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| Titel |
Inferences on weather extremes and weather-related disasters: a review of statistical methods |
| VerfasserIn |
H. Visser, A. C. Petersen |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1814-9324
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| Digitales Dokument |
URL |
| Erschienen |
In: Climate of the Past ; 8, no. 1 ; Nr. 8, no. 1 (2012-02-09), S.265-286 |
| Datensatznummer |
250005373
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| Publikation (Nr.) |
 copernicus.org/cp-8-265-2012.pdf |
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| Zusammenfassung |
The study of weather extremes and their impacts, such as weather-related
disasters, plays an important role in research of climate change. Due to the
great societal consequences of extremes – historically, now and in the
future – the peer-reviewed literature on this theme has been growing
enormously since the 1980s. Data sources have a wide origin, from
century-long climate reconstructions from tree rings to relatively short (30
to 60 yr) databases with disaster statistics and human impacts.
When scanning peer-reviewed literature on weather extremes and its impacts,
it is noticeable that many different methods are used to make inferences.
However, discussions on these methods are rare. Such discussions are
important since a particular methodological choice might substantially
influence the inferences made. A calculation of a return period of once in
500 yr, based on a normal distribution will deviate from that based on a
Gumbel distribution. And the particular choice between a linear or a
flexible trend model might influence inferences as well.
In this article, a concise overview of statistical methods applied in the
field of weather extremes and weather-related disasters is given. Methods
have been evaluated as to stationarity assumptions, the choice for specific
probability density functions (PDFs) and the availability of uncertainty
information. As for stationarity assumptions, the outcome was that good
testing is essential. Inferences on extremes may be wrong if data are
assumed stationary while they are not. The same holds for the
block-stationarity assumption. As for PDF choices it was found that often
more than one PDF shape fits to the same data. From a simulation study the
conclusion can be drawn that both the generalized extreme value (GEV)
distribution and the log-normal PDF fit very well to a variety of
indicators. The application of the normal and Gumbel distributions is more
limited. As for uncertainty, it is advisable to test conclusions on extremes
for assumptions underlying the modelling approach. Finally, it can be
concluded that the coupling of individual extremes or disasters to climate
change should be avoided. |
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