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Multivariate Postprocessing Methods for High-Dimensional Seasonal Weather Forecasts
Journal of the American Statistical Association ( IF 3.7 ) Pub Date : 2020-06-30 , DOI: 10.1080/01621459.2020.1769634
Claudio Heinrich 1 , Kristoffer H. Hellton 1 , Alex Lenkoski 1 , Thordis L. Thorarinsdottir 1
Affiliation  

Abstract–Seasonal weather forecasts are crucial for long-term planning in many practical situations and skillful forecasts may have substantial economic and humanitarian implications. Current seasonal forecasting models require statistical postprocessing of the output to correct systematic biases and unrealistic uncertainty assessments. We propose a multivariate postprocessing approach using covariance tapering, combined with a dimension reduction step based on principal component analysis for efficient computation. Our proposed technique can correctly and efficiently handle nonstationary, non-isotropic and negatively correlated spatial error patterns, and is applicable on a global scale. Further, a moving average approach to marginal postprocessing is shown to flexibly handle trends in biases caused by global warming, and short training periods. In an application to global sea surface temperature forecasts issued by the Norwegian climate prediction model, our proposed methodology is shown to outperform known reference methods. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.



中文翻译:

高维季节性天气预报的多元后处理方法

摘要-在许多实际情况下,季节性天气预报对于长期规划至关重要,熟练的预报可能会产生重大的经济和人道主义影响。当前的季节性预测模型需要对输出进行统计后处理,以纠正系统偏差和不切实际的不确定性评估。我们提出了一种使用协方差逐渐变细的多元后处理方法,结合基于主成分分析的降维步骤以进行有效计算。我们提出的技术可以正确有效地处理非平稳、非各向同性和负相关的空间误差模式,并且适用于全球范围。此外,边缘后处理的移动平均方法被证明可以灵活地处理由全球变暖和短培训期引起的偏差趋势。在挪威气候预测模型发布的全球海面温度预测的应用中,我们提出的方法被证明优于已知的参考方法。本文的补充材料,包括对可用于复制作品的材料的标准化描述,可作为在线补充材料获得。

更新日期:2020-06-30
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