论文标题
使用全局ECMWF合奏预测比较多元后处理方法
Comparison of multivariate post-processing methods using global ECMWF ensemble forecasts
论文作者
论文摘要
天气预报的有影响力的一步是由于其能力解决了将来的大气状态，因此引入了运营使用的整体预测。但是，合奏天气预报通常是不足的，也可能包含偏见，这需要某种形式的后处理。一种流行的校准方法是集合模型统计（EMOS）方法，导致给定天气变量的完整预测分布。但是，这种单变量后处理形式可能会忽略不同维度之间的主要空间和/或时间相关结构。由于许多应用程序都要求在空间和/或时间上相干预测，因此多元后处理旨在捕获这些可能丢失的依赖性。我们比较了不同非参数多元方法的预测技能，以建模具有不同预测范围的集合天气预测的时间依赖性。重点是两步方法，在单变量后处理后，使用经验副总统将与不同预测范围相对应的EMOS预测分布合并到多变量校准的预测中。基于2002年1月至2014年3月的欧洲中等天气预测中心的温度，风速和降水积累的全球整体预测，我们研究了不同版本的集合Copula耦合（ECC）和Schaake Shuffle（SSH）的预测技能。通常，与原始和独立校准的预测相比，多元后加工实质上可提高预测技能。尽管最简单的ECC方法具有低计算成本提供了强大的基准方法，但最近提出的ECC和SSH的高级扩展名不能比其基本对应物提供任何显着改进。
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.
