Explorative Analysis of Long Time Series of Very High Resolution Spatial Rainfall

Abstract. Rainfall is often represented by a design storm with uniform intensity in urban hydrological models even though rainfall is a highly dynamic process across very small temporal and spatial scales. This study examines characteristics of high-resolution radar data (5-minute temporal resolution, 1x1 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. Both hourly and daily extremes were analysed with respect to a set of 16 descriptive variables. The spatio-temporal properties of the extreme events are explored by means of principal component analysis, cluster analysis, and linear models for these 16 variables. The PCA indicated between 5 and 9 dimensions in the extreme event characteristic data. The cluster analyses identified four rainfall types: extreme convective, convective, convective events in front systems and front system events. The stepwise regression for each variable identified independent variables that correspond well with the correlation structure identified in the clusters. This indicates that the correlation structure may prove useful in setting up a weather generator.