Scalable radar-driven approach with compact gradient-boosting models for gap filling in high-resolution precipitation measurements

Abstract. High-frequency precipitation records are essential for hydrological modeling, weather forecasting, and ecosystem research. Unfortunately, they usually exhibit data gaps originating from sensor malfunctions, significantly limiting their usability. We present a framework to reconstruct missing data in precipitation measurements sampled at 10 min frequency using radar-based, gauge independent, precipitation estimates as the only predictor. We fit gradient-boosting models to the statistical relationships between radar-based precipitation fields and collocated rain gauges. The obtained models allow for the filling of data gaps of arbitrary length and additionally provide confidence interval approximations. We evaluate the method using the rain gauge network of the German Weather Service (DWD), which roughly covers the entirety of Germany. The results show robust performance across diverse climatic and topographic conditions at a high level, with the coefficient of determination averaging at around 0.7. The framework is computationally very cheap, relying on a single CPU core only. This makes scaling easy and integration into operational gap filling of extensive sensor networks feasible.