Probabilistic weather forecasts from ensemble systems require statistical postprocessing to yield calibrated and sharp predictive distributions. This paper presents an area-covering postprocessing method for ensemble precipitation predictions. We rely on the ensemble model output statistics (EMOS) approach, which generates probabilistic forecasts with a parametric distribution whose parameters depend on (statistics of) the ensemble prediction. A case study with daily precipitation predictions across Switzerland highlights that postprocessing at observation locations indeed improves high-resolution ensemble forecasts, with 4.5% CRPS reduction on average in the case of a lead time of 1 day. Our main aim is to achieve such an improvement without binding the model to stations, by leveraging topographical covariates. Specifically, regression coefficients are estimated by weighting the training data in relation to the topographical similarity between their station of origin and the prediction location. In our case study, this approach is found to reproduce the performance of the local model without using local historical data for calibration. We further identify that one key difficulty is that postprocessing often degrades the performance of the ensemble forecast during summer and early autumn. To mitigate, we additionally estimate on the training set whether postprocessing at a specific location is expected to improve the prediction. If not, the direct model output is used. This extension reduces the CRPS of the topographical model by up to another 1.7% on average at the price of a slight degradation in calibration. In this case, the highest improvement is achieved for a lead time of 4 days.

翻译：由混合系统产生的概率天气预报需要统计后处理,以得出校准和精确的预测分布。本文件展示了一种覆盖地区的后处理方法,以进行混合降水预测。我们主要依靠混合模型输出统计方法,这种方法产生概率预测,其参数取决于(统计)组合预测的参数的参数分布,在瑞士各地进行的每日降水预测案例研究突出表明,观察地点的后处理确实改进了高分辨率混合预测,在领先时间为1天的情况下,平均减少4.5%的CRPS。我们的主要目的,是通过利用地形变换法,实现这种改进,而不将模型与台站捆绑在一起。具体地说,回归系数是通过将培训数据与其来源地点和预测地点之间的地形相似性(统计)加权而估算的。在我们的案例研究中发现,在不使用当地历史变现数据进行校准的情况下,当地模型的性能确实有所改善,我们进一步发现一个关键困难是,在领先时间为1天的情况下,后处理往往使模型的降幅降低,而没有将模型与模型连接起来,而是利用地形变换的模型。在夏季和预测中,我们采用的是更接近的预测地点的进度。