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Modeling Pairwise Dependencies in Precipitation Intensities : Volume 14, Issue 6 (05/12/2007)

By Vrac, M.

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Book Id: WPLBN0003985737
Format Type: PDF Article :
File Size: Pages 9
Reproduction Date: 2015

Title: Modeling Pairwise Dependencies in Precipitation Intensities : Volume 14, Issue 6 (05/12/2007)  
Author: Vrac, M.
Volume: Vol. 14, Issue 6
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Drobinski, P., Naveau, P., & Vrac, M. (2007). Modeling Pairwise Dependencies in Precipitation Intensities : Volume 14, Issue 6 (05/12/2007). Retrieved from

Description: Laboratoire des Sciences du Climat et de l'Environnement, IPSL-CNRS, Gif-sur-Yvette, France. In statistics, extreme events are classically defined as maxima over a block length (e.g. annual maxima of daily precipitation) or as exceedances above a given large threshold. These definitions allow the hydrologist and the flood planner to apply the univariate Extreme Value Theory (EVT) to their time series of interest. But these strategies have two main drawbacks. Firstly, working with maxima or exceedances implies that a lot of observations (those below the chosen threshold or the maximum) are completely disregarded. Secondly, this univariate modeling does not take into account the spatial dependence. Nearby weather stations are considered independent, although their recordings can show otherwise.

To start addressing these two issues, we propose a new statistical bivariate model that takes advantages of the recent advances in multivariate EVT. Our model can be viewed as an extension of the non-homogeneous univariate mixture. The two strong points of this latter model are its capacity at modeling the entire range of precipitation (and not only the largest values) and the absence of an arbitrarily fixed large threshold to define exceedances. Here, we adapt this mixture and broaden it to the joint modeling of bivariate precipitation recordings. The performance and flexibility of this new model are illustrated on simulated and real precipitation data.

Modeling pairwise dependencies in precipitation intensities

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