这个框架用扩散模型做公里级天气预测，垂直风误差不到3%，比传统方法快很多，做精细化气象预报的朋友可以看看。
Apeliotes框架结合全球再分析数据、预训练天气基础模型和区域扩散模型，生成公里级多变量大气场。在垂直风廓线预测上误差低于3%，10米风速相关性达0.91，2米温度相关性达0.99，NRMSE分别为0.42和0.17。该方法比传统动力降尺度计算成本大幅降低，可扩展至不同区域和变量。
Apeliotes: A Diffusion-Based Modeling Framework for km-scale Multi-Level Atmospheric Fields
High-resolution atmospheric data are required to resolve mesoscale and localized meteorological structures, however such datasets remain limited in many regions of the world. Existing high-resolution weather products are typically produced through dynamical downscaling, which is computationally expensive and difficult to scale across locations, variables, and forecast scenarios. These limitations motivate machine-learning-based downscaling systems that can generate multiple weather variables stochastically while producing new high-resolution fields directly. In this paper we present Apeliotes, a framework for high-resolution weather forecasting. Built on the global re-analysis atmospheric data, a pre-trained global weather foundation model, and a regionally trained generative diffusion model, Apeliotes not only provides accurate kilometer-scale weather variables, but also multi-level atmospheric fields which are not directly available in the existing global atmospheric data. Our comprehensive evaluation demonstrates that Apeliotes achieves highly competitive performance. The model predicts vertical wind profile with less than 3\% error between truth and predicted fields, achieving correlations of 0.91 for 10-m wind speed and 0.99 for 2-m temperature, with NRMSE values of 0.42 and 0.17, respectively.