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https://doi.org/10.5194/hess-2020-42
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-42
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 12 Feb 2020

Submitted as: research article | 12 Feb 2020

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This preprint is currently under review for the journal HESS.

The use of personal weather station observation for improving precipitation estimation and interpolation

András Bárdossy, Jochen Seidel, and Abbas El Hachem András Bárdossy et al.
  • Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, D-70569 Stuttgart, Germany

Abstract. The number of personal weather stations (PWS) with data available online through the internet is increasing gradually in many parts of the world. The purpose of this study is to investigate the applicability of these data for the spatial interpolation of precipitation for high intensity events of different durations. Due to unknown errors and biases of the observations rainfall amounts of the PWS network are not considered directly. Instead, only their temporal order is assumed to be correct. The crucial step is to find the stations with informative measurements. This is done in two steps, first by selecting the locations using time series of indicators of high precipitation amounts. The remaining stations are checked whether they fit into the spatial pattern of the other stations. Thus, it is assumed that the percentiles of the PWS network are accurate. These percentiles are then translated to precipitation amounts using the distribution functions which were interpolated using the information from German National Weather Service (DWD) data only. The suggested procedure was tested for the State of Baden-Württemberg in Germany. A detailed cross validation of the interpolation was carried out for aggregated precipitation amounts of 1, 3, 6, 12 and 24 hours. For each aggregation nearly 200 intense events were evaluated. The results show that filtering the secondary observations is necessary as the interpolation error after filtering and data transformation decreases significantly. The biggest improvement is achieved for the shortest time aggregations.

András Bárdossy et al.

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András Bárdossy et al.

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Latest update: 18 Feb 2020
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Short summary
In this study, the applicability of data from private weather stations (PWS) for precipitation interpolation was investigated. Due to unknown errors and biases in these observations, a two-step filter was developed that uses indicator correlations and event based spatial precipitation patterns. The procedure was tested and cross-validated for the State of Baden-Württemberg (Germany). The biggest improvement is achieved for the shortest time aggregations.
In this study, the applicability of data from private weather stations (PWS) for precipitation...
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