Articles | Volume 25, issue 2
Hydrol. Earth Syst. Sci., 25, 583–601, 2021
Hydrol. Earth Syst. Sci., 25, 583–601, 2021
Research article
10 Feb 2021
Research article | 10 Feb 2021

The use of personal weather station observations to improve precipitation estimation and interpolation

András Bárdossy et al.

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Why our rainfall-runoff models keep underestimating the peak flows?
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Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS
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Cited articles

Ahmed, S. and de Marsily, G.: Comparison of geostatistical methods for estimating transmissivity using data transmissivity and specific capacity, Water Resour. Res., 23, 1717–1737, 1987. a
Bárdossy, A.: Interpolation of groundwater quality parameters with some values below the detection limit, Hydrol. Earth Syst. Sci., 15, 2763–2775,, 2011. a
Bárdossy, A. and Kundzewicz, Z.: Geostatistical methods for detection of outliers in groundwater quality spatial fields, J. Hydrol., 115, 343–359,, 1990. a
Bárdossy, A. and Pegram, G.: Interpolation of precipitation under topographic influence at different time scales, Water Resour. Res., 49, 4545–4565, 2013. a, b, c
Berndt, C. and Haberlandt, U.: Spatial interpolation of climate variables in Northern Germany – Influence of temporal resolution and network density, J. Hydrol.: Reg. Stud., 15, 184–202,, 2018. a
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.