Preprints
https://doi.org/10.5194/hess-2022-177
https://doi.org/10.5194/hess-2022-177
 
11 May 2022
11 May 2022
Status: this preprint is currently under review for the journal HESS.

Technical Note: Space-Time Statistical Quality Control of Extreme Precipitation Observations

Abbas El Hachem1, Jochen Seidel1, Florian Imbery2, Thomas Junghänel2, and András Bárdossy1 Abbas El Hachem et al.
  • 1Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, D-70569 Stuttgart, Germany
  • 2Deutscher Wetterdienst, Offenbach, Germany

Abstract. Precipitation extremes form the basis of many engineering design decisions. Extremes are rare events which may differ strongly from “normal” observations. Unfortunately, some of the observed extremes may be inaccurate or false. The purpose of this investigation is to present a quality check of observed extremes using space-time statistical methods. As a first step, the biggest values for each observation location and event duration are selected. For each of these the observed values of all other stations corresponding to the same time steps are collected and transformed using a Box-Cox transformation factor derived from a fitted truncated normal distribution. The value at the extreme location is estimated using the surrounding stations and the calculated spatial variogram, and this estimated value is compared to the observed extreme. If the difference exceeds the critical value of the test, the extreme is flagged as possible outlier. The same procedure is repeated for different aggregations in order to avoid singularities caused by convection. The flagged extremes are then compared to the extremes of the surrounding stations using the same procedure – interpolation and subsequent comparison of the interpolated and the observed values. Flagged extremes are subsequently compared to the corresponding radar and discharge observations and finally, implausible extremes are removed. The procedure is demonstrated using observations of sub-daily and daily temporal resolution in Germany.

Abbas El Hachem et al.

Status: open (until 06 Jul 2022)

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Abbas El Hachem et al.

Abbas El Hachem et al.

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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.