An important aspect of rainfall estimation is to accurately capture extreme events. Commercial microwave links (CMLs) can complement weather radar and rain gauge data by estimating path-averaged rainfall intensities near ground. Our aim with this paper was to investigate attenuation induced complete loss of signal (blackout) in the CML data. This effect can occur during heavy rain events and leads to missing extreme values. We analyzed 3 years of attenuation data from 4,000 CMLs in Germany and compared it to a weather radar derived attenuation climatology covering 20 years. We observed that the average CML experiences 8.5 times more blackouts than we would have expected from the radar derived climatology. Blackouts did occur more often for longer CMLs (e.g., >10 km) despite their increased dynamic range. Therefore, both the hydrometeorological community and network providers can consider our analysis to develop mitigation measures.
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.
Precipitation can impact human security significantly, and its accurate estimation in time and space is vital for various applications, including water management decisions and flash flood forecasting. Traditional observation systems like rain gauges, weather radars, and satellite instruments have individual limitations in capturing precipitation accurately and are not available in all regions of the world. Opportunistic rainfall sensors (OS) like commercial microwave links (CMLs) and personal weather stations (PWS) can provide additional rainfall information, and their numbers have surpassed the ones from traditional sensors. However, dealing with the heterogeneous quality of OS-derived precipitation information remains a scientific problem and limits their use in hydrologic applications. To address this gap, this thesis aims to develop methods for quality control, processing, and spatial reconstruction of OS data. First, data from 4000 CMLs in Germany, which are part of the cellular backhaul network, were used to derive rainfall information based on the attenuation of their microwave signal. CML processing methods were developed and optimized, leading to rainfall estimates with good quality when compared to a rain gauge-adjusted weather radar product used as a reference. To improve CML processing further, a novel method for the crucial step of rain event detection was developed using a convolutional neural network. This method improved the rainfall estimation significantly by reducing falsely classified rainfall by over 50%. With a similar performance for new CMLs and time periods, the method proved its ability to generalize to previously unseen data. CMLs may experience a total loss of signal due to high attenuation during heavy rainfall. These so-called blackouts were investigated using three years of CML and 20 years of weather radar data. Overall, only around one percent of rainfall was missed due to blackouts in the CML data. However, blackouts have to be considered in applications using CML rainfall estimates, as this one percent consisted of the most intense events. Surprisingly, longer CMLs had more blackouts, despite having a higher dynamic range to compensate for more attenuation that is caused by their length. PWS are another source of opportunistically sensed rainfall information. Data from around 20,000 PWS were evaluated individually and in combination with CML and rain gauge data in Germany. Filtering and interpolation methods were developed for these datasets, and the resulting rainfall maps were evaluated against three reference datasets covering different spatial and temporal scales. The OS-based products performed similarly well as operational radar products of the DWD, especially on local and regional scales with hourly resolution, and surpassed the quality of products using conventional rain gauges. In conclusion, this thesis demonstrates the development and evaluation of methods for filtering, processing, and combining CML and PWS data. The evaluation of the OS-based rainfall estimates proves that a quality similar to that of operational rainfall products can be achieved.
We present high-resolution rainfall maps from commercial microwave link (CML) data in the city of Ouagadougou, Burkina Faso. Rainfall was quantified based on data from 100 CMLs along unique paths and interpolated to achieve rainfall maps with a 5-min temporal and 0.55-km spatial resolution for the monsoon season of 2020. Established processing methods were combined with newly developed filtering methods, minimizing the loss of data availability. The rainfall maps were analyzed qualitatively both at a 5-min and aggregated daily scales. We observed high spatiotemporal variability on the 5-min scale that cannot be captured with any existing measurement infrastructure in West Africa. For the quantitative evaluation, only one rain gauge with a daily resolution was available. Comparing the gauge data with the corresponding CML rainfall map pixel showed a high agreement, with a Pearson correlation coefficient > 0.95 and an underestimation of the CML rainfall maps of ∼10%. Because the CMLs closest to the gauge have the largest influence on the map pixel at the gauge location, we thinned out the CML network around the rain gauge synthetically in several steps and repeated the interpolation. The performance of these rainfall maps dropped only when a radius of 5 km was reached and approximately one-half of all CMLs were removed. We further compared ERA5 and GPM IMERG data with the rain gauge and found that they had much lower correlation than data from the CML rainfall maps. This clearly highlights the large benefit that CML data can provide in the data-scarce but densely populated African cities.
Two simple feedforward neural networks (multilayer perceptrons – MLPs) are trained to detect rainfall events using signal attenuation from commercial microwave links (CMLs) as predictors and high-temporal-resolution reference data as the target. MLPGA is trained against nearby rain gauges, and MLPRA is trained against gauge-adjusted weather radar. Both MLPs were trained on 26 CMLs and tested on 843 CMLs, all located within 5 km of a rain gauge. Our results suggest that these MLPs outperform existing methods, effectively capturing the intermittent behaviour of rainfall. This study is the first to use both radar and rain gauges for training and testing CML rainfall detection. While previous studies have mainly focused on hourly reference data, our findings show that it is possible to classify rainy and dry time steps with a higher temporal resolution.
