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A spatio-temporal model for Red Sea surface temperature anomalies
Extremes ( IF 1.3 ) Pub Date : 2020-06-26 , DOI: 10.1007/s10687-020-00383-2
Christian Rohrbeck , Emma S. Simpson , Ross P. Towe

This paper details the approach of team Lancaster to the 2019 EVA data challenge, dealing with spatio-temporal modelling of Red Sea surface temperature anomalies. We model the marginal distributions and dependence features separately; for the former, we use a combination of Gaussian and generalised Pareto distributions, while the dependence is captured using a localised Gaussian process approach. We also propose a space-time moving estimate of the cumulative distribution function that takes into account spatial variation and temporal trend in the anomalies, to be used in those regions with limited available data. The team’s predictions are compared to results obtained via an empirical benchmark. Our approach performs well in terms of the threshold-weighted continuous ranked probability score criterion, chosen by the challenge organiser.



中文翻译:

红海表面温度异常的时空模型

本文详细介绍了兰开斯特队的方法应对2019年EVA数据挑战,处理红海表面温度异常的时空建模。我们分别对边际分布和依赖特征进行建模。对于前者,我们使用高斯分布和广义Pareto分布的组合,而依赖关系则使用局部高斯过程方法进行捕获。我们还提出了一种累积分布函数的时空移动估计,该估计考虑了异常中的空间变化和时间趋势,将在可用数据有限的区域中使用。将团队的预测与通过经验基准获得的结果进行比较。我们的方法在挑战组织者选择的阈值加权连续排名概率评分标准方面表现良好。

更新日期:2020-06-26
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