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