Since the start of the operational use of ensemble prediction systems, ensemble-based probabilistic forecasting has become the most advanced approach in weather prediction. However, despite the persistent development of the last three decades, ensemble forecasts still often suffer from the lack of calibration and might exhibit systematic bias, which calls for some form of statistical post-processing. Nowadays, one can choose from a large variety of post-processing approaches, where parametric methods provide full predictive distributions of the investigated weather quantity. Parameter estimation in these models is based on training data consisting of past forecast-observation pairs, thus post-processed forecasts are usually available only at those locations where training data are accessible. We propose a general clustering-based interpolation technique of extending calibrated predictive distributions from observation stations to any location in the ensemble domain where there are ensemble forecasts at hand. Focusing on the ensemble model output statistics (EMOS) post-processing technique, in a case study based on wind speed ensemble forecasts of the European Centre for Medium-Range Weather Forecasts, we demonstrate the predictive performance of various versions of the suggested method and show its superiority over the regionally estimated and interpolated EMOS models and the raw ensemble forecasts as well.

翻译：自集合预报系统投入业务化运行以来，基于集合的概率预测已成为天气预报领域最先进的方法。然而，尽管过去三十年持续发展，集合预报仍常存在校准不足的问题，并可能表现出系统性偏差，因此需要采用某种形式的统计后处理。目前，可从多种后处理方法中选择，其中参数化方法能提供所研究气象量的完整预测分布。这些模型中的参数估计基于由历史预报-观测对构成的训练数据，因此后处理预报通常仅在可获取训练数据的位置可用。我们提出一种基于聚类的通用插值技术，可将校准后的预测分布从观测站点扩展到集合预报域内存在集合预报的任何位置。以集合模型输出统计（EMOS）后处理技术为重点，基于欧洲中期天气预报中心（ECMWF）的风速集合预报案例研究，我们展示了所提方法不同版本的预测性能，并证明其优于区域估计插值EMOS模型及原始集合预报。