Nowadays, weather forecasts are commonly generated by ensemble forecasts based on multiple runs of numerical weather prediction models. However, such forecasts are usually miscalibrated and/or biased, thus require statistical postprocessing. Non-homogeneous regression models, such as the ensemble model output statistics are frequently applied to correct these forecasts. Nonetheless, these methods often rely on the assumption of an unimodal parametric distribution, leading to improved, but sometimes not fully calibrated forecasts. To address this issue, a mixture regression model is presented, where the ensemble forecasts of each exchangeable group are linked to only one mixture component and mixture weight, called mixture of model output statistics (MIXMOS). In order to remove location specific effects and to use a longer training data, the standardized anomalies of the response and the ensemble forecasts are employed for the mixture of standardized anomaly model output statistics (MIXSAMOS). As carefully selected covariates, e.g. from different weather variables, can enhance model performance, the non-cyclic gradient-boosting algorithm for mixture regression models is introduced. Furthermore, MIXSAMOS is extended by this gradient-boosting algorithm (MIXSAMOS-GB) providing an automatic variable selection. The novel mixture regression models substantially outperform state-of-the-art postprocessing models in a case study for 2m surface temperature forecasts in Germany.

翻译：如今，天气预报通常基于数值天气预报模式的多重运行生成的集合预报。然而，此类预报通常存在校准不当和/或偏差问题，因此需要进行统计后处理。非齐次回归模型，如集合模式输出统计，常被用于校正这些预报。尽管如此，这些方法通常依赖于单峰参数分布的假设，导致预报虽有所改进，但有时未能完全校准。为解决此问题，本文提出了一种混合回归模型，其中每个可交换组的集合预报仅与一个混合分量和混合权重相关联，称为模型输出统计混合。为消除特定地点效应并利用更长的训练数据，响应变量和集合预报的标准化异常被用于标准化异常模型输出统计混合。由于精心选择的协变量（例如来自不同天气变量）可提升模型性能，本文引入了用于混合回归模型的非循环梯度提升算法。此外，通过该梯度提升算法扩展了标准化异常模型输出统计混合，实现了自动变量选择。在德国2米地表温度预报的案例研究中，新型混合回归模型显著优于当前最先进的后处理模型。