Statistical Postprocessing and Blending are nowadays becoming key components of the forecasting suites in many National Meteorological Services (NMS), with the objective to correct the impact of different sources of errors on the forecasts in an optimal way and to merge different sources of forecast. The final aim is to provide optimal automated seamless forecasts for the end user. Many approaches and techniques are now flourishing in the literature and in the NMSs, calling for the necessity to evaluate, compare and possibly harmonize the tools already in use.

The purpose of the EUMETNET module on statistical postprocessing is to build a community in which such activities will be shared and compared to the benefit of all EUMETNET members, and to define best practices.

The EUMETNET postprocessing benchmark dataset regroups the data used by the project to perform benchmarks of postprocessing techniques. It covers a wide area over Europe and includes gridded and stations data. Along with the benchmark itself, it is one of the main output of the project.