Statistical Post-Processing of Forecasts for Extremes Using Bivariate Brown-Resnick Processes with an Application to Wind Gusts

2013/12/16 by Marco Oesting, Oesting, Marco, Martin Schlather +3 · 1 citation
Mathematics · #FOS: Computer and information sciences #Methodology (stat.ME) #stat.ME

paper · pdf · doi:10.48550/arxiv.1312.4584

22 pages, 4 figures

arxiv created 2015/05/29 · arxiv updated 2015/06/01

Abstract

To improve the forecasts of weather extremes, we propose a joint spatial model for the observations and the forecasts, based on a bivariate Brown-Resnick process. As the class of stationary bivariate Brown-Resnick processes is fully characterized by the class of pseudo cross-variograms, we contribute to the theorical understanding of pseudo cross-variograms refining the knowledge of the asymptotic behaviour of all their components and introducing a parsimonious, but flexible parametric model. Both findings are of interest in classical geostatistics on their own. The proposed model is applied to real observation and forecast data for extreme wind gusts at 119 stations in Northern Germany.

Cited by

• Post-processing of wind gusts from COSMO-REA6 with a spatial Bayesian hierarchical extreme value model

Related

• Estimation of Huesler-Reiss distributions and Brown-Resnick processes
• Conditional simulations of Brown-Resnick processes
• On modelling positive continuous data with spatio-temporal dependence
• Post-processing of wind gusts from COSMO-REA6 with a spatial Bayesian hierarchical extreme value model
• Multivariate post-processing of probabilistic sub-seasonal weather regime forecasts