EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Improvement of rainfall estimates using opportunistic sensors - the example of the flood in Rhineland-Palatinate in July 2021

Micha Eisele1, András Bárdossy1, Christian Chwala2,3, Norbert Demuth4, Abbas El Hachem1, Maximilian Graf2, Harald Kunstmann2,3, and Jochen Seidel1
Micha Eisele et al.
  • 1Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, Germany (
  • 2Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany
  • 3Institute of Geography, University of Augsburg, Augsburg, Germany
  • 4State Environmental Agency, Rhineland-Palatinate, Germany


Precipitation is highly variable in space and time. Ground-based precipitation gauging networks such as those from national weather services are often not able to capture this variability. Weather radars have the potential to capture the spatio-temporal characteristics of rainfall fields but they also suffer from specific errors such as attenuation. The increasing number and availability of opportunistic sensors (OS), such as commercial microwave links (CML) and personal weather stations (PWS), provides new opportunities to improve rainfall estimates based on ground observations.

We have developed a geostatistical interpolation method that allows a combination of different opportunistic sensors and their specific features and geometric properties, e.g., point and line information. In addition, the uncertainty of the different data sets can be considered [1].

The flood event in the western provinces of Germany in July 2021 showed that both, the precipitation interpolations based on rain gauge data from the German National Weather Service and radar-based precipitation products, underestimated precipitation. We show that the additional information of OS data can improve precipitation estimates in terms of areal precipitation amounts and spatial distribution.  


[1] Graf, M., El Hachem, A., Eisele, M., Seidel, J., Chwala, C., Kunstmann, H. and Bárdossy, A.: Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales,

How to cite: Eisele, M., Bárdossy, A., Chwala, C., Demuth, N., El Hachem, A., Graf, M., Kunstmann, H., and Seidel, J.: Improvement of rainfall estimates using opportunistic sensors - the example of the flood in Rhineland-Palatinate in July 2021, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6968,, 2022.