Although present day weather forecast models usually cannot provide realistic
descriptions of local and particularly extreme weather conditions, they provide
reliable forecasts of the atmospheric circulation that encompasses the sub-scale
processes leading to extremes. Hence, forecasts of extreme events can only be achieved
through a combination of dynamical and statistical analysis methods, where a stable
and significant statistical model based on a-priori physical reasoning establishes
a-posterior a statistical-dynamical model between the local extremes and the large scale
circulation.
Here we present the development and application of such a statistical model calibration
(downscaling) on the basis of extreme value theory, in order to derive probabilistic estimates
for (extreme) local precipitation. Besides a semi-parametric approach that employs censored
quantile regression we use parametric extreme value distributions to derive conditional
quantile estimates. The performance of two parametric approaches is compared,
which use a Poisson point process with non-stationary parameters but a constant
threshold, and the non-stationary generalized Pareto distribution and a variable
threshold.
The downscaling applies to ERA40 reanalysis, in order to derive estimates of the
conditional quantiles of daily precipitation accumulations at more than 2000 German weather
stations. |