New approaches to postprocessing of multi-model ensemble forecasts
New approaches to postprocessing of multi-model ensemble forecasts
复制标题
多模式集合预报后处理的新方法
DOI：
10.1002/qj.3632
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发表时间：
2019
影响因子：
8.9
通讯作者：
Barnes C
中科院分区：
文献类型：
--
作者：
Barnes C
集合天气预报常常低估不确定性，导致人们对预测过于自信。事实证明，结合多个单独集合的多模型预报在预测温度方面比单集合预报显示出更高的技能，但在联合预测中往往会保留一些偏差。已建立的后处理技术能够纠正单变量预测中的偏差和校准问题，但通常不适用于处理多变量预测（例如多个变量或多个位置的预测）。我们提出了一种灵活的多元贝叶斯后处理框架，基于表示集合和观测到的天气之间关系的有向无环图。后验预测是根据可用的集合预测和对共享差异的估计来推断的，而共享差异的估计是从过去的预测-观测对的集合中获得的。我们还提出了一种新方法来选择合适的训练集来估计所需的校正，使用天气尺度的类似物来获得依赖于制度的调整估计。所提出的技术应用于 2007 年至 2013 年冬季英国地表温度的预测。尽管由此产生的参数多元正态概率预测的锐度略低于主要竞争对手，但它们比仅基于集合或气候学的相关结构更好地捕捉观测的空间结构，并且对预测的变量和空间域的变化具有鲁棒性，同时大大降低了计算成本。
Ensemble weather forecasts often under‐represent uncertainty, leading to overconfidence in their predictions. Multi‐model forecasts combining several individual ensembles have been shown to display greater skill than single‐ensemble forecasts in predicting temperatures, but tend to retain some bias in their joint predictions. Established postprocessing techniques are able to correct bias and calibration issues in univariate forecasts, but are generally not designed to handle multivariate forecasts (of several variables or at several locations, say). We propose a flexible multivariate Bayesian postprocessing framework, based on a directed acyclic graph representing the relationships between the ensembles and the observed weather. The posterior forecast is inferred from available ensemble forecasts and an estimate of the shared discrepancy, obtained from a collection of past forecast–observation pairs. We also propose a novel approach to selecting an appropriate training set for estimation of the required correction, using synoptic‐scale analogues to obtain a regime‐dependent estimate of the adjustment. The proposed technique is applied to forecasts of surface temperature over the UK during the winter period from 2007 to 2013. Although the resulting parametric multivariate‐normal probabilistic forecasts are marginally less sharp than those of the leading competitor, they capture the spatial structure of the observations better than a correlation structure based on either the ensembles or climatology alone, and are robust to changes in the variables and spatial domain of the forecast, at a greatly reduced computational cost.