Spatiotemporal hierarchical modelling of extreme precipitation in Western Australia using anisotropic Gaussian random fields
Spatiotemporal hierarchical modelling of extreme precipitation in Western Australia using anisotropic Gaussian random fields
复制标题
使用各向异性高斯随机场对西澳大利亚极端降水进行时空分层建模
DOI：
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发表时间：
2013
影响因子：
3.8
通讯作者：
A. Stephenson
中科院分区：
文献类型：
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作者：
P. Apputhurai;A. Stephenson
我们讨论了澳大利亚西南部极端降水事件在空间和时间上的统计模拟方法，该方法使用了一个潜在的时空过程，其中降水极大值服从广义极值分布。通过对位置和尺度参数的趋势建模来捕捉时间特征。利用各向异性高斯随机场获取空间特征。现场特定的解释变量也被纳入其中。我们使用贝叶斯推断方法对西澳大利亚州首府珀斯周围36个气象站1907-2009年期间记录的极端降水量进行了拟合。使用DIC标准进行型号选择。最佳拟合模型显示，随着时间的推移，极端降水事件变得不那么频繁，表现出显著的非平稳性。极端降水事件在沿海地区更强烈，强度随着我们前往东北更高和更干燥的地区而减弱。
We discuss an approach for the statistical modelling of extreme precipitation events in South-West Australia over space and time, using a latent spatiotemporal process where precipitation maxima follow a generalised extreme value distribution. Temporal features are captured by modelling trends on the location and scale parameters. Spatial features are captured using anisotropic Gaussian random fields. Site specific explanatory variables are also incorporated. We fit several models using Bayesian inferential methods to precipitation extremes recorded at 36 weather stations around the Western Australian state capital city of Perth over the period 1907–2009. Model choice is performed using the DIC criterion. The best fitting model shows significant non-stationarity over time, with extreme precipitation events becoming less frequent. Extreme precipitation events are stronger at coastal locations, with the intensity decreasing as we head to the higher and drier areas to the North-East.