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

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

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  • NETL
  • NSTL
  • Oxford Univ Press
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 x 1 km2, over an area of size 6,461 km2, 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.

computational statisticsextreme high-resolution precipitationI NLAspatial conditional extremesstochastic weather generators

Silius M. Vandeskog、Raphaeel Huser、Oddbjorn Bruland、Sara Martino

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Department of Mathematics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway##Norwegian Computing Center, Oslo, Norway

Statistics program, CEMSE Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

Department of Civil and Environmental Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

Department of Mathematics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

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2025

10.1093/jrsssc/qlae074

Journal of the royal statistical society, Series C. Applied statistics

Journal of the royal statistical society, Series C. Applied statistics

ISSN：0035-9254
年,卷(期)：2025.74(Pt.3)
  • 119