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Spatial extreme value analysis to project extremes of large-scale indicators for severe weather
Environmetrics ( IF 1.5 ) Pub Date : 2013-09-01 , DOI: 10.1002/env.2234
Eric Gilleland 1 , Barbara G Brown , Caspar M Ammann
Affiliation  

Concurrently high values of the maximum potential wind speed of updrafts (Wmax) and 0–6 km wind shear (Shear) have been found to represent conducive environments for severe weather, which subsequently provides a way to study severe weather in future climates. Here, we employ a model for the product of these variables (WmSh) from the National Center for Atmospheric Research/United States National Center for Environmental Prediction reanalysis over North America conditioned on their having extreme energy in the spatial field in order to project the predominant spatial patterns of WmSh. The approach is based on the Heffernan and Tawn conditional extreme value model. Results suggest that this technique estimates the spatial behavior of WmSh well, which allows for exploring possible changes in the patterns over time. While the model enables a method for inferring the uncertainty in the patterns, such analysis is difficult with the currently available inference approach. A variation of the method is also explored to investigate how this type of model might be used to qualitatively understand how the spatial patterns of WmSh correspond to extreme river flow events. A case study for river flows from three rivers in northwestern Tennessee is studied, and it is found that advection of WmSh from the Gulf of Mexico prevails while elsewhere, WmSh is generally very low during such extreme events. © 2013 The Authors. Environmetrics published by JohnWiley & Sons, Ltd.

中文翻译:

空间极值分析预测恶劣天气大尺度指标极值

同时发现上升气流的最大潜在风速 (Wmax) 和 0-6 公里风切变 (Shear) 的高值代表了恶劣天气的有利环境,这随后为研究未来气候中的恶劣天气提供了一种方法。在这里,我们使用来自美国国家大气研究中心/美国国家环境预测中心再分析的这些变量的乘积模型 (WmSh),以它们在空间场中具有极端能量为条件,以投射主要的WmSh 的空间模式。该方法基于 Heffernan 和 Tawn 条件极值模型。结果表明,该技术可以很好地估计 WmSh 的空间行为,从而可以探索模式随时间可能发生的变化。虽然该模型支持推断模式中的不确定性的方法,但使用当前可用的推断方法难以进行此类分析。还探索了该方法的一种变体,以研究如何使用这种类型的模型来定性地了解 WmSh 的空间模式如何对应于极端河流流量事件。以田纳西州西北部三条河流的河流流量为例,发现墨西哥湾的 WmSh 对流盛行,而其他地方的 WmSh 在此类极端事件中通常很低。© 2013 作者。JohnWiley & Sons, Ltd. 出版的Environmetrics 还探索了该方法的变体,以研究如何使用这种类型的模型来定性地了解 WmSh 的空间模式如何对应于极端河流流量事件。以田纳西州西北部三条河流的河流流量为例,发现墨西哥湾的 WmSh 对流盛行,而其他地方的 WmSh 在此类极端事件中通常很低。© 2013 作者。JohnWiley & Sons, Ltd. 出版的Environmetrics 还探索了该方法的变体,以研究如何使用这种类型的模型来定性地了解 WmSh 的空间模式如何对应于极端河流流量事件。以田纳西州西北部三条河流的河流流量为例,发现墨西哥湾的 WmSh 对流盛行,而其他地方的 WmSh 在此类极端事件中通常很低。© 2013 作者。JohnWiley & Sons, Ltd. 出版的Environmetrics © 2013 作者。JohnWiley & Sons, Ltd. 出版的Environmetrics © 2013 作者。JohnWiley & Sons, Ltd. 出版的Environmetrics
更新日期:2013-09-01
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