Abstract. Non-homogeneous regression is a frequently used post-processing method for increasing the predictive skill of probabilistic ensemble weather forecasts. To adjust for seasonally varying error characteristics between ensemble forecasts and corresponding observations, different time-adaptive training schemes, including the classical sliding training window, have been developed for non-homogeneous regression. This study compares three such training approaches with the sliding-window approach for the application of post-processing near-surface air temperature forecasts across central Europe. The predictive performance is evaluated conditional on three different groups of stations located in plains, in mountain foreland, and within mountainous terrain, as well as on a specific change in the ensemble forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) used as input for the post-processing. The results show that time-adaptive training schemes using data over multiple years stabilize the temporal evolution of the coefficient estimates, yielding an increased predictive performance for all station types tested compared to the classical sliding-window approach based on the most recent days only. While this may not be surprising under fully stable model conditions, it is shown that “remembering the past” from multiple years of training data is typically also superior to the classical sliding-window approach when the ensemble prediction system is affected by certain model changes. Thus, reducing the variance of the non-homogeneous regression estimates due to increased training data appears to be more important than reducing its bias by adapting rapidly to the most current training data only.

中文翻译：

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记住过去：非齐次回归的时间自适应训练方案的比较

摘要。非齐次回归是一种常用的后处理方法，用于提高概率集合天气预报的预测技能。为了调整集合预测和相应观测值之间季节性变化的误差特征，针对非齐次回归开发了不同的时间自适应训练方案，包括经典的滑动训练窗口。本研究将三种此类培训方法与滑动窗口方法进行比较，以在整个中欧应用后处理近地表气温预测。预测性能以位于平原、山区前陆和山区的三组不同台站为条件进行评估，以及用作后处理输入的欧洲中期天气预报中心 (ECMWF) 集合预报系统的具体变化。结果表明，使用多年数据的时间自适应训练方案稳定了系数估计的时间演变，与仅基于最近几天的经典滑动窗口方法相比，对所有测试的站点类型产生了更高的预测性能。虽然在完全稳定的模型条件下这可能并不令人惊讶，但事实表明，当集成预测系统受到某些模型变化的影响时，从多年的训练数据中“记住过去”通常也优于经典的滑动窗口方法。因此，