学术文献库

Due to complex physical phenomena, the distribution of heavy rainfall events is difficult to model spatially. Physically based numerical models can often provide physically coherent spatial patterns, but may miss some important precipitation features like heavy rainfall intensities. Measurements at ground-based weather stations, however, supply adequate rainfall intensities, but most national weather recording networks are often spatially too sparse to capture rainfall patterns adequately. To bring the best out of these two sources of information, climatologists and hydrologists have been seeking models that can efficiently merge different types of rainfall data. One inherent difficulty is to capture the appropriate multivariate dependence structure among rainfall maxima. For this purpose, multivariate extreme value theory suggests the use of a max-stable process. Such a process can be represented by a max-linear combination of independent copies of a hidden stochastic process weighted by a Poisson point process. In practice, the choice of this hidden process is non-trivial, especially if anisotropy, non-stationarity and nugget effects are present in the spatial data at hand. By coupling forecast ensemble data from the French national weather service (Météo-France) with local observations, we construct and compare different types of data driven max-stable processes that are parsimonious in parameters, easy to simulate and capable of reproducing nugget effects and spatial non-stationarities. We also compare our new method with classical approaches from spatial extreme value theory such as Brown-Resnick processes.