Welcome to the "AssimilEx"'s home page, a three-year ANR project that focuses on developing new statistical models to improve the assimilation of extreme events in geosciences. This project is sponsored by the French agency ANR (Agence Nationale de la Recherche) and it has four members:

Frédéric Chevallier
Laboratoire des Sciences du Climat et de l'Environnement
Anne-Laure Fougères
Université Paris X Nanterre, MODAL'X
Armelle Guillou
Institut de Recherche Mathématique Avancée de Strasbourg
Philippe Naveau (PI)
Laboratoire des Sciences du Climat et de l'Environnement

Latest News

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June 23rd-26th 2008 - ANR AssimilEx Lecture & Workshop on Extremes :
  Statistical modeling of extremes in data assimilation and filtering approaches







The AssimilEx project summary

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Loss of life and economic damage from extreme weather and climate events has been recurrent in human history. Although the mean behaviour of most climatic processes is well understood, the statistical modelling of extreme events in time and space remains a difficult mathematical challenge. This is mainly due to the intrinsic rarity of extreme events, their non-Gaussian amplitudes and the different spatio-temporal scales involved.
In this inter-disciplinary project (mathematics and geosciences), we propose to develop new statistical models for one important geophysical research topic: data assimilation of extreme events. The fundamental problem of data assimilation may be simply stated as follows: given the state of the atmosphere at one time, what is the state of the atmosphere at a later time if one knows the observational data with the underlying dynamical principles governing the system under observation? Mathematically, this corresponds to a state-space formulation in which the state equation drives the dynamics of the system and the observational equation integrates measurements with the state variables. The originality of this project is to combine the expertise of climate scientists and statisticians in order to propose innovative statistical models that are capable of accurately representing the distribution of extreme events when implementing a statistical data assimilation procedure.
We aim at taking advantage of recent developments in the field of Extreme Value Theory (EVT) and to offer mathematically sound models. More precisely, we plan on focusing on maxima and consequently, to build statistical models based on the multivariate mixture extremes class proposed recently by Fougeres et al. (2005). This EVT family offers the flexibility to perform spatio-temporal extrapolation and easy interpretation by suitable choices of the mixing variables. These two characteristics are essential for data assimilation. Besides proposing these new EVT models, we will study their statistical properties, derive inference schemes and test the validity of our approach on simulated data and real applications, e.g. annual maxima precipitation and pollution peaks.
The overall benefits of the proposed activity are three fold. First, this research will lead to an enhanced understanding of climate extremes. Second, the new mathematical methods will not only allow to solve similar problems in Earth sciences, but it will also be beneficial to other fields based on applied mathematics. Extending state-space modelling techniques in a spatial-temporal EVT framework is novel and will provide new tools for spatio-temporal analysis. Finally, research collaborations between atmospheric scientists and applied mathematicians will be strengthened by providing better statistical models to the climate community, new algorithms to statisticians and directing a post-doctoral fellow at the intersection of two fields.








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Data assimilation

Extreme Value Theory


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