A framework for merging precipitation retrievals and gauge-based observations based on a novel concept namely virtual gauges

Abstract

The merging of multi-source precipitation retrievals (PRs) and gauge-based observations (GO) provides a new opportunity for precipitation field estimation. However, the uncertainties associated with PRs may become relatively more evident when the gauge network better captures the spatial distribution of the rainfall fields. To dynamically balance the utilisation of PRs, we propose a merging framework based on a novel concept; namely, the virtual gauge, where the grid cells utilising PRs are regarded as virtual gauges, and the framework is henceforth referred to as VG. The main steps were as follows: i) determine the locations of virtual gauges from multi-source PRs, ii) estimate rainfall at virtual gauges using a basic merging method, and iii) spatially interpolate using both actual and virtual gauges. Accordingly, the case study employed random forest and inverse distance weight as the basic merging and interpolation methods of VG. Evaluation using real world data over a region where nearly each 0.1° grid cell contains a ground gauge indicates that VG improves around 7–11% over its basic methods and improves around 32–240% over its inputting PRs. VG performed better and was more stable than the basic methods under various gauge densities, rainfall intensities, and rainfall distributions. The results showed that VG could supplement the spatial information of rainfall fields missed by the gauge network and reduce interference caused by the uncertainties of PRs. Overall, the framework integrates the advantages of existing merging and spatial interpolation methods by adjusting the number, locations, and rainfall estimations of virtual gauges.