Compressive Sensing-Based 3-D Rain Field Tomographic Reconstruction Using Simulated Satellite Signals

As an alternative to traditional meteorological methods, rain attenuation in satellite-to-Earth microwave communication signals has been used for rainfall reconstruction in recent years. In this article, the existing 2-D rain field reconstruction problem is extended to a 3-D scenario by leveraging the low Earth orbit satellite system. A compressive sensing approach is further proposed to solve the 3-D rain field reconstruction problem. The Starlink system is used as a reference, and two synthetic rain events near the Great Barrier Reef in Australia, which are generated from the weather research and forecasting model, are used to evaluate the reconstruction performance. Simulation results show that the compressive sensing approach performs better than both the traditional least squares and the least absolute shrinkage and selection operator approaches.

Automated summary

In this article, rain attenuation in satellite-to-Earth microwave communication signals is used to reconstruct rainfall, extending the problem from 2D to 3D scenarios. A compressive sensing approach is proposed and evaluated using synthetic rain events near the Great Barrier Reef in Australia. The results show that the compressive sensing approach outperforms traditional methods.