Two simple neural networks are trained to detect rainfall events using signal loss from commercial microwave links. Whereas existing rainfall event detection methods have focused on hourly resolution reference data, this study uses weather radar and rain gauges with 5 min and 1 min temporal resolutions, respectively. Our results show that the developed neural networks can detect rainfall events with a higher temporal precision than existing methods.

We present a novel method for classifying rain and snow by combining data from Commercial Microwave Links (CMLs) with weather radar. We compare this to a reference method using dew point temperature for precipitation type classification. Evaluations with nearby disdrometers show that CMLs improve the classification of dry snow and rainfall, outperforming the reference method.

Commercial microwave links (CMLs) can be used for rainfall retrieval. The detection of rainy periods in their attenuation time series is a crucial processing step. We investigate the usage of rainfall data from MSG SEVIRI for this task, compare this approach with existing methods, and introduce a novel combined approach. The results show certain advantages for SEVIRI-based methods, particularly for CMLs where existing methods perform poorly. Our novel combination yields the best performance.

The technology of ridged-waveguide filters provides high midband frequencies as well as the suppression of spurious passbands up until very high frequencies. Due to the concept of these filters, broad bandwidths lead to reduced filter dimensions that are very difficult to fabricate. The interdigital arrangement of resonators guarantees a more reliable fabrication of these filters. The research was done in order to supply filters for the different frequency bands in the 5G communication standard.

UAV-based antenna measurements are of particular interest for physically large antennas, where traditional antenna measurement techniques are not applicable. Precise position information is obtained by GNSS, where the receiving GNSS antenna is a critical system component. An additional antenna groundplane may be beneficial or even required, depending on the antenna design. Appropriate groundplane configurations improved the achievable position accuracy in the majority of investigated cases.