发表机构
Old Dominion University（奥多明尼昂大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高分辨率大气数据受限问题，Apeliotes框架基于全球再分析等数据构建，能提供千米尺度天气变量和多层大气场，经评估性能极具竞争力，预测垂直风廓线等有高精度表现。
AI 中文摘要
高分辨率大气数据对于解析中尺度和局部气象结构至关重要，但世界许多地区此类数据集有限。现有高分辨率天气产品通常通过动力降尺度生成，成本高且难以跨地点、变量和预测场景扩展。基于此，本文提出Apeliotes框架，它基于全球再分析大气数据、预训练的全球天气基础模型和区域训练的生成扩散模型构建，不仅能提供准确的千米尺度天气变量，还能生成现有全球大气数据中没有的多层大气场。综合评估表明其性能极具竞争力，如预测垂直风廓线误差小于3%，10米风速相关性达0.91，2米温度相关性达0.99，归一化均方根误差分别为0.42和0.17。
英文摘要
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.
