用扩散模型生成千米级大气场，突破传统气象预报精度与范围限制
Apeliotes: A Diffusion-Based Modeling Framework for km-scale Multi-Level Atmospheric Fields
- 基于全局再分析数据与区域微调的扩散模型生成高分辨率大气场
- 垂直风速误差低于3%，2米气温相关性达0.99，10米风速相关性0.91
- 可生成多层级大气变量，适合气候建模与灾害预警研究者使用
高分辨率大气数据对于解析中尺度及局部气象结构至关重要，但全球许多地区仍缺乏此类数据。现有高分辨率天气产品通常通过动力降尺度生成，计算成本高昂且难以跨区域、变量和预报场景扩展。这促使了基于机器学习的降尺度系统发展，能够随机生成多个气象变量并直接生成新的高分辨率场。本文提出Apeliotes框架，用于高分辨率天气预报。该框架基于全球再分析大气数据、预训练的全球天气基础模型以及区域微调的生成式扩散模型，不仅能提供精确的千米级气象变量，还能生成现有全球大气数据中未直接提供的多层级大气场。全面评估表明，Apeliotes表现优异：垂直风速场预测误差低于3%，10米风速相关性达0.91，2米温度相关性达0.99，相应归一化均方根误差（NRMSE）分别为0.42和0.17。
原文摘要 · Abstract (English)
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.
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