Abstract
AI weather prediction ensembles with latent noise injection and optimized with the continuous ranked probability score (CRPS) have produced both accurate and well-calibrated predictions with far less computational cost compared with diffusion-based methods. However, current CRPS ensemble approaches vary in their training strategies and noise injection mechanisms, with most injecting noise globally throughout the network via conditional normalization. This structure increases training expense and limits the physical interpretability of the stochastic perturbations. We introduce Stochastic Decomposition Layers (SDL) for converting deterministic machine learning weather models into probabilistic ensemble systems. Adapted from StyleGAN's hierarchical noise injection, SDL applies learned perturbations at three decoder scales through latent-driven modulation, per-pixel noise, and channel scaling. When applied to WXFormer via transfer learning, SDL requires less than 2\% of the computational cost needed to train the baseline model. Each ensemble member is generated from a compact latent tensor (5 MB), enabling perfect reproducibility and post-inference spread adjustment through latent rescaling. Evaluation on 2022 ERA5 reanalysis shows ensembles with spread-skill ratios approaching unity and rank histograms that progressively flatten toward uniformity through medium-range forecasts, achieving calibration competitive with operational IFS-ENS. Multi-scale experiments reveal hierarchical uncertainty: coarse layers modulate synoptic patterns while fine layers control mesoscale variability. The explicit latent parameterization provides interpretable uncertainty quantification for operational forecasting and climate applications.
Abstract (translated)
AI天气预测集成模型通过隐式噪声注入并使用连续排名概率评分（CRPS）进行优化，与基于扩散的方法相比，在计算成本大幅降低的情况下仍能提供准确且校准良好的预测结果。然而，当前的CRPS集成方法在训练策略和噪声注入机制方面存在差异，大多数采用全局条件归一化方式在整个网络中注入噪声。这种结构增加了训练成本，并限制了随机扰动的物理可解释性。我们引入了随机分解层（SDL），用于将确定性的机器学习天气模型转换为概率集合系统。SDL借鉴了StyleGAN分层噪声注入的方法，通过潜在驱动调制、逐像素噪声和通道缩放，在解码器的三个尺度上应用学到的扰动。当应用于通过迁移学习方式的WXFormer时，SDL所需的计算成本仅为基线模型训练成本的大约2%。每个集合成员由一个紧凑的潜在张量（5MB）生成，这使得完全可重现且可通过潜在重缩放进行后推理扩散调整成为可能。在2022年ERA5再分析数据上的评估表明，所构建的集成系统在中程预报中的扩散-技能比接近于1，排名直方图逐渐趋于均匀化，实现了与操作级IFS-ENS相当的校准水平。多尺度实验揭示了分层不确定性：粗粒度层次调节大尺度天气模式，而细粒度层次控制中尺度变化。显式的潜在参数化提供了适用于操作预报和气候应用的可解释性不确定性量化方法。
URL
https://arxiv.org/abs/2512.18815
