不確定性感知的端到端AI天氣預報：解析觀測與模式的貢獻

摘要

端到端天氣預報系統可直接從原始地球觀測資料產生具有技巧的全球格點與測站預報，取代包含資料同化在內的數值天氣預報流程，而其成本僅為後者的極小部分。此類系統為確定性系統，不發布不確定性資訊。於此，我們透過為Aardvark天氣模型的每個組成部分附加一個隨機機制，使其成為機率性模型：觀測編碼器採用經學習的、依輸入而定的雜訊，用以捕捉繼承自觀測系統的偶然不確定性；處理器採用蒙地卡羅丟棄法，用以捕捉學習動力學中的認識不確定性。由此產生的巢式系集透過全變異數定律分解，將系集離散度歸因於上述兩個來源，並以留出觀測流的方式進行交叉驗證。機率性微調顯著改善了平均預報，在各變數與預報時效上平均提升4.2%。該系集在中期預報範圍內經由ERA5完成校準（離散度-技巧比為0.98），其測站均方根誤差維持在確定性模型的2.4%以內，同時在各預報時效的連續排名機率評分上優於確定性模型，並緊追作業化的歐洲中期天氣預報中心系集。編碼器分支的作用等同於觀測驅動的不確定性。組成部分歸因的不確定性使端到端預報更具透明度，此為邁向大氣觀測驅動數位雙生的關鍵一步。

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.