Improving estimations of design storms from weather radar observations using a simplified metastatistical extreme value approach

Extreme precipitation is among the most devastating atmospheric phenomena, causing severe damage worldwide, and is likely to intensify in strength and occurrence in a warming climate. Quantifying the frequency of occurrence of short-duration extreme rainfall exceeding certain amounts is important for hydrologists and urban planners. In Germany, the official design storms are determined from long time series of stations measurements (KOSTRA DWD2010R). Stations, however, only represent a limited region and cannot provide information for ungauged areas. Weather radar networks represent an alternative to overcome these issues, but their time series currently span periods of up to a few decades. Therefore, estimating design precipitation from radar observations with traditional methods is prone to large uncertainties because only few extreme events are included in the statistical analyses. 
Recently, non-asymptotic approaches, such as the simplified metastatistical extreme value, proved promising in estimating design precipitation from short records of sub-daily data, such as the ones from weather radar archives. 
We will present the results obtained applying the simplified metastatistical extreme value approach to the 21-year time series of the German weather radar network, and compare them to traditional estimates from the KOSTRA DWD2010R and to estimates from station observations. Using the simplified metastatistical approach, we find a clear reduction in uncertainty of hourly to daily design precipitation with return periods of 5, 20 and 100 years. Last, we show that the simplified metastatistical approach is less sensitive to particularly extreme events (such as the July 2021 event in Western Germany) than traditional methods.