Frontal Rainfall Observation by a Commercial Microwave Communication Network

Abstract

Abstract 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 −0.2 and 4.2 mm h−1, respectively, and up to 0.96 with RMSE of 1.6 mm h−1 at the 10-min time interval for a 22-h intensive rainstorm with an average rain rate of 3.0 mm h−1 and a peak rain rate of 84 mm h−1. 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.