Combining Commercial Microwave Link and Rain Gauge Observations to Estimate Countrywide Precipitation: A Stochastic Reconstruction and Pattern Analysis Approach

Abstract

Accurate spatiotemporal precipitation quantification is a crucial prerequisite for hydrological analyses. The optimal reconstruction of the spatial distribution, that is, the rainfall patterns, is particularly challenging. In this study, we reconstructed spatial rainfall on a countrywide scale for Germany by combining commercial microwave link and rain gauge observations for a better representation of the variability and spatial structure of rainfall. We further developed and applied the Random-Mixing-Whittaker-Shannon method, enabling the stochastic reconstruction of ensembles of spatial fields via linear combinations of unconditional random fields. The pattern of rainfall objects is evaluated by three performance characteristics, that is, ensemble Structure-, Amplitude-, and Location-error. Precipitation estimates obtained are in good agreement with the gauge-adjusted weather radar product RADOLAN-RW of the German Weather Service (DWD) which was used as a reference. Compared to reconstructions by Ordinary Kriging, Random Mixing showed clear advantages in the pattern representation via a five times smaller median structure error.