Rainfall Estimation from Polarimetric S-Band Radar Measurements: Validation of a Neural Network Approach

Abstract

Abstract A procedure for the estimation of rainfall rate, capitalizing on a radar-based raindrop size distribution (RSD) parameter retrieval and neural network (NN) inversion techniques, is validated using an extensive and quality-controlled archive. The RSD retrieval algorithm utilizes polarimetric variables measured by the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), through an ad hoc regularized neural network method. Evaluation of rainfall estimation from the NN-based method is accomplished using a large radar data and surface gauge observation dataset collected in central Oklahoma during the multiyear Joint Polarization Experiment (JPOLE) field campaign. Point estimates of hourly rainfall accumulations and instantaneous rainfall rates from NN-based and parametric polarimetric rainfall relations are compared with dense surface gauge observations. Rainfall accumulations from RSD retrieval-based methods are shown to be sensitive to the choice of a raindrop fall speed model. To minimize the impact of this choice, a new “direct” neural network approach is tested. Proposed NN-based approaches exhibit bias and root-mean-square error characteristics comparable with those obtained from parametric relations, specifically optimized for the JPOLE dataset, indicating an appealing generalization capability with respect to the climatological context. All tested polarimetric relations are shown to be sensitive to hail contamination as inferred from the results of automatic polarimetric echo classification and available storm reports.