Weather forecasting is one of the cornerstones of meteorological work. In this paper, we present a new benchmark dataset named Weather2K, which aims to make up for the deficiencies of existing weather forecasting datasets in terms of real-time, reliability, and diversity, as well as the key bottleneck of data quality. To be specific, our Weather2K is featured from the following aspects: 1) Reliable and real-time data. The data is hourly collected from 2,130 ground weather stations covering an area of 6 million square kilo- meters. 2) Multivariate meteorological variables. 20 meteorological factors and 3 constants for position information are provided with a length of 40,896 time steps. 3) Applicable to diverse tasks. We conduct a set of baseline tests on time series forecasting and spatio-temporal forecasting. To the best of our knowledge, our Weather2K is the first attempt to tackle weather forecasting task by taking full advantage of the strengths of observation data from ground weather stations. Based on Weather2K, we further propose Meteorological Factors based Multi-Graph Convolution Network (MFMGCN), which can effectively construct the intrinsic correlation among geographic locations based on meteorological factors. Sufficient experiments show that MFMGCN improves both the forecasting performance and temporal robustness. We hope our Weather2K can significantly motivate researchers to develop efficient and accurate algorithms to advance the task of weather forecasting. The dataset can be available at https://github.com/bycnfz/weather2k/.

翻译：天气预报是气象工作的基石之一。本文提出一个名为Weather2K的新型基准数据集，旨在弥补现有气象预报数据集在实时性、可靠性和多样性方面的不足，以及数据质量这一关键瓶颈。具体而言，Weather2K具有以下特点：1）可靠且实时的数据。数据每小时从覆盖600万平方公里区域的2130个地面气象站采集而来。2）多元气象变量。提供20个气象因子及3个地理定位常数，时间步长共计40,896步。3）适用于多样化任务。我们在时间序列预测和时空预测任务上开展了一系列基线测试。据我们所知，Weather2K是首个充分利用地面气象站观测数据优势来解决天气预报任务的尝试。基于Weather2K，我们进一步提出基于气象因子的多图卷积网络（MFMGCN），该网络能有效依据气象因子构建地理位置间的内在关联性。充足实验表明，MFMGCN提升了预测性能和时间鲁棒性。我们期待Weather2K能显著激励研究者开发高效准确的算法，推动天气预报任务的发展。该数据集可在https://github.com/bycnfz/weather2k/获取。