SEEDS：使用扩散模型模拟天气预报集合

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

概率预测对于在未来天气方面做出决策至关重要，特别是在不确定性下。主导方法是利用一组预测来表示和量化操作性数值天气预测中的不确定性。然而，生成这些预测集合在计算上是昂贵的。在本文中，我们提出通过利用生成人工智能领域的最新进展，在规模上生成集合预测。我们的方法从包含5个成员的GEFS再预测数据集中学习数据驱动的概率扩散模型。然后可以高效地对模型进行抽样，以生成真实的天气预测，条件是操作性GEFS预测系统的几个成员。生成的集合在对ERA5再分析进行评估时具有与完整GEFS 31成员集合相似的预测技能，并且很好地模拟了大型基于物理的集合的统计数据。我们还将相同方法应用于开发扩散模型以进行生成后处理：该模型直接学习通过在训练期间利用再分析数据作为标签来纠正模拟预测系统中存在的偏差。从这种生成后处理模型中产生的集合显示出更高的可靠性和准确性，特别是在极端事件分类方面。总的来说，它们比GEFS操作性集合更可靠，并更准确地预测极端天气的概率。我们的模型以不到操作性GEFS系统计算成本的1/10实现了这些结果。

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.