  Ensemble forecast post-processing is a necessary step in producing accurate
probabilistic forecasts. Conventional post-processing methods operate by
estimating the parameters of a parametric distribution, frequently on a
per-location or per-lead-time basis. We propose a novel, neural network-based
method, which produces forecasts for all locations and lead times, jointly. To
relax the distributional assumption of many post-processing methods, our
approach incorporates normalizing flows as flexible parametric distribution
estimators. This enables us to model varying forecast distributions in a
mathematically exact way. We demonstrate the effectiveness of our method in the
context of the EUPPBench benchmark, where we conduct temperature forecast
post-processing for stations in a sub-region of western Europe. We show that
our novel method exhibits state-of-the-art performance on the benchmark,
outclassing our previous, well-performing entry. Additionally, by providing a
detailed comparison of three variants of our novel post-processing method, we
elucidate the reasons why our method outperforms per-lead-time-based approaches
and approaches with distributional assumptions.
