EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales – Geostatistical interpolation framework

Micha Eisele1, Maximilian Graf2, Abbas El Hachem1, Jochen Seidel1, Christian Chwala2,3, Harald Kunstmann2,3, and András Bárdossy1
Micha Eisele et al.
  • 1Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, 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

Precipitation - highly variable in space and time - is the most important input for many hydrological models. As these models become more and more detailed in space and time, high-resolution input data are required. Especially for modeling and prediction in fast reacting catchments, such as urban catchment areas, a higher space-time resolution is needed than the current ground measurement networks operated by national weather services usually provide. With the increasing number and availability of opportunistic sensors such as commercial microwave links (CMLs) and personal weather stations (PWS) in recent years, new opportunities for measuring meteorological data are emerging.

We developed a geostatistical interpolation framework which allows a combination of different opportunistic sensors and their specific features and geometric properties, e.g. point and line information. In this framework, a combined kriging approach is introduced, taking into account not only the point information of a reliable primary network, e.g., from national weather services, but also the higher uncertainty of the PWS- and CML-based precipitation. The path-averaged information of the CMLs is included through a block kriging-type approach.

The methodology was applied for two 7-months periods in Germany using an hourly temporal and a 1x1 km spatial resolution. By incorporating CMLs and PWS, the Pearson correlation could be increased from 0.56 to 0.73 compared to using only primary network for interpolation. The resulting precipitation maps also provided good agreement compared to gauge adjusted radar products.

How to cite: Eisele, M., Graf, M., El Hachem, A., Seidel, J., Chwala, C., Kunstmann, H., and Bárdossy, A.: Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales – Geostatistical interpolation framework, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12415,, 2021.


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