Articles | Volume 26, issue 23
https://doi.org/10.5194/hess-26-6137-2022
https://doi.org/10.5194/hess-26-6137-2022
Technical note
 | 
07 Dec 2022
Technical note |  | 07 Dec 2022

Technical Note: Space–time statistical quality control of extreme precipitation observations

Abbas El Hachem, Jochen Seidel, Florian Imbery, Thomas Junghänel, and András Bárdossy

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Cited articles

Bárdossy, A. and Kundzewicz, Z.: Geostatistical methods for detection of outliers in groundwater quality spatial fields, J. Hydrol., 115, 343–359, https://doi.org/10.1016/0022-1694(90)90213-H, 1990. a, b
Barnett, V. and Lewis, T.: Outliers in statistical data, John Wiley and Sons, Hoboken, NJ, ISBN 978-0-471-93094-5, 1994. a
Bayerisches Landesamt für Umwelt (lfu Bayern): Landesmessnetz Wasserstand und Abfluss, https://www.gkd.bayern.de, last access: 7 October 2020. a
Bayerisches Landesamt für Umwelt: Wasserstand und Abfluss, https://www.lfu.bayern.de/wasser/wasserstand_abfluss/index.htm (last access: 1 September 2020), 2022. a
Box, G. E. and Cox, D. R.: An analysis of transformations, J. Roy. Stat. Soc. Ser. B, 26, 211–243, 1964. a, b
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Short summary
Through this work, a methodology to identify outliers in intense precipitation data was presented. The results show the presence of several suspicious observations that strongly differ from their surroundings. Many identified outliers did not have unusually high values but disagreed with their neighboring values at the corresponding time steps. Weather radar and discharge data were used to distinguish between single events and false observations.