Uncertainty quantification is crucial to decision-making. A prominent example is probabilistic forecasting in numerical weather prediction. The dominant approach to representing uncertainty in weather forecasting is to generate an ensemble of forecasts. This is done by running many physics-based simulations under different conditions, which is a computationally costly process. We propose to amortize the computational cost by emulating these forecasts with deep generative diffusion models learned from historical data. The learned models are highly scalable with respect to high-performance computing accelerators and can sample hundreds to tens of thousands of realistic weather forecasts at low cost. When designed to emulate operational ensemble forecasts, the generated ones are similar to physics-based ensembles in important statistical properties and predictive skill. When designed to correct biases present in the operational forecasting system, the generated ensembles show improved probabilistic forecast metrics. They are more reliable and forecast probabilities of extreme weather events more accurately. While this work demonstrates the utility of the methodology by focusing on weather forecasting, the generative artificial intelligence methodology can be extended for uncertainty quantification in climate modeling, where we believe the generation of very large ensembles of climate projections will play an increasingly important role in climate risk assessment.

翻译：不确定性量化对决策至关重要，数值天气预报中的概率预测便是一个典型例子。目前天气预报中表示不确定性的主流方法是生成预报集合，即通过在不同条件下运行多个基于物理的模拟来实现，这一过程计算成本高昂。我们提出通过利用历史数据训练的深度生成扩散模型来模拟这些预报，从而分摊计算成本。该学习模型在高性能计算加速器上具有高度可扩展性，能够以较低成本采样数百至数万个逼真的天气预报样本。当设计用于模拟业务化集合预报时，生成样本在关键统计特性和预测技能上与基于物理的集合相当；当设计用于修正业务预报系统的偏差时，生成的集合在概率预报指标上表现更优，其可靠性更高，对极端天气事件发生概率的预报也更准确。尽管本研究通过聚焦天气预报展示了该方法的应用价值，但该生成式人工智能方法论可扩展至气候建模的不确定性量化领域——我们相信，生成超大规模气候预估集合将在气候风险评估中发挥日益重要的作用。