Toward a Space-Borne Radar Classification Scheme for Coalescence Dominant Precipitation

High-intensity precipitation represents a threat for several regions in the world (especially urban and mountainous areas) because it increases the risk of flood and devastation. We focus here on precipitation enhancement due to collision-coalescence processes occurring in the warm layer of a cloud observed at tropical and middle latitudes. Collision-coalescence is effective in shifting the drop size distribution towards bigger drops as the precipitation approaches the surface, increasing the water content conveyed to the ground and possibly leading to extreme rainfall. Operational radars often manifest limitations in detecting processes occurring at low atmospheric levels and, in case of coalescence enhancement, may underestimate precipitation. It is thus fundamental in order to improve quantitative precipitation estimation to identify coalescence dominant events with reliable remote observations at a global scale. In this work we present a classification scheme to detect coalescence dominant precipitating systems using observations at Ka and Ku bands from the Dual-frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) core observatory. The proposed scheme derives from a classification methodology based on polarimetric radar variables applied to ground-based radars, and aims at transferring the polarimetric signatures of coalescence dominant processes to the dual frequency capability of DPR. The GPM Ground Validation Network data archive matching NEXRAD S-band ground radar data with Ka/Ku-band DPR observations over CONUS is analyzed. Coalescence dominant events are identified using ground polarimetric observations and their signatures are studied in the DPR vertical profiles of reflectivity. In particular, the vertical slopes of reflectivity and dual frequency ratio in the liquid layer are found to be markers for coalescence development in cloud. This work fits in the scientific community commitment of improving satellite (especially GPM) precipitation estimation algorithms.