SEEDS：使用擴散模型模擬天氣預報集成

SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models

June 24, 2023

作者: Lizao Li, Rob Carver, Ignacio Lopez-Gomez, Fei Sha, John Anderson

cs.AI

摘要

在未來天氣的不確定性下，機率預測對於決策至關重要。主要方法是使用一組預測來表示和量化操作性數值天氣預報中的不確定性。然而，生成預測組合具有計算成本高的特點。本文提出通過利用最新的生成式人工智慧技術，以規模化生成集合預測。我們的方法從包含5個成員的GEFS重新預報數據集中學習數據驅動的概率擴散模型。然後可以高效地對模型進行抽樣，以產生逼真的天氣預報，條件是操作性GEFS預報系統的幾個成員。生成的集合具有與完整GEFS 31個成員集合相似的預測技能，根據對ERA5再分析的評估，並且很好地模擬了基於大型物理的集合的統計數據。我們還將相同方法應用於開發用於生成後處理的擴散模型：該模型直接學習通過在訓練期間利用再分析數據作為標籤來糾正模擬預報系統中存在的偏差。來自這種生成後處理模型的集合表現出更高的可靠性和準確性，特別是在極端事件分類方面。一般來說，它們比GEFS操作集合更可靠，更準確地預測極端天氣的概率。我們的模型以不到操作性GEFS系統計算成本的1/10達到這些結果。

English

Probabilistic forecasting is crucial to decision-making under uncertainty about future weather. The dominant approach is to use an ensemble of forecasts to represent and quantify uncertainty in operational numerical weather prediction. However, generating ensembles is computationally costly. In this paper, we propose to generate ensemble forecasts at scale by leveraging recent advances in generative artificial intelligence. Our approach learns a data-driven probabilistic diffusion model from the 5-member ensemble GEFS reforecast dataset. The model can then be sampled efficiently to produce realistic weather forecasts, conditioned on a few members of the operational GEFS forecasting system. The generated ensembles have similar predictive skill as the full GEFS 31-member ensemble, evaluated against ERA5 reanalysis, and emulate well the statistics of large physics-based ensembles. We also apply the same methodology to developing a diffusion model for generative post-processing: the model directly learns to correct biases present in the emulated forecasting system by leveraging reanalysis data as labels during training. Ensembles from this generative post-processing model show greater reliability and accuracy, particularly in extreme event classification. In general, they are more reliable and forecast the probability of extreme weather more accurately than the GEFS operational ensemble. Our models achieve these results at less than 1/10th of the computational cost incurred by the operational GEFS system.