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A Rainfall Data Intercomparison Dataset of RADKLIM, RADOLAN, and Rain Gauge Data for Germany

Author

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  • Jennifer Kreklow

    (Institute of Physical Geography and Landscape Ecology, Leibniz Universität Hannover, Schneiderberg 50, 30167 Hannover, Germany)

  • Björn Tetzlaff

    (Institute of Bio- and Geosciences IBG-3, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany)

  • Gerald Kuhnt

    (Institute of Physical Geography and Landscape Ecology, Leibniz Universität Hannover, Schneiderberg 50, 30167 Hannover, Germany)

  • Benjamin Burkhard

    (Institute of Physical Geography and Landscape Ecology, Leibniz Universität Hannover, Schneiderberg 50, 30167 Hannover, Germany
    Leibniz Centre for Agricultural Landscape Research ZALF, Eberswalder Straße 84, 15374 Müncheberg, Germany)

Abstract

Quantitative precipitation estimates (QPE) derived from weather radars provide spatially and temporally highly resolved rainfall data. However, they are also subject to systematic and random bias and various potential uncertainties and therefore require thorough quality checks before usage. The dataset described in this paper is a collection of precipitation statistics calculated from the hourly nationwide German RADKLIM and RADOLAN QPEs provided by the German Weather Service (Deutscher Wetterdienst (DWD)), which were combined with rainfall statistics derived from rain gauge data for intercomparison. Moreover, additional information on parameters that can potentially influence radar data quality, such as the height above sea level, information on wind energy plants and the distance to the next radar station, were included in the dataset. The resulting two point shapefiles are readable with all common GIS and constitutes a spatially highly resolved rainfall statistics geodataset for the period 2006 to 2017, which can be used for statistical rainfall analyses or for the derivation of model inputs. Furthermore, the publication of this data collection has the potential to benefit other users who intend to use precipitation data for any purpose in Germany and to identify the rainfall dataset that is best suited for their application by a straightforward comparison of three rainfall datasets without any tedious data processing and georeferencing.

Suggested Citation

  • Jennifer Kreklow & Björn Tetzlaff & Gerald Kuhnt & Benjamin Burkhard, 2019. "A Rainfall Data Intercomparison Dataset of RADKLIM, RADOLAN, and Rain Gauge Data for Germany," Data, MDPI, vol. 4(3), pages 1-16, August.
    • Handle: RePEc:gam:jdataj:v:4:y:2019:i:3:p:118-:d:254373
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    References listed on IDEAS

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    1. Marcus Eichhorn & Mattes Scheftelowitz & Matthias Reichmuth & Christian Lorenz & Kyriakos Louca & Alexander Schiffler & Rita Keuneke & Martin Bauschmann & Jens Ponitka & David Manske & Daniela Thrän, 2019. "Spatial Distribution of Wind Turbines, Photovoltaic Field Systems, Bioenergy, and River Hydro Power Plants in Germany," Data, MDPI, vol. 4(1), pages 1-15, February.
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    Cited by:

    1. Crescenzo Pepe & Silvia Maria Zanoli, 2024. "Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review," Energies, MDPI, vol. 17(4), pages 1-35, February.
    2. Rasoul Sarvestan & Reza Barati & Aliakbar Shamsipour & Sahar Khazaei & Manfred Kleidorfer, 2024. "Evaluation of the performance of satellite products and microphysical schemes with the aim of forecasting early flood warnings in arid and semi-arid regions (a case study of northeastern Iran)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 12401-12426, October.
    3. Polina Lemenkova, 2022. "Handling Dataset with Geophysical and Geological Variables on the Bolivian Andes by the GMT Scripts," Data, MDPI, vol. 7(6), pages 1-18, June.

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