A new aggregation and riming discrimination algorithm based on polarimetric weather radars

Abstract. The distinction between riming and aggregation is of high relevance for model microphysics, data assimilation, and warnings of potential aircraft hazards due to the link between riming and updrafts as well as the presence of supercooled liquid water in the atmosphere. Even though the polarimetric fingerprints for aggregation and riming are qualitatively similar, we hypothesize that it is feasible to implement an area-wide discrimination algorithm based on national polarimetric weather radar networks only. Quasi-vertical profiles (QVPs) of reflectivity (ZH), differential reflectivity (ZDR), and estimated depolarization ratio (DR) are utilized to learn about the information content of each individual polarimetric variable and their combinations for riming detection. High-resolution Doppler spectra from the vertical (birdbath) scans of the C-band radar network of the German Meteorological Service serve as input and ground truth for algorithm development. Mean isolated spectra profiles (MISPs) of the Doppler velocity are used to infer regions with frozen hydrometeors falling faster than 1.5 m s−1 and accordingly associated with significant riming. Several machine learning methods have been tested to detect riming from the corresponding QVPs of polarimetric variables. The best-performing algorithm is a fine-tuned gradient-boosting model based on decision trees. The precipitation event on 14 July 2021, which led to catastrophic flooding in the Ahr valley in western Germany, was selected to validate the performance. Considering balanced accuracy, the algorithm is able to correctly predict 74 % of the observed riming features; thus, the feasibility of reliable riming detection with national radar networks has been successfully demonstrated.