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Effects of sample size on estimation of rainfall extremes at high temperatures

  • High precipitation quantiles tend to rise with temperature, following the so-called Clausius–Clapeyron (CC) scaling. It is often reported that the CC-scaling relation breaks down and even reverts for very high temperatures. In our study, we investigate this reversal using observational climate data from 142 stations across Germany. One of the suggested meteorological explanations for the breakdown is limited moisture supply. Here we argue that, instead, it could simply originate from undersampling. As rainfall frequency generally decreases with higher temperatures, rainfall intensities as dictated by CC scaling are less likely to be recorded than for moderate temperatures. Empirical quantiles are conventionally estimated from order statistics via various forms of plotting position formulas. They have in common that their largest representable return period is given by the sample size. In small samples, high quantiles are underestimated accordingly. The small-sample effect is weaker, or disappears completely, when using parametricHigh precipitation quantiles tend to rise with temperature, following the so-called Clausius–Clapeyron (CC) scaling. It is often reported that the CC-scaling relation breaks down and even reverts for very high temperatures. In our study, we investigate this reversal using observational climate data from 142 stations across Germany. One of the suggested meteorological explanations for the breakdown is limited moisture supply. Here we argue that, instead, it could simply originate from undersampling. As rainfall frequency generally decreases with higher temperatures, rainfall intensities as dictated by CC scaling are less likely to be recorded than for moderate temperatures. Empirical quantiles are conventionally estimated from order statistics via various forms of plotting position formulas. They have in common that their largest representable return period is given by the sample size. In small samples, high quantiles are underestimated accordingly. The small-sample effect is weaker, or disappears completely, when using parametric quantile estimates from a generalized Pareto distribution (GPD) fitted with L moments. For those, we obtain quantiles of rainfall intensities that continue to rise with temperature.show moreshow less

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Metadaten
Author:Berry Boessenkool, Gerd Brüger, Maik HeistermannORCiDGND
DOI:https://doi.org/10.5194/nhess-17-1623-2017
ISSN:1561-8633
Parent Title (English):Natural hazards and earth system sciences
Publisher:Copernicus
Place of publication:Göttingen
Document Type:Article
Language:English
Date of first Publication:2017/09/25
Year of Completion:2017
Publishing Institution:Universität Potsdam
Release Date:2017/11/17
Volume:17
Issue:9
First Page:1623
Last Page:1629
Funder:Universität Potsdam, Publikationsfonds
Grant Number:PA 2017_62
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Erd- und Umweltwissenschaften
Dewey Decimal Classification:9 Geschichte und Geografie / 91 Geografie, Reisen / 910 Geografie, Reisen
Peer Review:Referiert
Grantor:Publikationsfonds der Universität Potsdam
Publication Way:Open Access
Licence (English):License LogoCreative Commons - Attribution 3.0 unported
Notes extern:Zweitveröffentlichung in der Schriftenreihe Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe ; 396