用扩散模型生成天气预测的扰动，让确定性模型变随机，提升长期预测精度。
DEF: Diffusion-augmented Ensemble Forecasting
- 用条件扩散模型生成有结构的初始状态扰动
- 在5.625° ERA5数据上长期预测误差更小，分布合理
- 可迭代使用且能直观控制扰动强度，适合机器学习天气模型
我们提出DEF（Diffusion-augmented Ensemble Forecasting），一种生成初始条件扰动的新方法。现有扰动方法主要针对数值天气预报（NWP）求解器，限制了其在机器学习天气预测领域的应用，导致该领域随机模型常需逐个定制。我们证明，一个简单的条件扩散模型能够（1）生成有意义的结构性扰动，（2）支持迭代应用，（3）通过引导项直观控制扰动程度。该方法可将任意确定性神经预报系统转化为随机系统。在覆盖1960年代至今的5.625° ERA5再分析数据集上，我们的方法显著提升预测性能，同时保持合理的预报不确定性估计，且长期预报累积误差更低。
原文摘要 · Abstract (English)
We present DEF (\textbf{\ul{D}}iffusion-augmented \textbf{\ul{E}}nsemble \textbf{\ul{F}}orecasting), a novel approach for generating initial condition perturbations. Modern approaches to initial condition perturbations are primarily designed for numerical weather prediction (NWP) solvers, limiting their applicability in the rapidly growing field of machine learning for weather prediction. Consequently, stochastic models in this domain are often developed on a case-by-case basis. We demonstrate that a simple conditional diffusion model can (1) generate meaningful structured perturbations, (2) be applied iteratively, and (3) utilize a guidance term to intuitivey control the level of perturbation. This method enables the transformation of any deterministic neural forecasting system into a stochastic one. With our stochastic extended systems, we show that the model accumulates less error over long-term forecasts while producing meaningful forecast distributions. We validate our approach on the 5.625$^\circ$ ERA5 reanalysis dataset, which comprises atmospheric and surface variables over a discretized global grid, spanning from the 1960s to the present. On this dataset, our method demonstrates improved predictive performance along with reasonable spread estimates.
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