Accurate observation of the high spatio‐temporal variability of rainfall is crucial for hydrometeorological applications. However, the existing observations from rain gauges and weather radars have individual shortcomings that can introduce considerable errors and uncertainties. A fairly new technique to get additional rainfall information is the usage of the country‐wide commercial microwave link (CML) networks for rainfall estimation by exploiting the measurements of rain‐induced attenuation along these CMLs. This technique has seen an increasing number of applications during the last years. Different methods have been developed to process the noisy raw data and to derive rainfall fields. It has been shown that CMLs can provide important line‐integrated rainfall information that complements pointwise rain gauge and spatial radar observations. There exist several limitations, though. Robustly dealing with the erratic fluctuations of the CML raw data is a challenge, in particular with the growing number of CMLs. How to correctly compensate for the biases from the effect of wet antenna attenuation for different CMLs also remains a crucial research question. Progress is additionally hampered by the lack of method intercomparisons, which in turn is hampered by restricted data sharing. Hence, collaboration is key for further advancements, also with regard to extended interaction with the CML network operators, which is a prerequisite to achieve increased data availability. In regions where rain gauges and weather radars are available, CMLs are a welcome complement. But in developing countries, which are characterized by weak technical infrastructure and which often suffer from water stress, additional rainfall information is a necessity. CMLs could play a crucial role in this respect. This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Extremes Science of Water > Methods

Abstract. Measuring rain rates over complex terrain is afflicted with large uncertainties, because rain gauges are influenced by orography and weather radars are mostly not able to look into mountain valleys. We apply a new method to estimate near surface rain rates exploiting attenuation data from commercial microwave links in the alpine region of Southern Germany. Received signal level (RSL) data are recorded minutely with small data loggers at the towers and then sent to a database server via GSM (Global System for Mobile Communications). Due to the large RSL fluctuations in periods without rain, the determination of attenuation caused by precipitation is not straightforward. To be able to continuously process the RSL data from July 2010 to October 2010, we introduce a new method to detect wet and dry periods using spectral time series analysis. Its performance and limitations are presented, showing that the mean detection error rates of wet and dry periods can be reduced to 10 % for all five links. After, the wet/dry classification rain rates are derived from the RSL and compared to rain gauge and weather radar measurements. The resulting correlations differ for different links and reach values of R 2 = 0.81 for the link-gauge comparison and R 2 = 0.85 for the link-radar comparison.

Global change has triggered several transformations, such as alterations in climate, land productivity, water resources, and atmospheric chemistry, with far reaching impacts on ecosystem functions and services. Finding solutions to climate and land cover change-driven impacts on our terrestrial environment is one of the most important scientific challenges of the 21st century, with farreaching interlinkages to the socio-economy. The setup of the German Terrestrial Environmental Observatories (TERENO) Pre-Alpine Observatory was motivated by the fact that mountain areas, such as the pre-alpine region in southern Germany, have been exposed to more intense warming compared with the global average trend and to higher frequencies of extreme hydrological events, such as droughts and intense rainfall. Scientific research questions in the TERENO Pre-Alpine Observatory focus on improved process understanding and closing of combined energy, water, C, and N cycles at site to regional scales. The main long-term objectives of the TERENO Pre-Alpine Observatory include the characterization and quantification of climate change and land cover-management effects on terrestrial hydrology and biogeochemical processes at site and regional scales by joint measuring and modeling approaches. Here we present a detailed climatic and biogeophysical characterization of the TERENO Pre-Alpine Observatory and a summary of novel scientific findings from observations and projects. Finally, we reflect on future directions of climate impact research in this particularly vulnerable region of Germany.

Abstract. Rainfall is one of the most important environmental variables. However, it is a challenge to measure it accurately over space and time. During the last decade, commercial microwave links (CMLs), operated by mobile network providers, have proven to be an additional source of rainfall information to complement traditional rainfall measurements. In this study, we present the processing and evaluation of a German-wide data set of CMLs. This data set was acquired from around 4000 CMLs distributed across Germany with a temporal resolution of 1 min. The analysis period of 1 year spans from September 2017 to August 2018. We compare and adjust existing processing schemes on this large CML data set. For the crucial step of detecting rain events in the raw attenuation time series, we are able to reduce the amount of misclassification. This was achieved by using a new approach to determine the threshold, which separates a rolling window standard deviation of the CMLs' signal into wet and dry periods. For the compensation for wet antenna attenuation, we compare a time-dependent model with a rain-rate-dependent model and show that the rain-rate-dependent model performs better for our data set. We use RADOLAN-RW, a gridded gauge-adjusted hourly radar product from the German Meteorological Service (DWD) as a precipitation reference, from which we derive the path-averaged rain rates along each CML path. Our data processing is able to handle CML data across different landscapes and seasons very well. For hourly, monthly, and seasonal rainfall sums, we found good agreement between CML-derived rainfall and the reference, except for the winter season due to non-liquid precipitation. We discuss performance measures for different subset criteria, and we show that CML-derived rainfall maps are comparable to the reference. This analysis shows that opportunistic sensing with CMLs yields rainfall information with good agreement with gauge-adjusted radar data during periods without non-liquid precipitation.