Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data
Graf, Maximilian ; Chwala, Christian ; Polz, Julius ; Kunstmann, Harald
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.</p>
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