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Weather forecasting is one of the cornerstones of meteorological work. Inthis paper, we present a new benchmark dataset named Weather2K, which aims tomake up for the deficiencies of existing weather forecasting datasets in termsof real-time, reliability, and diversity, as well as the key bottleneck of dataquality. 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 groundweather stations covering an area of 6 million square kilometers. 2)Multivariate meteorological variables. 20 meteorological factors and 3constants for position information are provided with a length of 40,896 timesteps. 3) Applicable to diverse tasks. We conduct a set of baseline tests ontime series forecasting and spatio-temporal forecasting. To the best of ourknowledge, our Weather2K is the first attempt to tackle weather forecastingtask by taking full advantage of the strengths of observation data from groundweather stations. Based on Weather2K, we further propose Meteorological Factorsbased Multi-Graph Convolution Network (MFMGCN), which can effectively constructthe intrinsic correlation among geographic locations based on meteorologicalfactors. Sufficient experiments show that MFMGCN improves both the forecastingperformance and temporal robustness. We hope our Weather2K can significantlymotivate researchers to develop efficient and accurate algorithms to advancethe task of weather forecasting. The dataset can be available athttps://github.com/bycnfz/weather2k/.

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