An influential step in weather forecasting was the introduction of ensemble forecasts in operational use due to their capability to account for the uncertainties in the future state of the atmosphere. However, ensemble weather forecasts are often underdispersive and might also contain bias, which calls for some form of post-processing. A popular approach to calibration is the ensemble model output statistics (EMOS) approach resulting in a full predictive distribution for a given weather variable. However, this form of univariate post-processing may ignore the prevailing spatial and/or temporal correlation structures among different dimensions. Since many applications call for spatially and/or temporally coherent forecasts, multivariate post-processing aims to capture these possibly lost dependencies. We compare the forecast skill of different nonparametric multivariate approaches to modeling temporal dependence of ensemble weather forecasts with different forecast horizons. The focus is on two-step methods, where after univariate post-processing, the EMOS predictive distributions corresponding to different forecast horizons are combined to a multivariate calibrated prediction using an empirical copula. Based on global ensemble predictions of temperature, wind speed and precipitation accumulation of the European Centre for Medium-Range Weather Forecasts from January 2002 to March 2014, we investigate the forecast skill of different versions of Ensemble Copula Coupling (ECC) and Schaake Shuffle (SSh). In general, compared with the raw and independently calibrated forecasts, multivariate post-processing substantially improves the forecast skill. While even the simplest ECC approach with low computational cost provides a powerful benchmark method, recently proposed advanced extensions of the ECC and the SSh are found to not provide any significant improvements over their basic counterparts.
翻译:在天气预报方面,一个具有影响力的步骤是,由于能够对未来大气状况的不确定性进行核算,在业务使用中引入了整体性预测,这是因为它们有能力对各个不同层面之间的空间和/或时间相关结构进行统算。然而,整体性天气预报往往不够分散,而且可能包含偏差,这就要求某种形式的后处理。一种流行的校准方法是混合模型产出统计(EMOS)方法,导致对特定天气变量进行全面的预测分布。然而,这种形式的单级后处理可能会忽视当前不同层面的空间和/或时间性相关结构。由于许多应用需要空间和/或时间一致的预报,因此多变量后处理的目的是捕捉这些可能丧失的依赖性。我们比较了不同非分级多变量的多变量方法的预测技能,以不同预测的预测前景为模式,在连续后处理后,EMOSS预测的低位性分布与不同的预测性预测性方法相结合,在使用实证性相协调的预测、多变量后后后期性后期性后期性后期预测,我们从2014年1月的中度预测中值中值中值中值中值中值中值中值中值中值中值中值中值中值中值中值中值中值中值中值后,提供了任何。