A method for real‐time temporal disaggregation of blended radar–rain gauge precipitation fields

Yannick Barton,Urs Germann,Olivia Martius,Ioannis V Sideris

doi:10.1002/met.1843

Abstract

AbstractA fully automated quasi‐real‐time method is presented to disaggregate hourly to sub‐hourly precipitation information operationally in a blended radar–rain gauge product. The method proposes a fully automated solution to disaggregate precipitation in regions characterized by measurement errors or partial absence of auxiliary information on the temporal precipitation evolution. The solution relies on a combination of low‐pass filtered radar information and stochastically generated noise fields. A comprehensive validation of the new method is provided demonstrating higher skill compared to a uniform disaggregation in time. The method is now an integral part of CombiPrecip, the official operational code of MeteoSwiss for radar–rain gauge merging.

Highlights

Quantitative precipitation estimates on sub-hourly time scales are of central importance for several research and forecasting applications
One explanation for the non-linear behaviour of the skill scores for spatial scales above 4 km could be that these are the spatial scales down to which the statistical dilation has an effective impact on the precipitation fields. These results suggest better performance of the new disaggregation method compared to the uniform method in terms of representativeness of the spatial precipitation structures at all spatial scales, especially for the fast hours
The CombiPrecip (CPC) disaggregation method is presented in this study

Summary

| INTRODUCTION

Quantitative precipitation estimates on sub-hourly time scales are of central importance for several research and forecasting applications. Sideris et al (2014) propose to disaggregate the hourly CPC to 5 min precipitation accumulations for Switzerland While this approach is computationally inexpensive, it has one major drawback: it assumes that the temporal evolution of precipitation at each grid location is represented accurately in the auxiliary dataset (here radar precipitation estimates). The blended product CPC may generate positive precipitation accumulations in regions where the radar does not measure any precipitation, and this happens when the rain gauge measurements at the region are different from zero, in the case of limited radar visibility of from smoothing inherent to the blending interpolation scheme Such inconsistencies are only occasional, but they complicate the disaggregation process, especially in the case of unsupervised real-time applications, leading to artefacts that manifest as sharp gradients in the final sub-hourly precipitation fields. This should have a negligible effect on the verification, since the assessment of the disaggregated fields relies on crossvalidation (described hereafter)

| METHODS

| RESULTS

Findings

| CONCLUSIONS