SimCast-S2S：一种基于气候模拟迁移学习的高效次季节降水预测生成模型
SimCast-S2S: A Computationally Efficient Diffusion Model for Subseasonal Precipitation Forecasting
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中文总结 AI 辅助
本研究提出SimCast-S2S，一种基于气候模拟迁移学习的潜在扩散生成模型，通过变分自编码器的紧凑潜在空间与LoRA迁移学习，实现高效次季节降水概率预测，性能优于深度学习基线及ECMWF-S2S等业务系统。
中文摘要 AI 辅助
次季节至季节（S2S）降水预测具有重大的经济和社会影响，但因预测信号薄弱、不确定性高以及业务系统的计算成本（该成本限制了模拟保真度）而极具挑战性。我们提出SimCast-S2S，这是一种用于概率S2S降水预测的生成式潜在扩散框架，旨在解决数据驱动预测中的三大瓶颈：其一，由于S2S预测需要不确定性量化而非仅确定性点预测，SimCast-S2S是首个采用基于扩散的生成流水线进行S2S预测的数据驱动系统，能够有效从潜在条件分布中采样；其二，由于在物理空间生成大型概率集合计算成本高昂，SimCast-S2S转而在变分自编码器学习得到的紧凑潜在空间中运行，可实现高效的大型集合生成；其三，扩散模型通常需要大型训练数据集，SimCast-S2S通过低秩适配（LoRA）的迁移学习克服这一问题，先在大型气候模拟集合上进行预训练，再在有限的再分析数据上微调。在再分析数据上，SimCast-S2S的性能优于深度学习基线模型，包括卷积神经网络和U-Net架构。值得注意的是，尽管仅使用了部分大气输入变量，且未进行后处理、偏差校正或校准，SimCast-S2S仍与ECMWF-S2S等最先进的业务系统具有竞争力，且在许多情况下表现更优。这些结果表明，潜在生成建模结合模拟到再分析的迁移学习，为数据驱动的概率S2S降水预测提供了一条高效且可扩展的路径。
英文摘要
Subseasonal-to-seasonal (S2S) precipitation forecasting has substantial financial and societal impact, yet remains challenging because of weak predictive signals, high associated uncertainty, and the computational cost of operational systems, which constrains simulation fidelity. We introduce SimCast-S2S, a generative latent-diffusion framework for probabilistic S2S precipitation forecasting that addresses three major bottlenecks in data-driven prediction. First, because S2S prediction requires uncertainty quantification rather than only deterministic point forecasts, SimCast-S2S is the first data-driven system that uses a diffusion-based generative pipeline for S2S prediction, enabling effective sampling from the underlying conditional distribution. Second, since generating large probabilistic ensembles is computationally costly in physical space, SimCast-S2S instead operates in a compact latent space learned by variational autoencoders (VAEs), enabling efficient large-ensemble generation. Third, diffusion models typically require large training datasets; SimCast-S2S overcomes this via transfer learning with low-rank adaptation (LoRA), pretraining on large ensembles of climate simulations before fine-tuning on limited reanalysis data. On reanalysis data, SimCast-S2S outperforms deep learning baselines, including convolutional neural networks and U-Net architectures. Notably, despite using only a subset of atmospheric input variables and no post-processing, bias correction, or calibration, SimCast-S2S remains competitive with, and in many aspects outperforms, state-of-the-art operational systems such as the ECMWF-S2S baseline. These results indicate that latent generative modeling combined with simulation-to-reanalysis transfer learning offers an efficient and scalable path toward data-driven probabilistic S2S precipitation forecasting.
发表机构
- University of Virginia（弗吉尼亚大学）
- School of Data Science（数据科学学院）
- Department of Environmental Sciences（环境科学系）
机构由 AI 辅助整理，请以论文原文为准。
