Articles | Volume 26, issue 23
https://doi.org/10.5194/hess-26-6137-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-6137-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
07 Dec 2022
Technical note |
| 07 Dec 2022
| 07 Dec 2022
Technical Note: Space–time statistical quality control of extreme precipitation observations
Abbas El Hachem
CORRESPONDING AUTHOR
Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, 70569 Stuttgart, Germany
Jochen Seidel
Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, 70569 Stuttgart, Germany
Florian Imbery
Deutscher Wetterdienst, Offenbach, Germany
Thomas Junghänel
Deutscher Wetterdienst, Offenbach, Germany
András Bárdossy
Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, 70569 Stuttgart, Germany
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Christian Chwala, Aart Overeem, Erlend Øydvin, Louise Petersson Wårdh, Jochen Seidel, Maximilian Graf, Bas Walraven, Elia Covi, Hai Victor Habi, Martin Fencl, Lotte de Vos, Filippo Giannetti, Amy Green, Tess O’Hara, Nico Blettner, Tom Keel, Georges Schutz, Abbas El Hachem, Nicholas Illich, Julius Polz, Taoufiq Shit, Lukáš Kaleta, Damaris Zulkarnaen, and Vojtěch Bareš
EGUsphere, https://doi.org/10.5194/egusphere-2025-5438, https://doi.org/10.5194/egusphere-2025-5438, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Data from so called opportunistic sensors, devices that have not been designed to provide reliable weather data, can offer valuable rainfall information. But good data processing is crucial. For this task, we created the OpenSense software ecosystem, featuring new specialized tools built on a shared foundation. These new tools allow us to collaboratively advance research and improve the operational usage of opportunistic rainfall data to provide more accurate rainfall maps.
Abbas El Hachem, Jochen Seidel, and András Bárdossy
Hydrol. Earth Syst. Sci., 29, 1335–1357, https://doi.org/10.5194/hess-29-1335-2025, https://doi.org/10.5194/hess-29-1335-2025, 2025
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The influence of climate change on areal precipitation extremes is examined. After an upscaling of reference observations, the climate model data are corrected, and a downscaling to a finer spatial scale is done. For different temporal durations and spatial scales, areal precipitation extremes are derived. The final result indicates an increase in the expected rainfall depth compared to reference values. However, the increase varied with the duration and area size.
Abbas El Hachem, Jochen Seidel, Tess O'Hara, Roberto Villalobos Herrera, Aart Overeem, Remko Uijlenhoet, András Bárdossy, and Lotte de Vos
Hydrol. Earth Syst. Sci., 28, 4715–4731, https://doi.org/10.5194/hess-28-4715-2024, https://doi.org/10.5194/hess-28-4715-2024, 2024
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This study presents an overview of open-source quality control (QC) algorithms for rainfall data from personal weather stations (PWSs). The methodology and usability along technical and operational guidelines for using every QC algorithm are presented. All three QC algorithms are available for users to explore in the OpenSense sandbox. They were applied in a case study using PWS data from the Amsterdam region in the Netherlands. The results highlight the necessity for data quality control.
Angelika Palarz, Thomas Junghänel, Jennifer Ostermöller, Thomas Deutschländer, and Frank Kaspar
EGUsphere, https://doi.org/10.5194/egusphere-2026-1067, https://doi.org/10.5194/egusphere-2026-1067, 2026
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This study assesses heavy precipitation events (HPEs) across Germany using recently digitised and quality-controlled 5-min precipitation data. The analysis of HPEs with durations ranging from 5 minutes to 7 days reveal a pronounced duration dependence. Short-duration HPEs show predominantly positive tendencies and increasing intensities, whereas long-duration HPEs display more balanced or locally slightly decreasing intensities in recent decades.
Christian Chwala, Aart Overeem, Erlend Øydvin, Louise Petersson Wårdh, Jochen Seidel, Maximilian Graf, Bas Walraven, Elia Covi, Hai Victor Habi, Martin Fencl, Lotte de Vos, Filippo Giannetti, Amy Green, Tess O’Hara, Nico Blettner, Tom Keel, Georges Schutz, Abbas El Hachem, Nicholas Illich, Julius Polz, Taoufiq Shit, Lukáš Kaleta, Damaris Zulkarnaen, and Vojtěch Bareš
EGUsphere, https://doi.org/10.5194/egusphere-2025-5438, https://doi.org/10.5194/egusphere-2025-5438, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
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Data from so called opportunistic sensors, devices that have not been designed to provide reliable weather data, can offer valuable rainfall information. But good data processing is crucial. For this task, we created the OpenSense software ecosystem, featuring new specialized tools built on a shared foundation. These new tools allow us to collaboratively advance research and improve the operational usage of opportunistic rainfall data to provide more accurate rainfall maps.
