发表机构
Hunan University; China Meteorological Administration（湖南大学; 中国气象局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有研究分离处理站点间空间依赖与变量间物理耦合的问题，本文提出M$^2$Weather基准，涵盖多尺度站点数据与统一协议，并设计即插即用适配器，验证了联合建模及补全缺失关系可提升预报精度。
AI 中文摘要
站点天气预报从根本上受到站点间复杂空间依赖性和天气变量间强物理耦合的影响。然而，现有研究往往分别考虑这些关系，并使用不同的数据集和实验设置，阻碍了对它们各自及联合贡献的系统评估。在本文中，我们介绍了M$^2$Weather，一个用于联合多站点和多变量天气预报的基准。通过多标准质量控制和站点分层，我们收集了2,809个高质量站点，包含5个物理耦合的天气变量，覆盖三个空间尺度：法国、欧洲和全球。这种多尺度设计使我们能够检验结论是否从国家站点网络推广到全球站点网络。我们还引入了统一的训练和评估协议，以实现不同站点-变量建模范式的公平比较。为了进一步检验建模站点-变量关系的好处，我们设计了一个轻量级、即插即用的适配器。使用训练好的天气预报模型，该适配器可以在不重新训练模型的情况下引入缺失的站点或变量关系。这使得对站点-变量关系的公平且高效的调查成为可能。对16个代表性模型的系统评估显示了联合建模站点和变量关系的好处。完成缺失的关系进一步降低了所有三个数据集上所有适配模型的MSE。总之，这些结果确定了站点和变量之间的互补信息是改进站点天气预报的重要资源。我们的代码可在以下网址获取：https URL。
英文摘要
Station weather forecasting is fundamentally shaped by both complex spatial dependencies across stations and strong physical coupling among weather variables. However, existing studies often consider these relationships separately and use different datasets and experimental settings, hindering systematic assessment of their individual and joint contributions. In this paper, we introduce $M^2$Weather, a benchmark for joint multi-station and multi-variable weather forecasting. Through multi-criteria quality control and station stratification, we collect 2,809 high-quality stations with 5 physically coupled weather variables across three spatial scales: France, Europe, and Global. This multi-scale design lets us examine whether conclusions persist from national to global station networks. We also introduce unified training and evaluation protocols to enable fair comparison of different station-variable modeling paradigms. To further examine the benefits of modeling station-variable relationships, we design a lightweight, plug-and-play adapter. With a trained weather forecasting model, this adapter can introduce missing station or variable relationships without retraining the model. This enables fair and efficient investigation of station-variable relationships. Systematic evaluation of 16 representative models shows the benefits of jointly modeling station and variable relationships. Completing missing relationships further reduces MSE for all adapted models on all three datasets. Together, these results identify the complementary information across stations and variables as an important resource for improving station weather forecasting. Our code can be obtained at https://github.com/hnu-vis/M2-Weather.
Comments36 pages
