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Radar-rainfall estimation is a complex process that involves several error sources, some of which are related to the environmental context. The presence of orographic obstacles heavily affects the quality of the retrieved radar products. In relatively flat terrain conditions, dual-polarization capability has been proven either to increase the data quality or to improve the rainfall estimate. The potential benefit of using polarimetric techniques for precipitation retrieval is evaluated here using data coming from two radar systems operating in Italy under complex-orography conditions. The analysis outlines encouraging results that might open new scenarios for operational applications. Indeed, the applied rainfall algorithm employing specific differential phase mostly outperformed the examined reflectivity-based retrieval techniques except for the analyzed winter storm. In the latter case, the likely contamination by frozen or melting snow tended to degrade the performance of the examined Kdp-based rainfall algorithms. [ABSTRACT FROM AUTHOR]

Rain path attenuation correction is a challenging task for quantitative use of weather radar measurements at frequencies higher than S band. The proportionality relationship between specific attenuation αhh (specific differential attenuation αdp) and specific differential phase Kdp is the basis for simple path-integrated attenuation correction using differential phase Φdp. However, the coefficients of proportionality are known to be dependent upon temperature, on the one hand, and shape and raindrop size distribution, on the other hand. To solve this problem, a Bayesian classification scheme is proposed to empirically find the prevailing rain regime and adapt the Φdp-based method. The proposed approach herein is compared with other polarimetric techniques currently available in the literature. Several episodes observed in the Paris, France, area by the C-band dual-polarized weather radar operating in Trappes (France) are analyzed and results are discussed. [ABSTRACT FROM AUTHOR]

In this paper, a Bayesian statistical approach for supervised classification and estimation of hydrometeors, using a C-band polarimetric radar, is presented and discussed. The Bayesian Radar Algorithm for Hydrometeor Classification at C-band (BRAHCC) is supervised by a backscattering microphysical model, aimed at representing ten different hydrometeor classes in water, ice, and mixed phase. The expected error budget is evaluated by means of contingency tables on the basis of C-band radar noisy and attenuated synthetic data. Its accuracy is better than that obtained from a previously developed fuzzy logic C-band classification algorithm. As a second step of the overall retrieval algorithm, a multivariate regression is adopted to derive water content statistical estimators, exploiting simulated polarimetric radar data for each hydrometeor class. The BRAHCC methodology is then applied to a convective hail event, observed by two C-band dual-polarized radars in a network configuration. The hydrometeor classification along the line of sight, connecting the two C-band radars, is performed using the BRAHCC applied to path-attenuation-corrected data. Qualitative results are consistent with those derived from the fuzzy logic algorithm. Hydrometeor water content temporal evolution is tracked along the radar line of sight. Hail vertical occurrence is derived and compared with an empirical hail detection index applied along the radar connection line during the whole event. [ABSTRACT FROM AUTHOR]

A model-based fuzzy-logic method for hydrometeor classification using C-band polarimetric radar data is presented and discussed. Membership functions of the fuzzy-logic algorithm are designed for best fitting simulated radar signatures at C-band. Such signatures are derived for ten supervised hydrometeor classes by means of a fully polarimetric radar scattering model. The Fuzzy-logic Radar Algorithm for Hydrometeor Classification at C-band (FRAHCC) is designed to use a relatively small set of polarimetric observables, i.e., copolar reflectivity and differential reflectivity, but a version of the algorithm based on the use of specific differential phase is also numerically tested and documented. The classification methodology is applied to volume data coming from a C-band two-radar network that is located in north Italy within the Po valley. Numerical and experimental results clearly show the improvements of hydrometeor classification, which were obtained by using FRAHCC with respect to the direct use of fuzzy-logic-based algorithms that are specifically tuned for S-band radar data. Moreover, the availability of two C-band rainfall observations of the same event allowed us to implement a path-integrated attenuation correction procedure, based on either a composite radar field approach or a network-constrained variational algorithm. The impact of these correction procedures on hydrometeor classification is qualitatively discussed within the considered case study. [ABSTRACT FROM AUTHOR]

The raindrop size distribution (RSD) is a critical factor in estimating rain intensity using advanced dual-polarized weather radars. A new neural-network algorithm to estimate the RSD from S-band dual-polarized radar measurements is presented. The corresponding rain rates are then computed assuming a commonly used raindrop diameter speed relationship. Numerical simulations are used to investigate the efficiency and accuracy of this method. A stochastic model based on disdrometer measurements is used to generate realistic range profiles of the RSD parameters, while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. The error analysis, which is performed in order to evaluate the expected errors of this method, shows an improvement with respect to other methodologies described in the literature. A further sensitivity evaluation shows that the proposed technique performs fairly well even for low specific differential phase-shift values. [ABSTRACT FROM AUTHOR]