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The most reliable areal precipitation estimation is usually generated via combinations of different measurements. Path-averaged rainfall rates can be derived from commercial microwave links (CMLs), where attenuation of the emitted radiation is strongly related to rainfall rate. CMLs can be combined with data from other rainfall measurements or can be used individually. They are available almost worldwide and often represent the only opportunity for ground-based measurement in data-scarce regions. However, deriving rainfall estimates from CML data requires extensive data processing. The separation of the attenuation time series into rainy and dry periods (rain event detection) is the most important step in this processing and has a high impact on the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time-series-based (TSB) rain event detection methods. We used data from 3748 CMLs in Germany for 4 months in the summer of 2021 and data from the two SEVIRI-derived products PC and PC-Ph. We analyzed all rain event detection methods for different rainfall intensities, differences between day and night, and their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW was used for validation. The results showed that both SEVIRI products are promising candidates for ADB rainfall detection, yielding only slightly worse results than the TSB methods, with the main advantage that the ADB method does not rely on extensive validation for different CML datasets. The main uncertainty of all methods was found for light rain. Slightly better results were obtained during the day than at night due to the reduced availability of SEVIRI channels at night. In general, the ADB methods led to improvements for CMLs performing comparatively weakly using TSB methods. Based on these results, combinations of ADB and TSB methods were developed by emphasizing their specific advantages. Compared to basic and advanced TSB methods, these combinations improved the Matthews correlation coefficient of the rain event detection from 0.49 (or 0.51) to 0.59 during the day and from 0.41 (or 0.50) to 0.55 during the night. Additionally, these combinations increased the number of true-positive classifications, especially for light rainfall compared to the TSB methods, and reduced the number of false negatives while only leading to a slight increase in false-positive classifications. Our results show that utilizing MSG SEVIRI data in CML data processing significantly increases the quality of the rain event detection step, in particular for CMLs which are challenging to process with TSB methods. While the improvement is useful even for applications in Germany, we see the main potential of using ADB methods in data-scarce regions like West Africa where extensive validation is not possible. [ABSTRACT FROM AUTHOR]

Unlike actual rainfall, the spatial extent of rainfall maps is often determined by administrative and political boundaries. Similarly, data from commercial microwave links (CMLs) is usually acquired on a national basis and exchange among countries is limited. Up to now, this has prohibited the generation of transboundary CML‐based rainfall maps despite the great extension of networks across the world. We present CML based transboundary rainfall maps for the first time, using independent CML data sets from Germany and the Czech Republic. We show that straightforward algorithms used for quality control strongly reduce anomalies in the results. We find that, after quality control, CML‐based rainfall maps can be generated via joint and consistent processing, and that these maps allow to seamlessly visualize rainfall events traversing the German‐Czech border. This demonstrates that quality control represents a crucial step for large‐scale (e.g., continental) CML‐based rainfall estimation. Plain Language Summary: Rainfall maps are usually based on gauge observations on the ground or radar. They are crucial for predicting or reconstructing flooding events. Commercial microwave links are special kinds of rainfall sensors. Their actual purpose is the signal propagation within a cellular network. However, since the signal is attenuated when it rains, they can also be exploited for rainfall estimation. To estimate rainfall from the observed attenuation requires careful data processing. Algorithms for that are usually adjusted to national data sets with their specific characteristics. In this study, we use, for the first time, two independent data sets of commercial microwave links (from Germany and the Czech Republic) with the aim of generating transboundary rainfall maps. As the data sets vary in many respects, several algorithms need to be adjusted and extended to allow processing them consistently. We show that it is possible to create meaningful rainfall maps of rain events that traverse the border between Germany and the Czech Republic. This can be considered a major step toward seamless rainfall maps on even larger, that is, continental scale. Key Points: Transboundary rainfall maps based on independent networks of commercial microwave links (CMLs) are generated for the first timeGerman and Czech data sets of CMLs differ significantly with respect to network characteristicsQuality control is important for heterogeneous data of CMLs [ABSTRACT FROM AUTHOR]