Space–Time Simulations of Extreme Rainfall: Why and How?

Toulemonde, Gwladys; Carreau, Julie; Guinot, Vincent

doi:10.1002/9781119706922.ch3

Mathematical Modeling of Random and Deterministic Phenomena 2020

DOI: 10.1002/9781119706922.ch3

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Space–Time Simulations of Extreme Rainfall: Why and How?

Gwladys Toulemonde¹,

Julie Carreau²,

Vincent Guinot³

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Cited by 3 publications

References 68 publications

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An alternative approach to estimate any subdaily extreme of rainfall and wind from usually available records

Pérez-Bella

Hernández

Martínez-Martínez

et al. 2021

Stoch Environ Res Risk Assess

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A wide variety of engineering applications requires the use of maximum values of rainfall intensity and wind speed related to short recording intervals, which can often only be estimated from available less exhaustive records. Given that many locations lack exhaustive climatic records that would allow accurate empirical correlations between different recording intervals to be identified, generic equations are often used to estimate these extreme values. The accuracy of these generic estimates is especially important in fields such as the study of wind-driven rain, in which both climatic variables are combined to characterise the phenomenon. This work assesses the reliability and functionality of some of these most widespread generic equations, analysing climatic datasets gathered since 2008 in 109 weather stations in Spain and the Netherlands. Considering multiple recording intervals at each location, it is verified that most of these generic estimations, used especially in the study of wind-driven rain, have functional limitations and can cause significant errors when characterising both variables for subdaily intervals and extreme conditions. Finally, an alternative approach is proposed to accurately extrapolate extreme values of both variables related to any subdaily recording interval in a functional manner and from any available records.

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An alternative approach to estimate any subdaily extreme of rainfall and wind from usually available records

Pérez-Bella

Hernández

Martínez-Martínez

et al. 2021

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

Fast generation of high-dimensional spatial extremes

Van de Vyver

2024

Weather and Climate Extremes

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Fast spatial simulation of extreme high-resolution radar precipitation data using integrated nested Laplace approximations

Vandeskog,

Huser,

Bruland

et al. 2024

Journal of the Royal Statistical Society Series C: Applied Statistics

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Aiming to deliver improved precipitation simulations for hydrological impact assessment studies, we develop a methodology for modelling and simulating high-dimensional spatial precipitation extremes, focusing on both their marginal distributions and tail dependence structures. Tail dependence is crucial for assessing the consequences of extreme precipitation events, yet most stochastic weather generators do not attempt to capture this property. The spatial distribution of precipitation occurrences is modelled with four competing models, while the spatial distribution of nonzero extreme precipitation intensities are modelled with a latent Gaussian version of the spatial conditional extremes model. Nonzero precipitation marginal distributions are modelled using latent Gaussian models with gamma and generalized Pareto likelihoods. Fast inference is achieved using integrated nested Laplace approximations. We model and simulate spatial precipitation extremes in Central Norway, using 13 years of hourly radar data with a spatial resolution of 1×1km2, over an area of size 6,461km2, to describe the behaviour of extreme precipitation over a small drainage area. Inference on this high-dimensional data set is achieved within hours, and the simulations capture the main trends of the observed precipitation well.

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Space–Time Simulations of Extreme Rainfall: Why and How?

Cited by 3 publications

References 68 publications

An alternative approach to estimate any subdaily extreme of rainfall and wind from usually available records

An alternative approach to estimate any subdaily extreme of rainfall and wind from usually available records

Fast generation of high-dimensional spatial extremes

Fast spatial simulation of extreme high-resolution radar precipitation data using integrated nested Laplace approximations

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