  Global Station Weather Forecasting (GSWF) is crucial for various sectors,
including aviation, agriculture, energy, and disaster preparedness. Recent
advancements in deep learning have significantly improved the accuracy of
weather predictions by optimizing models based on public meteorological data.
However, existing public datasets for GSWF optimization and benchmarking still
suffer from significant limitations, such as small sizes, limited temporal
coverage, and a lack of comprehensive variables. These shortcomings prevent
them from effectively reflecting the benchmarks of current forecasting methods
and fail to support the real needs of operational weather forecasting. To
address these challenges, we present the WEATHER-5K dataset. This dataset
comprises a comprehensive collection of data from 5,672 weather stations
worldwide, spanning a 10-year period with one-hour intervals. It includes
multiple crucial weather elements, providing a more reliable and interpretable
resource for forecasting. Furthermore, our WEATHER-5K dataset can serve as a
benchmark for comprehensively evaluating existing well-known forecasting
models, extending beyond GSWF methods to support future time-series research
challenges and opportunities. The dataset and benchmark implementation are
publicly available at: https://github.com/taohan10200/WEATHER-5K.
