Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 6
Articles | Volume 26, issue 6
https://doi.org/10.5194/hess-26-1631-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-1631-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 6
Research article
 | 
25 Mar 2022
Research article |  | 25 Mar 2022

Improving radar-based rainfall nowcasting by a nearest-neighbour approach – Part 1: Storm characteristics

Bora Shehu and Uwe Haberlandt

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Ayzel, G., Scheffer, T., and Heistermann, M.: RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting, Geosci. Model Dev., 13, 2631–2644, https://doi.org/10.5194/gmd-13-2631-2020, 2020. 
Bartels, H., Weigl, E., Reich, T., Lang, P., Wagner, A., Kohler, O., Gerlach, N., and MeteoSolutions GmbH: Projekt RADOLAN – Routineverfahren zur Online-Aneichung der Radarniederschlagsdaten mit Hilfe von automatischen Bodenniederschlagsstationen (Ombrometer), Zusammenfassender Abschlussbericht für die Projektlaufzeit von 1997 bis 2004, http://dwd.de (last access: 25 February 2022)​​​​​​​, 2004. 
Berenguer, M., Surcel, M., Zawadzki, I., Xue, M., and Kong, F.: The diurnal cycle of precipitation from continental radar mosaics and numerical weather prediction models. Part II: Intercomparison among numerical models and with Nowcasting, Mon. Weather Rev., 140, 2689–2705, https://doi.org/10.1175/MWR-D-11-00181.1, 2012. 
Berndt, C., Rabiei, E., and Haberlandt, U.: Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios, J. Hydrol., 508, 88–101, https://doi.org/10.1016/j.jhydrol.2013.10.028, 2014. 
Berne, A., Delrieu, G., Creutin, J. D., and Obled, C.: Temporal and spatial resolution of rainfall measurements required for urban hydrology, J. Hydrol., 299, 166–179, https://doi.org/10.1016/S0022-1694(04)00363-4, 2004. 
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In this paper we investigate whether similar storms behave similarly and whether the information obtained from past similar storms can improve storm nowcast based on radar data. Here a nearest-neighbour approach is employed to first identify similar storms and later to issue either a single or an ensemble nowcast based on k most similar past storms. The results indicate that the information obtained from similar storms can reduce the errors considerably, especially for convective storm nowcast.
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