Enhancing the usability of weather radar data for the statistical analysis of extreme precipitation events

Abstract

Abstract. Spatially explicit quantification on design storms is essential for flood risk assessment and planning. Due to the limited temporal data availability from weather radar data, design storms are usually estimated on the basis of rainfall records of a few precipitation stations only that have a substantially long time coverage. To achieve a regional picture, these station-based estimates are spatially interpolated, incorporating a large source of uncertainty due to the typical low station density, in particular for short event durations. In this study we present a method to estimate spatially explicit design storms with a return period of up to 100 years on the basis of statistically extended weather radar precipitation estimates, based on the ideas of regional frequency analyses and subsequent bias correction. Associated uncertainties are quantified using an ensemble-sampling approach and event-based bootstrapping. With the resulting dataset, we compile spatially explicit design storms for various return periods and event durations for the federal state of Baden Württemberg, Germany. We compare our findings with two reference datasets based on interpolated station estimates. We find that the transition in the spatial patterns of the design storms from a rather random (short-duration events, 15 min) to a more structured, orographically influenced pattern (long-duration events, 24 h) seems to be much more realistic in the weather-radar-based product. However, the absolute magnitude of the design storms, although bias-corrected, is still generally lower in the weather radar product, which should be addressed in future studies in more detail.

Long-term multi-source precipitation estimation with high resolution (RainGRS Clim)

“…Weather radar networks cover a large part of the more densely populated areas of the globe, so increasingly radar data, when supplemented with other observations, are also applied in climatological studies to provide extensive information on the multi-year variability in the precipitation field with very high spatial resolution not available with other measurement techniques (Fabry et al, 2017;Saltikoff et al, 2019a). They are also used to study the climatology of intense convective phenomena, as the high spatial resolution is particularly important in such cases (Hamidi et al, 2017;Burcea et al, 2019;Voormansik et al, 2021;Hänsler and Weiler, 2022;Piscitelli et al, 2022).…”