标题： 基于时间序列的集成模型输出统计学在温度预报后处理中的应用 显示英文标题

标题： Time Series based Ensemble Model Output Statistics for Temperature Forecasts Postprocessing

摘要： 目前，天气预报基于数值天气预报（NWP）模型来生成一组预测。尽管在过去几十年里取得了巨大改进，但它们仍然倾向于表现出系统偏差和离散误差。因此，这些预报可以通过统计后处理得到改善。 本文提出了一种在时间序列框架下扩展集合模型输出统计（EMOS）方法的方式。除了考虑预测分布的位置参数和尺度参数中的季节性和趋势外，还考虑了平均预报误差或标准化预报误差中的自回归过程。通过允许广义自回归条件异方差性（GARCH），模型可以进一步扩展。最后但同样重要的是，概述了如何使用这些模型进行任意的预报时长预测。 为了展示所提出的EMOS模型在时间序列中的性能，我们通过使用德国五个不同提前期和一组观测站对2米表面温度预报进行了后处理案例研究。结果显示，在大多数提前期-站点组合情况下，时间序列EMOS扩展模型能够显著优于基准EMOS和自回归调整EMOS（AR-EMOS）。为了补充本文内容，我们的方法附带了一个名为tsEMOS的R包。 显示英文摘要

摘要： Nowadays, weather prediction is based on numerical weather prediction (NWP) models to produce an ensemble of forecasts. Despite of large improvements over the last few decades, they still tend to exhibit systematic bias and dispersion errors. Consequently, these forecasts may be improved by statistical postprocessing. This work proposes an extension of the ensemble model output statistics (EMOS) method in a time series framework. Besides of taking account of seasonality and trend in the location and scale parameter of the predictive distribution, the autoregressive process in the mean forecast errors or the standardized forecast errors is considered. The models can be further extended by allowing generalized autoregressive conditional heteroscedasticity (GARCH). Last but not least, it is outlined how to use these models for arbitrary forecast horizons. To illustrate the performance of the suggested EMOS models in time series fashion, we present a case study for the postprocessing of 2 m surface temperature forecasts using five different lead times and a set of observation stations in Germany. The results indicate that the time series EMOS extensions are able to significantly outperform the benchmark EMOS and autoregressive adjusted EMOS (AR-EMOS) in most of the lead time-station cases. To complement this article, our method is accompanied by an R-package called tsEMOS.