Commercial microwave links (CMLs) have become established opportunistic rainfall sensors used to estimate rainfall fields of various temporal resolutions and spatial scales. A major potential of CMLs lies in their great abundance even in sparsely gauged regions. Moreover, they provide close-to-ground measurements and can be particularly valuable in mountainous terrain where the reliability of weather radars is limited. These advantages theoretically allow for valuable rainfall products of continental scale in near real-time availability.
However, in practice, limitations to such large scale CML-based products exist due to legal and administrative burdens which hinder data exchange and lead to processing algorithms that are customized to specific data sets. Not being dedicated rainfall sensors, CMLs require careful processing and filtering algorithms to retrieve rain rates from raw signal data. These algorithms have so far been developed by individual research groups for rather homogeneous data sets stemming from single acquisition systems. Up to now rainfall products have not been based on two independent national CML data sets.
In this study we merge data sets of 3900 CMLs in Germany and 2500 CMLs in the Czech Republic that are obtained from different network operators and have distinct lengths and frequencies distributions. We develop and adjust universal processing algorithms, and calculate transboundary rainfall fields. Our focus lies on producing and analyzing maps for the mountainous border region where radar observations particularly suffer from the large measuring height above ground. We analyze a period of one month in summer 2021 which contains several rainfall events. For evaluation we compare our results with German and Czech national radar and rain gauge observations.
We find that independent CML data sets can be merged successfully. We are able to produce coherent transboundary rainfall maps, which is an important step towards rainfall products at continental scale. This study demonstrates the interoperability using independent CML data sets and identifies limitations of current custom-made preprocessing algorithms.
How to cite: Blettner, N., Fencl, M., Špačková, A., Bareš, V., Chwala, C., and Kunstmann, H.: Merging independent networks of commercial microwave links from the Czech Republic and Germany to generate transboundary rainfall fields, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-186, https://doi.org/10.5194/ems2022-186, 2022.
