We developed an open-source tool to combine weather radar and commercial microwave link data, tested on large, public datasets. Merging these measurements produced more accurate rainfall estimates than radar alone, and we show which methods work best in different situations to support improved rainfall monitoring and applications.

We describe the OpenSat4Weather public dataset, which features time series of received power from 215 TV-SAT satellite microwave links located in Southern France over a five-month observation period and at 1-min resolution. These data can be processed to gather indirect rainfall intensity measurements. OpenSat4Weather also includes concurrent data from operational rain gauges and weather radars, and ERA5 0°C isotherm height data, to derive the wet fraction of the satellite-to-ground path.

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.

A hand sprayer is used to deposit droplets on the radome of an antenna. Before and during the drying process, measurements of the antenna reflection coefficient are performed repeatedly over time and frequency using a vector network analyzer. Continuous drifts from the wet state back to the initial dry state are demonstrated for individual antennas and frequency ranges. The obtained insights qualify the antenna reflection coefficient to be a promising indicator of momentary radome wetness.

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.