The global spread of wireless networks brings a great opportunity for their use in environmental studies. Weather, atmospheric conditions, and constituents cause propagation impairments on radio links. As such, while providing communication facilities, existing wireless communication systems can be used as a widely distributed, high-resolution atmospheric observation network, operating in real time with minimum supervision and without additional cost. Here we demonstrate how measurements of the received signal level, which are made in a cellular network, provide reliable measurements for surface rainfall. We compare the estimated rainfall intensity with radar and rain gauge measurements.

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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. <br><br> In this work we address modeling of instrumental impairments, i.e. signal variability due to antenna wetting, baseline attenuation uncertainty and digital quantization, as well as environmental ones, i.e. variability of drop size distribution along a link affecting accuracy of path-averaged rainfall measurement and spatial variability of rainfall in the link's neighborhood affecting the accuracy of rainfall estimation out of the link path. 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 21 operational communication links in 12 different locations have been compared to records of five nearby rain gauges over three rainstorm events. <br><br> The experiments show that the prediction accuracy is above 90% for temporal accumulation less than 30 min and lowers for longer accumulation intervals. Spatial variability in the vicinity of the link, baseline attenuation uncertainty and, possibly, suboptimality of wet antenna attenuation model are the major sources of link-gauge discrepancies. In addition, the dependence of the optimal coefficients of a conventional wet antenna attenuation model on spatial rainfall variability and, accordingly, link length has been shown. <br><br> 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

A novel approach for reconstruction of rainfall spatial-temporal dynamics from a wireless microwave network is presented. It employs a stochastic space-time model based on a rainfall advection model, assimilated using a Kalman filter. The technique aggregates the data in time and space along the direction of motion of the rainfall field, which is recovered from the simultaneous observation of a multitude of microwave links. The technique is applied on a standard microwave communication network used by a cellular communication system, comprising 23 microwave links, and it allows for observation of near-surface rainfall at the temporal resolutions of 1 min. The accuracy of the method is demonstrated by comparing instantaneous rainfall estimates with measurements from five rain gauges, reaching correlations of up to 0.85 at the 1-min time interval with a bias and RMSE of 20.2 and 4.2 mm h 21 , respectively, and up to 0.96 with RMSE of 1.6 mm h 21 at the 10-min time interval for a 22-h intensive rainstorm with an average rain rate of 3.0 mm h 21 and a peak rain rate of 84 mm h 21 . The results are compared with those of other spatial reconstruction techniques. The proposed dynamic rainfall reconstruction approach can be applied to larger-scale dynamic rainfall assimilation methods, enabling interpolation over data-void regions and straightforward incorporation of data from other sources, for example, rain gauge networks and radars.