Ensemble weather forecast post-processing with a flexible probabilistic neural network approach

• URL: http://arxiv.org/abs/2303.17610v3
• Date: Thu, 27 Apr 2023 18:34:54 GMT
• Title: Ensemble weather forecast post-processing with a flexible probabilistic neural network approach
• Authors: Peter Mlakar, Janko Mer\v{s}e, Jana Faganeli Pucer
• Abstract summary: We propose a novel, neural network-based method, which produces forecasts for all locations and lead times jointly. We demonstrate the effectiveness of our method in the context of the EUPPBench benchmark.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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