发表机构
Vrije Universiteit Amsterdam; University of Amsterdam; Delft University of Technology; Centrum Wiskunde & Informatica（阿姆斯特丹自由大学; 阿姆斯特丹大学; 代尔夫特理工大学; 数学与计算机科学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将确定性基础模型Aurora扩展为生成式集合预测模型Xaurora，通过去噪随机插值法结合回放缓冲区训练SDE轨迹，实现高效概率预报，在13分钟内生成有技巧的15天预报，并接近最先进水平。
AI 中文摘要
深度学习近年来彻底改变了天气预报领域，尤其是通过大气基础模型，这些模型以经典基于物理模型的一小部分计算成本提供了具有竞争力的技能。然而，大多数现有的基础模型是确定性的，限制了生成大规模集合以进行准确的不确定性量化、极端天气风险评估和长期天气预报的能力。此外，这些模型从头训练会带来巨大且往往令人望而却步的计算开销。为了解决这些不足，我们将预训练的确定性先验模型，即Aurora基础模型，转变为生成式集合预测模型。为此，我们引入了一种新颖的生成方法，即去噪随机插值法，并结合用于随机微分方程（SDE）推演的回放缓冲区，实现了SDE轨迹的概率性训练。我们的随机基础模型Xaurora，从Aurora的小型版本进行微调，却在全球集合指标上接近最先进水平，并且与Aurora的大型版本具有竞争力。我们的方法在参数和样本方面效率高，能在13分钟内生成有技巧的15天预报。我们的结果表明，确定性基础模型可以被高效地扩展为更强大的随机模型。
英文摘要
Deep learning has revolutionised weather forecasting in recent years, especially through atmospheric foundation models, which offer competitive skill for a fraction of the computational costs of classic physics-based models. However, most existing foundation models are deterministic, limiting the generation of large ensembles for accurate uncertainty quantification, extreme weather risk assessment, and long-range weather forecasting. Furthermore, these models incur a large, often prohibitive, computational overhead to train from scratch. To address these shortcomings, we turn a pretrained deterministic prior model, namely the Aurora foundation model, into a generative ensemble-prediction model. To that end, we introduce a novel generative method, Denoising Stochastic Interpolants, combined with a replay buffer for Stochastic Differential Equation (SDE) rollout, enabling probabilistic training of SDE trajectories. Our stochastic foundation model, Xaurora, is finetuned from the small Aurora version, yet it approaches the state-of-the-art on global ensemble metrics and is competitive with the large version of Aurora. Our method is parameter and sample efficient, and generates skilful 15-day forecasts in 13 minutes. Our results demonstrate that deterministic foundation models can be efficiently extended into even stronger stochastic models.
Comments53 pages, 42 figures
