Dynamic rainfall monitoring using microwave links

Abstract

In this work, we propose a sparsity-exploiting dynamic rainfall monitoring methodology using rain-induced attenuation measurements from microwave links. To estimate rainfall field intensity dynamically from a limited number of non-linear measurements, we exploit physical properties of the rainfall such as spatial sparsity and non-negativity along with the dynamics of rainfall intensity. We develop a dynamic state estimation algorithm, where the aforementioned spatial properties are utilized as prior information. To exploit spatial sparsity, we use a basis function to tailor the sparse representation of the rainfall intensity. The basis is selected based on some criteria for sparse reconstruction such as orthonormality and mutual coherence. The tuning parameter that controls the sparsity in the spatial rainfall distribution is dynamically updated at every correction step. The developed methodology is applied to dynamically monitor the rainfall field intensity in an area with a specified spatial resolution using less number of simulated non-linear measurements than pixels. The proposed methodology can be generalized for any dynamic field reconstruction, where the limited number of non-linear measurements are field intensities integrated over a linear path.

High‐Resolution Simulation Study Exploring the Potential of Radars, Crowdsourced Personal Weather Stations, and Commercial Microwave Links to Monitor Small‐Scale Urban Rainfall

“…This is a simplified approach that has been applied before (Overeem et al, , , ; Rios Gaona et al, ). In future research, the interpolation could be done in a more sophisticated manner, so that the entire link path is considered (e.g., Roy et al, ; Zinevich et al, , ).…”

Commercial microwave link networks for rainfall observation: Assessment of the current status and future challenges

“…More sophisticated methods which can truly account for the path integrated nature of the CML measurements use tomographic reconstruction algorithms (D'Amico et al, ; Zinevich et al, ), Bayesian assimilation (Scheidegger & Rieckermann, ), stochastic reconstruction based on Copulas (Haese et al, ) or exploit the sparsity of rainfall fields (Roy, Gishkori, & Leus, ). But even the most sophisticated method cannot reproduce small scale rain events which have not been adequately detected because they occurred in a region with only very sparse, or without, CML coverage.…”

Opportunistic remote sensing of rainfall using microwave links from cellular communication networks

“…For high-resolution rainfall mapping in urban areas on the other hand, where microwave link networks are much denser than those in rural areas and, as a consequence, propagation paths may cross each other, the mapping problem is essentially a tomographic problem rather than an interpolation problem. Techniques to tackle this challenging problem (i.e., rainfall mapping on a spatial grid that is finer than the average link length) have been proposed in the scientific literature, typically taking advantage of the sparsity of the rainfall phenomenon (Giuli et al, 1991;Roy, Gishkori, & Leus, 2016). Since the appearance of microwave link networks changes over time, the quality of rainfall maps, either based on (geostatistical) interpolation (Goldshtein, Messer, & Zinevich, 2009;Overeem et al, 2016a), on tomography (Zinevich, Alpert, & Messer, 2008), or on a form of data assimilation (Bianchi, van Leeuwen, Hogan, & Berne, 2013;Zinevich, Messer, & Alpert, 2009) will also be time-dependent.…”