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Evaluation of a compound distribution based on weather pattern subsampling for extreme rainfall in Norway

机译：基于天气模式子采样的挪威极端降雨复合分布评估

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Simulation methods for design flood analyses require estimates of extremeprecipitation for simulating maximum discharges. This article evaluates the multi-exponential weather pattern(MEWP) model, a compound model based on weather pattern classification,seasonal splitting and exponential distributions, for its suitability for usein Norway. The MEWP model is the probabilistic rainfall model used in theSCHADEX method for extreme flood estimation. Regional scores of evaluation are used in a split sample framework to compare the MEWP distribution with more general heavy-tailed distributions, in this case the Multi Generalized Pareto Weather Pattern (MGPWP) distribution. The analysis shows the clear benefit obtained from seasonal and weather pattern-based subsampling for extreme value estimation. The MEWP distribution is found to have an overall better performance as compared with the MGPWP, which tends to overfit the data and lacks robustness. Finally, we take advantage of the split sample framework to present evidence for an increase in extreme rainfall in the southwestern part of Norway during the period 1979–2009, relative to 1948–1978.
机译：用于设计洪水分析的模拟方法需要估算极端降水才能模拟最大流量。本文评估了多指数天气模式（MEWP）模型，该模型是基于天气模式分类，季节分裂和指数分布的复合模型，适用于挪威。 MEWP模型是SCHADEX方法中用于极端洪水估计的概率降雨模型。在拆分样本框架中使用区域评估分数来比较MEWP分布与更一般的重尾分布，在这种情况下为多广义帕累托天气模式（MGPWP）分布。分析显示，从季节性和基于天气模式的二次采样中获得了明显的收益，可用于极值估计。与MGPWP相比，发现MEWP分布总体上具有更好的性能，而MGPWP往往会过度拟合数据且缺乏鲁棒性。最后，我们利用分割样本框架来提供证据，证明1979-2009年挪威西南部地区的极端降雨相对于1948-1978年有所增加。

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