标题： Weather2K：基于地面气象站实时观测数据的多变量时空基准数据集用于气象预报 显示英文标题

标题： Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations

摘要： 气象预报是气象工作的核心之一。 在本文中，我们提出一个名为Weather2K的新基准数据集，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，以及数据质量的关键瓶颈。 具体而言，我们的Weather2K从以下几个方面具有特色： 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个地面气象站收集。 2) 多变量气象变量。 提供了20个气象因素和3个位置信息常量，时间步长为40896。 3) 适用于多种任务。 我们在时间序列预测和时空预测上进行了一组基线测试。 据我们所知，我们的Weather2K是首次充分利用地面气象站观测数据优势来解决气象预报任务的尝试。 基于Weather2K，我们进一步提出了基于气象因子的多图卷积网络（MFMGCN），该网络可以有效地根据气象因子构建地理位置之间的内在相关性。 充分的实验表明，MFMGCN提高了预测性能和时间鲁棒性。 我们希望我们的Weather2K能显著激励研究人员开发高效准确的算法，以推进气象预报任务。 该数据集可在https://github.com/bycnfz/weather2k/获取。 显示英文摘要

摘要： 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 kilometers. 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/.