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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 6
Nonlin. Processes Geophys., 14, 789–797, 2007
https://doi.org/10.5194/npg-14-789-2007
© Author(s) 2007. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Special issue: Extreme Events: Nonlinear Dynamics and Time Series Analysis

Nonlin. Processes Geophys., 14, 789–797, 2007
https://doi.org/10.5194/npg-14-789-2007
© Author(s) 2007. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  05 Dec 2007

05 Dec 2007

Modeling pairwise dependencies in precipitation intensities

M. Vrac1, P. Naveau1, and P. Drobinski2 M. Vrac et al.
  • 1Laboratoire des Sciences du Climat et de l'Environnement, IPSL-CNRS, Gif-sur-Yvette, France
  • 2Service d'Aéronomie, IPSL-CNRS, Université Pierre et Marie Curie, Paris, France

Abstract. 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.

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