Improvement of X-Band Polarization Radar Melting Layer Recognition by the Bayesian Method and ITS Impact on Hydrometeor Classification

Using melting layer (ML) and non-melting layer (NML) data observed with the X-band dual linear polarization Doppler weather radar (X-POL) in Shunyi, Beijing, the reflectivity (Z H ), differential reflectivity (Z DR ), and correlation coefficient (CC) in the ML and NML are obtained in several stable precipitation processes. The prior probability density distributions (PDDs) of the Z H , Z DR and CC are calculated first, and then the probabilities of Z H , Z DR and CC at each radar gate are determined ( P BB in the ML and P NB in the NML) by the Bayesian method. When P BB > P NB the gate belongs to the ML, and when P BB < P NB the gate belongs to the NML. The ML identification results with the Bayesian method are contrasted under the conditions of the independent PDDs and joint PDDs of the Z H , Z DR and CC. The results suggest that MLs can be identified effectively, although there are slight differences between the two methods. Because the values of the polarization parameters are similar in light rain and dry snow, it is difficult for the polarization radar to distinguish them. After using the Bayesian method to identify the ML, light rain and dry snow can be effectively separated with the X-POL observed data.