A Benchmarking Dataset for Seasonal Weather Forecasts

There is an increasing demand for high-quality seasonal weather forecasts from a broad range of stakeholders. However, the numerical weather prediction (NWP) output on which these forecasts are based require substantial postprocessing, as they are subject to systematic errors in both mean and spread. In order to validate any proposed post-processing methodology, the research community would benefit from a benchmark dataset on which more sophisticated methods can quickly be developed and tested. We supply a multi-model, multi-variable global dataset using five forecasting systems from the Copernicus climate data store (CDS) which can help serve these purposes. Our dataset is constructed using a straightforward anomaly standardization methodology with a leave-year-out cross validation design. In addition, validating observations from the ERA5 dataset are supplied, enabling rapid verification of system performance. The goal of this dataset is to save the research community the substantial investment in time necessary to create a usable baseline for their own investigations and also to create a standard benchmark dataset to which different research groups can compare results.