Bayesian classification of hydrometeors from polarimetric radars at S- and X- bands: algorithm design and experimental comparisons

Dual-polarized weather radars are capable to detect and identify different classes of hydrometeors, within stratiform and convective storms exploiting polarimetric diversity. A model- supervised Bayesian method for hydrometeor classification (BRAHC), tuned for S- and X- band, is described in this study. The critical issue of X-band radar data processing is the path attenuation correction, usually negligible at S-band. During the IHOP experiment (Oklahoma, 2002) two dual-polarized radars, at S- and X- bands, were deployed and jointly operated with closely matched scanning strategies, giving the opportunity to perform experimental comparisons between coincident measurements at different frequencies. Results of hydrometeor classification and water content estimates at S- and X- bands are discussed and the impact of path attenuation correction is quantitatively analyzed.