Detection of the melting level with polarimetric weather radar

Accurate estimation of the melting level (ML) is essential in radar rainfall estimation to mitigate the bright band enhancement, classify hydrometeors, correct for rain attenuation and calibrate radar measurements. This paper presents a novel and robust ML-detection algorithm based on either vertical profiles (VPs) or quasi-vertical profiles (QVPs) built from operational polarimetric weather radar scans. The algorithm depends only on data collected by the radar itself, and it is based on the combination of several polarimetric radar measurements to generate an enhanced profile with strong gradients related to the melting layer. The algorithm is applied to 1 year of rainfall events that occurred over southeast England, and the results were validated using radiosonde data. After evaluating all possible combinations of polarimetric radar measurements, the algorithm achieves the best ML detection when combining VPs of inline-formulaZ_H, inline-formulaρ_HV and the gradient of the velocity (inline-formulagradV), whereas, for QVPs, combining profiles of inline-formulaZ_H, inline-formulaρ_HV and inline-formulaZ_DR produces the best results, regardless of the type of rain event. The root mean square error in the ML detection compared to radiosonde data is inline-formula∼200 m when using VPs and inline-formula∼250 m when using QVPs.