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

Abstract

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.

Modelling extremes of spatial aggregates of precipitation using conditional methods

“…We believe this is due to the presence of multiple data generating processes in the climate model. Precipitation is typically generated by either high intensity events with localised spatial profiles, i.e., convective cells, or low intensity events with much large spatial profiles, i.e., frontal storms (Thomassen et al, 2020). In the absence of covariates to distinguish between these events in the data, we use a higher exceedance threshold to remove any frontal events; this is discussed further in Section 5.…”

Living and Prototyping Digital Twins for Urban Water Systems: Towards Multi-Purpose Value Creation Using Models and Sensors

“…For urban drainage systems, the complexity may increase because the input of rain is stochastic and temporally and spatially varying by nature and because run-off depends on past weather events, as well as many other time-varying phenomena. These present challenges to urban hydrology and rainfall-runoff modeling, which are still subject to intensive research [43][44][45]. Underground pipes are also sometimes in poor condition, resulting in increased infiltration or exfiltration.…”

An object-based climatology of precipitation systems in Sydney, Australia

“…Hitchcock et al (2021) also studied the characteristics of linear precipitation systems and their contributions in heavy rainfall over Melbourne by applying an object-based approach over a weather radar data near Melbourne. Similar object-based radar studies have been conducted in Sydney (Potts et al 2000), Southeast Queensland (Peter et al 2015) and other parts of the world such as Germany (Thomassen et al 2020), Italy (Sangiorgio and Barindelli 2020) and Spain (Rigo et al 2010). This technique has also been applied over satellite datasets to study the global climatology of different storm types (Nesbitt et al 2000;Jiang et al 2011;Wall et al 2013).…”