Radar and Rain Gauge Data Fusion Based on Disaggregation of Radar Imagery

Abstract

AbstractRemotely sensed radar data and in situ rain gauge observations provide complementary information about rainfall. Specifically, radar data inform rainfall location and extent, while rain gauges provide accurate observations of the local rain intensity and its temporal variability. Drawing on the respective strengths of these two data sources, radar and rain gauge data fusion is becoming more and more common to derive both accurate and comprehensive rain estimates. However, combined precipitation estimates are often restricted to a resolution of 1 km in space and 1 h in time. In this paper, I propose to use radar data disaggregation to merge radar and rain gauge observations at a high space‐time resolution. To this end, a stochastic rainfall model is first trained on rain gauge observations and then combined with area‐to‐point geostatistical simulations to simultaneously: (i) disaggregate radar data, (ii) honor rain gauge observations, and (iii) reproduce local rain statistics. The proposed approach is applied to summer rain data collected in a small Alpine catchment, and data fusion provides rainfall reanalysis results at a resolution of 200 m in space and 2 min in time, including remote locations where in‐situ observation was not feasible.