基于相似性的Schaake shuffle实现
A similarity-based implementation of the Schaake shuffle
摘要：
现代天气预报通常基于集合预报系统，集合预报系统由相对于初始条件和/或大气的参数化而变化的数值天气预报模型的多次运行组成。包围圈的预测往往是有偏差的，并显示离散误差，因此需要进行统计后处理。然而，目前的后处理方法往往是单变量的，并适用于一个单一的天气量在一个单一的位置和一个单一的预测范围，从而无法考虑潜在的关键依赖结构。基于经验copula的非参数多元后处理方法，如系综copula耦合或Schaake shuffle，可以解决这个缺点。介绍了Schaake shuffle的一个具体实现，称为SimSchaake方法。SimSchaake方法使用来自过去观测的依赖模式来聚合单变量后处理集合预报。具体而言，观测是从历史日期，在集合预报类似于当前集合预报的一个特定的相似性标准。SimSchaake集合在应用于欧洲中期天气预报中心的地面温度集合预报时，其性能优于所有参考集合。
英文摘要：
Contemporary weather forecasts are typically based on ensemble prediction systems, which consist of multiple runs of numerical weather prediction models that vary with respect to in the initial conditions and/or the the parameterization of the atmosphere. Ensemble forecasts are frequently biased and show dispersion errors and thus need to be statistically postprocessed. However, current postprocessing approaches are often univariate and apply to a single weather quantity at a single location and for a single prediction horizon only, thereby failing to account for potentially crucial dependence structures. Non-parametric multivariate postprocessing methods based on empirical copulas, such as ensemble copula coupling or the Schaake shuffle, can address this shortcoming. A specific implementation of the Schaake shuffle, called the SimSchaake approach, is introduced. The SimSchaake method aggregates univariately postprocessed ensemble forecasts using dependence patterns from past observations. Specifically, the observations are taken from historical dates at which the ensemble forecasts resembled the current ensemble prediction with respect to a specific similarity criterion. The SimSchaake ensemble outperforms all reference ensembles in an application to ensemble forecasts for surface temperature from the European Centre for Medium-Range Weather Forecasts.
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