Frontal Rainfall Observation by a Commercial Microwave Communication Network

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 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.

Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network

“…Since 2006 various studies have shown that microwave links from operational cellular communication networks may be used for rainfall monitoring for various networks and climates (e.g. Messer et al, 2006;Leijnse et al, 2007a;Zinevich et al, 2009;Overeem et al, 2011Overeem et al, , 2013Chwala et al, 2012;Rayitsfeld et al, 2012;Bianchi et al, 2013;Doumounia et al, 2014). The ability to observe other types of precipitation, such as snow, is limited however.…”

Capitalizing on Cellular Technology—Opportunities and Challenges for Near Ground Weather Monitoring

“…More advanced algorithms have been developed by signal processing experts, on which the tempo-spatial resolution of the rainfall maps, their accuracy and their coverage have been improved by exploiting the spatial spread of the CML measurements. Different authors used different approaches, such as: an iterative approach in which variability of rain along the links is exploited [19]; a compressed sensing approach [45,46]; a model based, parametric approach; a tomographic approach [47]; and dynamic mapping [48,49].…”

Rainfall measurement from the opportunistic use of an Earth–space link in the Ku band

“…By working with operational point-to-point microwave telecommunication links, Upton et al (2005), Messer et al (2006), Leijnse et al (2007), Zinevich et al (2009), Schleiss and Berne (2010), Kaufmann and Rieckermann (2011), Wang et al (2012), Overeem et al (2013) and Fenicia et al (2012) have shown that the path-averaged rain rate can be estimated from attenuation measurements. However, ground-based microwave link attenuations are provided by local telecom operators, leading to many practical constraints (in particular, coarse precision due to quantization errors and low temporal resolution (typically 15 min)).…”

“…In the future, the use of several simultaneous links associated with tomography or assimilation methods, based on an approach similar to that of Zinevich et al (2008Zinevich et al ( , 2009 or Giulli et al (1997Giulli et al ( , 1999, could be applied to the estimation of small-scale rainfall fields, and could be helpful in hydrological applications, flash flood forecasting and weather radar calibration.…”