An inverse method to retrieve 3D radar reflectivity composites

Abstract

Dense radar networks offer the possibility of getting better Quantitative Precipitation Estimates (QPE) than those obtained with individual radars, as they allow increasing the coverage and improving quality of rainfall estimates in overlapping areas. Well-known sources of error such as attenuation by intense rainfall or errors associated with range can be mitigated through radar composites. Many compositing techniques are devoted to operational uses and do not exploit all the information that the network is providing. In this work an inverse method to obtain high-resolution radar reflectivity composites is presented. The method uses a model of radar sampling of the atmosphere that accounts for path attenuation and radar measurement geometry. Two significantly different rainfall situations are used to show detailed results of the proposed inverse method in comparison to other existing methodologies. A quantitative evaluation is carried out in a 12 h-event using two independent sources of information: a radar not involved in the composition process and a raingauge network. The proposed inverse method shows better performance in retrieving high reflectivity values and reproducing variability at convective scales than existing methods.