Data-driven distinction between convective, frontal and mixed extreme rainfall events in radar data

Abstract. This study examines characteristics of extreme events based on a high-resolution precipitation dataset (5-minute temporal resolution, 1 &times 1 km spatial resolution) over an area of 1824 km2 covering the catchment of the river Wupper, North Rhine-Westphalia, Germany. Extreme events were sampled by a Peak Over Threshold method using several sampling strategies, all based on selecting an average of three events per year. A simple identification- and tracking algorithm for rain cells based on intensity threshold and fitting of ellipsoids, is developed for the study. Extremes were selected based on maximum intensities for 15-minute, hourly and daily durations and described by a set of 17 variables. The spatio-temporal properties of the extreme events are explored by means of a principal component analysis (PCA) and a cluster analysis for these 17 variables. We found that these analyses enabled us to distinguish and characterise types of extreme events useful for urban hydrology applications. The PCA indicated between 5 and 9 dimensions in the extreme event characteristic data. The cluster analyses identified four rainfall types: convective extremes, frontal extremes, mixed very extreme events and other extreme events, the last group consisting of events that are less extreme than the other events. The result is useful for selecting events of particular interest when assessing performance of e.g. urban drainage systems.