 |
| Titel |
Non-stationary extreme models and a climatic application |
| VerfasserIn |
M. Nogaj, S. Parey, D. Dacunha-Castelle |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1023-5809
|
| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 14, no. 3 ; Nr. 14, no. 3 (2007-06-25), S.305-316 |
| Datensatznummer |
250012203
|
| Publikation (Nr.) |
 copernicus.org/npg-14-305-2007.pdf |
|
|
|
|
|
| Zusammenfassung |
| In this paper, we study extreme values of non-stationary climatic phenomena.
In the usually considered stationary case, the modelling of extremes is only
based on the behaviour of the tails of the distribution of the remainder of
the data set. In the non-stationary case though, it seems reasonable to
assume that the temporal dynamics of the entire data set and that of
extremes are closely related and thus all the available information about
this link should be used in statistical studies of these events. We try to
study how centered and normalized data which are closer to stationary data
than the observation allows easier statistical analysis and to understand if
we are very far from a hypothesis stating that the extreme events of
centered and normed data follow a stationary distribution. The location and
scale parameters used for this transformation (the central field), as well
as extreme parameters obtained for the transformed data enable us to
retrieve the trends in extreme events of the initial data set. Through
non-parametric statistical methods, we thus compare a model directly built
on the extreme events and a model reconstructed from estimations of the
trends of the location and scale parameters of the entire data set and
stationary extremes obtained from the centered and normed data set. In case
of a correct reconstruction, we can clearly state that variations of the
characteristics of extremes are well explained by the central field. Through these analyses we bring arguments
to choose constant shape parameters of extreme distributions. We show that
for the frequency of the moments of high threshold excesses (or for the mean
of annual extremes), the general dynamics explains a large part of the
trends on frequency of extreme events. The conclusion is less obvious for
the amplitudes of threshold exceedances (or the variance of annual extremes)
– especially for cold temperatures, partly justified by the statistical
tools used, which require further analyses on the variability definition. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|