Isabel Knerr, Karsten Friedrich, and Florian Imbery
Adv. Sci. Res., 22, 97–102, https://doi.org/10.5194/asr-22-97-2025, https://doi.org/10.5194/asr-22-97-2025, 2025
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Long-term series of precipitation data are important for e.g. the precise quantification of climate change.
Such time series should be consistent, but changes in the measurement conditions can't always be avoided. Parallel measurements are carried out at German climate reference stations in order to analyse the effects of changes in the measuring systems, e.g. when switching the device to another.
In this study, the influence of funnel heating on precipitation measurement was analysed.
Sanika Baste, Daniel Klotz, Eduardo Acuña Espinoza, Andras Bardossy, and Ralf Loritz
Hydrol. Earth Syst. Sci., 29, 5871–5891, https://doi.org/10.5194/hess-29-5871-2025, https://doi.org/10.5194/hess-29-5871-2025, 2025
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This study evaluates the extrapolation performance of long short-term memory (LSTM) networks in rainfall–runoff modeling, specifically under extreme precipitation conditions. The findings reveal that the LSTM cannot predict discharge values beyond a theoretical limit and that this limit is well below the extremity of its training data. This behavior results from the LSTM's gating structures rather than saturation of the cell states alone.
Abbas El Hachem, Jochen Seidel, and András Bárdossy
Hydrol. Earth Syst. Sci., 29, 1335–1357, https://doi.org/10.5194/hess-29-1335-2025, https://doi.org/10.5194/hess-29-1335-2025, 2025
Short summary
Short summary
The influence of climate change on areal precipitation extremes is examined. After an upscaling of reference observations, the climate model data are corrected, and a downscaling to a finer spatial scale is done. For different temporal durations and spatial scales, areal precipitation extremes are derived. The final result indicates an increase in the expected rainfall depth compared to reference values. However, the increase varied with the duration and area size.
Abbas El Hachem, Jochen Seidel, Tess O'Hara, Roberto Villalobos Herrera, Aart Overeem, Remko Uijlenhoet, András Bárdossy, and Lotte de Vos
Hydrol. Earth Syst. Sci., 28, 4715–4731, https://doi.org/10.5194/hess-28-4715-2024, https://doi.org/10.5194/hess-28-4715-2024, 2024
Short summary
Short summary
This study presents an overview of open-source quality control (QC) algorithms for rainfall data from personal weather stations (PWSs). The methodology and usability along technical and operational guidelines for using every QC algorithm are presented. All three QC algorithms are available for users to explore in the OpenSense sandbox. They were applied in a case study using PWS data from the Amsterdam region in the Netherlands. The results highlight the necessity for data quality control.
Amy C. Green, Chris Kilsby, and András Bárdossy
Hydrol. Earth Syst. Sci., 28, 4539–4558, https://doi.org/10.5194/hess-28-4539-2024, https://doi.org/10.5194/hess-28-4539-2024, 2024
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Weather radar is a crucial tool in rainfall estimation, but radar rainfall estimates are subject to many error sources, with the true rainfall field unknown. A flexible model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard processing methods. This flexible and efficient model performs well in generating realistic weather radar images visually for a large range of event types.
András Bárdossy and Faizan Anwar
Hydrol. Earth Syst. Sci., 27, 1987–2000, https://doi.org/10.5194/hess-27-1987-2023, https://doi.org/10.5194/hess-27-1987-2023, 2023
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This study demonstrates the fact that the large river flows forecasted by the models show an underestimation that is inversely related to the number of locations where precipitation is recorded, which is independent of the model. The higher the number of points where the amount of precipitation is recorded, the better the estimate of the river flows.
Dhiraj Raj Gyawali and András Bárdossy
Hydrol. Earth Syst. Sci., 26, 3055–3077, https://doi.org/10.5194/hess-26-3055-2022, https://doi.org/10.5194/hess-26-3055-2022, 2022
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In this study, different extensions of the degree-day model were calibrated on snow-cover distribution against freely available satellite snow-cover images. The calibrated models simulated the distribution very well in Baden-Württemberg (Germany) and Switzerland. In addition to reliable identification of snow cover, the melt outputs from the calibrated models were able to improve the flow simulations in different catchments in the study region.
Jieru Yan, Fei Li, András Bárdossy, and Tao Tao
Hydrol. Earth Syst. Sci., 25, 3819–3835, https://doi.org/10.5194/hess-25-3819-2021, https://doi.org/10.5194/hess-25-3819-2021, 2021
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Accurate spatial precipitation estimates are important in various fields. An approach to simulate spatial rainfall fields conditioned on radar and rain gauge data is proposed. Unlike the commonly used Kriging methods, which provide a Kriged mean field, the output of the proposed approach is an ensemble of estimates that represents the estimation uncertainty. The approach is robust to nonlinear error in radar estimates and is shown to have some advantages, especially when estimating the extremes.
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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.
Through this work, a methodology to identify outliers in intense precipitation data was...
