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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-09-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://www.nat-hazards-earth-syst-sci.net/17/1623/2017/nhess-17-1623-2017.pdf |
| Summary: | 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
parametric quantile estimates from a generalized Pareto distribution (GPD) fitted with
<i>L</i> moments. For those, we obtain quantiles of rainfall intensities that
continue to rise with temperature. |
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| ISSN: | 1561-8633 1684-9981 |