Rainfall estimation using measurement report data from time-division long term evolution networks

Abstract

Accurate rainfall measurement is essential for flood forecasting, effective water resources management, agriculture, etc. Many traditional rainfall monitoring methods, such as rainfall gauges, weather radars, and satellites, have been developed. Recently, a rainfall retrieval algorithm based on the radio connection between one telephone tower and another has seen widespread use across countries as it does not require any deployment procedures or costs. However, because underground fibre-optic cable networks have been widely introduced and deployed between telephone towers, usage of rainfall retrieval algorithms has reduced. Measurement report (MR) data from time-division long term evolution (TD-LTE) networks for providing instructions to the user terminal has the potential to estimate rainfall owing to the impact of rainfall-based attenuation on MR data. We present an analysis of MR data collected during the local flood season over 2 months (May and August 2019) in Huizhou city, southern China. Comparisons between estimated and observed rainfall exhibit promising results. Rainfall estimation from MR data is a potential alternative/complementary rainfall estimation method, as well as having use in runoff simulations, with acceptable accuracy (>0.8); this is particularly relevant in the context of disappearing microwave links between telephone towers in cellular communication networks. The rainfall estimation method proposed herein can also be implemented in rarely gauged or ungauged areas for flood simulation.