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.

翻译：集合预报后处理是生成精确概率预报的必要步骤。传统后处理方法通过估计参数化分布的参数来运行，通常按每个位置或每个预报时效分别进行。我们提出一种基于神经网络的新方法，能够联合生成所有位置和所有预报时效的预报。为放宽许多后处理方法中存在的分布假设，我们的方法引入归一化流作为灵活的参数化分布估计器。这使得我们能够以数学精确的方式对变化的预报分布进行建模。我们在EUPPBench基准测试中验证了该方法的效果，针对西欧某子区域的气象站进行温度预报后处理。实验表明，我们的新方法在基准测试中展现出最先进的性能，优于我们之前表现良好的方法。此外，通过详细比较我们新后处理方法的三种变体，我们阐明了该方法优于基于逐预报时效方法和基于分布假设方法的原因。