Project start: 01.01.2020

Duration: 3 years

Funding: DFG, GAČR

Project lead: Christian Chwala, Uwe Siart, Vojtěch Bareš

Involved scientists: Nico Blettner, Harald Kunstmann, Barbara Haese, Andreas Wagner, Martin Fencl

Project partners:
        Technical University of Munich (TUM)

        Czech Technical University in Prague (CTU)



The overall goal of SpraiLINK is to improve spatial rainfall estimates. A part of the project is dedicated to the improvement of the accuracy of rainfall estimates derived from commercial microwave link (CML) attenuation data. Via the data from a dedicated microwave transmission field experiment, and by analyzing data from short CMLs, the effect of wet antenna attenuation (WAA) will be studied in order to allow for improving compensation algorithms. Furthermore, it will be investigated how the diversity of neighboring CMLs in dense urban networks can be leveraged to reduce CML rainfall estimation errors.

SpraiLINK further aims at statistically merging radar and CML data. This part involves the extension of the stochastic spatial reconstruction method Random-Mixing to account for observational uncertainties. For CMLs these uncertainties will be estimated based on the insights gained from the WAA and diversity analysis. For weather radar data SpraiLINK aims at developing new estimation techniques of the observational uncertainty and investigating the effect of thinning out radar data for its incorporation into Random-Mixing.

The new merging methods will be applied to a unique transboundary CML and radar data set.


Figure 1 Antennas of the field experiment at the TERENO-preAlpine site in Fendt (Sep. 2020).
Figure 2: Precipitation field estimate using the Random-Mixing method with data from commercial microwave links compared to the hourly pluviometer-adjusted radar product RADOLAN-RW.