One of the major benefits of using commercial microwave links (CMLs) for rainfall estimation is the vast abundance of these opportunistic sensors across the globe. Hence, they provide the potential for the generation of rainfall maps on large spatial scales. However, so far spatial rainfall estimation has been conducted at most on national levels using single data sets. A reason for the lack of extension across borders is the fact that independent data sets can be quite heterogeneous with regard to hardware characteristics and network topologies, which makes their joint processing challenging.

The aspect of combining independent CML data sets has been addressed recently in a publication by Nico Blettner and colleagues from University of Augsburg, Karlsruhe Institute of Technology and the Czech Technical University in Prague. The researchers estimated transboundary rainfall for the border region of Germany and the Czech Republic using quite dissimilar CML data sets of the two countries. To extract rainfall from the observed signal, established and new processing routines were applied to the combined data sets. It was found that after addressing issues of data quality appropriately, consistent reconstruction of rainfall events that traverse the German-Czech border was possible. Showing that transboundary rainfall maps could be generated in this case study is promising as it brings even larger-scale (e.g., continental) rainfall maps based on CML data in closer reach.

For further information, please have a look at the publication: https://doi.org/10.1029/2023EA002869