Spatial Reconstruction of Rain Fields From Wireless Telecommunication Networks—Scenario-Dependent Analysis of IDW-Based Algorithms

In the last decade, commercial microwave links (CMLs) have been treated as opportunistic near-ground rain sensors, and successfully used for the retrieval of 2-D near-ground rain fields in several countries. In spite of the path integration of a CML, most studies represent the rainfall measured by a CML as a single virtual rain gauge (VRG) in the center of the path. Here, we study the performance of spatial reconstruction of rain fields by an inverse distance weighting (IDW) spatial interpolation method. We compare the case where each CML is represented by a single VRG with the case where it is represented by several VRGs along its path. A synthetic rain field was produced, simplified to a single rain cell, and sampled by a synthetic CML network that was built according to statistics of actual CMLs. A Monte Carlo simulation study yielded a quantitative and specific set of metrics showing that the rain-retrieval results are scenario-dependent and can be used to design a rain-retrieval system. In particular, we show that if the rain-cell dimensions are in the order of the average length of the CMLs, using several VRG with the iterative algorithm can significantly improve the retrieval performance, whereas the performance gain is small otherwise.