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Spatially and temporally consistent prediction of heavy precipitation from mean values

Nature Climate Change volume 2, pages 544–547 (2012)Cite this article

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Abstract

Extreme precipitation can cause flooding, result in substantial damages and have detrimental effects on ecosystems1,2. Climate adaptation must therefore account for the greatest precipitation amounts that may be expected over a certain time span3. The recurrence of extreme-to-heavy precipitation is notoriously hard to predict, yet cost–benefit estimates of mitigation and successful climate adaptation will need reliable information about percentiles for daily precipitation. Here we present a new and simple formula that relates wet-day mean precipitation to heavy precipitation, providing a method for predicting and downscaling daily precipitation statistics. We examined 32,857 daily rain-gauge records from around the world and the evaluation of the method demonstrated that wet-day precipitation percentiles can be predicted with high accuracy. Evaluations against independent data demonstrated high skill in both space and time, indicating a highly robust methodology.

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Figure 1: Map showing the locations of the 33,599 original rain gauges on which the analysis was based.
Figure 2: Maps of wet-day 95th percentile for 24-h precipitation
Figure 3: Scatter plot providing an evaluation of the predicted wet-day q0.95 values for dependent data from North America presented in Fig. 2 (red) and independent data representing rain gauges outside North America (blue).
Figure 4: A comparison between predicted (line) and observed (symbols) wet-day q0.95 time series for the longest record of 24-h precipitation in the United States (station number 305801).

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Acknowledgements

The authors acknowledge a Norwegian Research Council grant (grant number 203866), the Norwegian Meteorological Institute and the National Center for Atmospheric Research.

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Authors and Affiliations

  1. The Norwegian Meteorological Institute, Oslo 0313, Norway

    R. E. Benestad

  2. National Center for Atmospheric Research, Boulder, Colorado 80305, USA

    D. Nychka & L. O. Mearns

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  1. R. E. Benestad
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  2. D. Nychka
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  3. L. O. Mearns
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Contributions

R.E.B. contributed to project planning, implemented the R-package, carried out the experimental work and the data analysis and took charge in the writing-up process; D.N. took part in project planning, interpretation of results and provided the statistical and gridding expertise; L.O.M. participated in the project planing and discussion of results.

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Correspondence to R. E. Benestad.

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The authors declare no competing financial interests.

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Benestad, R., Nychka, D. & Mearns, L. Spatially and temporally consistent prediction of heavy precipitation from mean values. Nature Clim Change 2, 544–547 (2012). https://doi.org/10.1038/nclimate1497

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