性贡献，经实验验证其预报精度提升4.2%，校准效果良好，推动了大气数字孪生的发展。
AI 中文摘要
端到端天气预报系统可直接从原始地球观测数据生成高精度的全球格点和站点预报，替代了包含数据同化的数值天气预报流程，成本仅为其一小部分。这些系统是确定性的，不输出任何不确定性信息。本研究通过为Aardvark天气模型的每个组件附加一种随机机制，使其具备概率属性：在观测编码器处添加学习到的、依赖输入的噪声，以捕捉观测系统带来的偶然不确定性；在处理器中采用蒙特卡洛dropout，以捕捉学习到的动力学过程中的认知不确定性。由此得到的嵌套集成系统通过总方差分解定律，将预报的离散度归因于上述两个来源，并通过 withheld 观测流进行交叉验证。概率微调显著提升了平均预报效果，在各变量和预报时效上平均提升4.2%。该集成系统在中期范围内针对ERA5进行了校准（离散度-技能比为0.98），其站点均方根误差（RMSE）保持在确定性模型的2.4%以内，且在所有预报时效上的连续排名概率得分（CRPS）均优于确定性模型，仅落后于业务化的ECMWF集成系统。编码器分支表现为观测驱动的不确定性。组件归因的不确定性使端到端预报更具透明度，是构建观测驱动的大气数字孪生的一步。
英文摘要
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.
Comments17 pages, 7 figures
