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Your search keyword '"Space-time Pareto processes"' showing total 2 results
Start Over Descriptor "Space-time Pareto processes" Topic extreme value theory
2 results on '"Space-time Pareto processes"'

1. Generalized Pareto processes for simulating space-time extreme events: an application to precipitation reanalyses.

Author

Palacios-Rodríguez, F., Toulemonde, G., Carreau, J., and Opitz, T.

Subjects
*EXTREME value theory, *DEFINITIONS, *NATURAL disasters, *EMPIRICAL research
Abstract

To better manage the risks of destructive natural disasters, impact models can be fed with simulations of extreme scenarios to study the sensitivity to temporal and spatial variability. We propose a semi-parametric stochastic framework that enables simulations of realistic spatio-temporal extreme fields using a moderate number of observed extreme space-time episodes to generate an unlimited number of extreme scenarios of any magnitude. Our framework draws sound theoretical justification from extreme value theory, building on generalized Pareto limit processes arising as limits for event magnitudes exceeding a high threshold. Specifically, we exploit asymptotic stability properties by decomposing extreme event episodes into a scalar magnitude variable (that is resampled), and an empirical profile process representing space-time variability. For illustration on hourly gridded precipitation data in Mediterranean France, we calculate various risk measures using extreme event simulations for yet unobserved magnitudes, and we highlight contrasted behavior for different definitions of the magnitude variable. [ABSTRACT FROM AUTHOR]

Published
2020
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2. Generalized Pareto processes for simulating space-time extreme events: an application to precipitation reanalyses

Author

Fátima Palacios-Rodríguez, Thomas Opitz, Julie Carreau, Gwladys Toulemonde, Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Universidad Complutense de Madrid [Madrid] (UCM), Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Biostatistique et Processus Spatiaux (BIOSP), Institut National de la Recherche Agronomique (INRA), Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Biostatistique et Processus Spatiaux (BioSP), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Littoral, Environment: MOdels and Numerics (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Extreme-value theory, Environmental Engineering, 010504 meteorology & atmospheric sciences, Risk analysis, risk analysis, 0208 environmental biotechnology, Magnitude (mathematics), Precipitation, 02 engineering and technology, precipitation, 01 natural sciences, Generalized Pareto distribution, Stochastic simulation, Econometrics, Environmental Chemistry, Safety, Risk, Reliability and Quality, Extreme value theory, space-time Pareto processes, [STAT.CO]Statistics [stat]/Computation [stat.CO], Space-time Pareto processes, 0105 earth and related environmental sciences, General Environmental Science, Water Science and Technology, Event (probability theory), [STAT.AP]Statistics [stat]/Applications [stat.AP], extreme-value theory, Scalar (physics), [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], stochastic simulation, 020801 environmental engineering, Variable (computer science), 13. Climate action, Environmental science, Spatial variability, [STAT.ME]Statistics [stat]/Methodology [stat.ME]
Abstract

To better manage the risks of destructive natural disasters, impact models can be fed with simulations of extreme scenarios to study the sensitivity to temporal and spatial variability. We propose a semi-parametric stochastic framework that enables simulations of realistic spatio-temporal extreme fields using a moderate number of observed extreme space-time episodes to generate an unlimited number of extreme scenarios of any magnitude. Our framework draws sound theoretical justification from extreme value theory, building on generalized Pareto limit processes arising as limits for event magnitudes exceeding a high threshold. Specifically, we exploit asymptotic stability properties by decomposing extreme event episodes into a scalar magnitude variable (that is resampled), and an empirical profile process representing space-time variability. For illustration on hourly gridded precipitation data in Mediterranean France, we calculate various risk measures using extreme event simulations for yet unobserved magnitudes, and we highlight contrasted behavior for different definitions of the magnitude variable.

Published
2019
Full Text
View/download PDF

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