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Post-processing ensemble prediction systems can improve the reliability ofweather forecasting, especially for extreme event prediction. In recent years,different machine learning models have been developed to improve the quality ofweather post-processing. However, these models require a comprehensive datasetof weather simulations to produce high-accuracy results, which comes at a highcomputational cost to generate. This paper introduces the ENS-10 dataset,consisting of ten ensemble members spanning 20 years (1998-2017). The ensemblemembers are generated by perturbing numerical weather simulations to capturethe chaotic behavior of the Earth. To represent the three-dimensional state ofthe atmosphere, ENS-10 provides the most relevant atmospheric variables at 11distinct pressure levels and the surface at 0.5-degree resolution for forecastlead times T=0, 24, and 48 hours (two data points per week). We propose theENS-10 prediction correction task for improving the forecast quality at a48-hour lead time through ensemble post-processing. We provide a set ofbaselines and compare their skill at correcting the predictions of threeimportant atmospheric variables. Moreover, we measure the baselines' skill atimproving predictions of extreme weather events using our dataset. The ENS-10dataset is available under the Creative Commons Attribution 4.0 International(CC BY 4.0) license.

ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts