AUTHOR=Messer H. , Eshel A. , Habi H. V. , Sagiv S. , Zheng X. 
  
TITLE=Rain Field Retrieval by Ground-Level Sensors of Various Types
  
JOURNAL=Frontiers in Signal Processing
  
VOLUME=Volume 2 - 2022
  
YEAR=2022
  
URL=https://www.frontiersin.org/journals/signal-processing/articles/10.3389/frsip.2022.877336
  
DOI=10.3389/frsip.2022.877336
  
ISSN=2673-8198
  
ABSTRACT=Rain gauges (RGs) have been utilized as sensors for local rain monitoring dating back to ancient Greece. The use of a network of RGs for 2-D rain mapping is based on spatial interpolation that, while presenting good results in limited experimental areas, have limited scalability because of the unrealistic need to install and maintain a large quantity of sensors. Alternatively, Commercial Microwave Links (CMLs), widely spread around the globe, have proven effective as near ground opportunistic rain sensors. In this paper, we study 2-D rain-field mapping using CMLs and/or RGs from a practical as well as a theoretical point-of-view, aiming to understand their inherent performance differences. We study sensor networks of either CMLs or RGs, and also a mixed network of CMLs and RGs. We show that with proper pre-processing, the rain field retrieval performance of CMLs network is better than that of RGs. However, depending on the characteristics of the rain field, this performance gain can be negligible, especially when the rain field is smooth (relative to the topology of the sensor network). In other words, for a given network, the advantage of rain retrieval using a network of CMLs is more significant when the rain field is spotty.