The considerable density of commercial microwave links (CMLs) in urban areas provides an unprecedented opportunity to reconstruct rain fields with high spatial resolution. The aim of this study was to investigate the potential advantages of CML measurements for rain field reconstruction. To this end, we compared the performance of equivalent sensor networks consisting of CMLs, rain gauges (RGs), or a mixture of CMLs and RGs (hybrid network) for near-ground rain field reconstruction under various hypothetical scenarios. Using simulated Gaussian-shaped rain fields and X-band radar rainfall maps as the ground truth, we synthesized CML and RG measurements, which were then used to reconstruct the rain fields. The results, in which the reconstructed rain fields were compared with the ground truth, showed that the CML and RG sensor networks perform similarly in terms of rainfall spatial distribution estimation in areas where dense CML networks are likely to exist. CMLs performed better than RGs in terms of areal rainfall estimation. With a given number of sensors, more CMLs in a hybrid network achieved better areal rainfall estimation. We also study the rain reconstruction performance when using an iterative method, which represents a link by several non-equal virtual rain gauges (VRGs), allowing rainfall intensities along a link to differ. We show that on average, in a small urban area where the density of CMLs is high, representing CMLs by a single VRG is sufficient for rainfall field reconstruction and there is no need to convert a link to multiple VRGs.
Bibliographical noteFunding Information:
This research was funded by grants from the Fundamental Research Funds for the Central Universities ( B200203052 ), grants from the National Natural Science Foundation of China ( 51879068 , 51809072 ), a grant from the Postgraduate Research & Practice Innovation Program of Jiangsu Province ( KYCX20_0463 ) and a grant from Hydrological Bureau of Jiangxi Province ( 820053316 ), a grant from Council for Higher Education of Israel's PhD Sandwich Scholarship Program . The authors thank Ericsson for sharing the CML data. Cordial thanks are also extended to the Editor-in-Chief, Professor Zhiyuan Cong; the Associate Editor; Dr. Adam Eshel; and anonymous referees for their valuable comments which greatly improved the quality of the paper.
- Commercial microwave links
- High spatial resolution rainfall
- Path-integrated data
- Rainfall spatial estimation
- Urban hydrology