Prediction of rainfall intensity measurement errors using commercial microwave communication links

Commercial microwave radio links forming cellular communication networks are known to be a valuable instrument for measuring near-surface rainfall. However, operational communication links are more uncertain relatively to the dedicated installations since their geometry and frequencies are optimized for high communication performance rather than observing rainfall. Quantification of the uncertainties for measurements that are non-optimal in the first place is essential to assure usability of the data. In this work we address modeling of environmental impairments, i.e. variability of drop size distribution along a link and spatial variability of rainfall in the link's neighborhood, as well as instrumental ones, i.e. signal variability due to antenna wetting, baseline attenuation uncertainty and digital quantization. Expressions for root mean squared error (RMSE) for estimates of path-averaged and point rainfall have been derived. To verify the RMSE expressions quantitatively, path-averaged measurements from 23 operational communication links have been compared to records of five nearby rain gauges over three rainstorm events. It has been demonstrated that the major sources of link-gauge discrepancies are spatial variability and baseline attenuation uncertainty; the former remains important for up to 120 min temporally averaged measurements. The experiments show that the predicted RMSE on the average fit the measured RMSE for various link lengths, rain rates and temporal intervals; above 90% of experimental errors for 1–60 min averages and 88% for 120 min are explained by the model. In addition, the dependence of the optimal coefficients of a conventional wet antenna attenuation model on spatial rainfall variability has been shown. The expressions for RMSE of the path-averaged rainfall estimates can be useful for integration of measurements from multiple heterogeneous links into data assimilation algorithms.