Abstract
Abstract. 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 ZH, ρHV and the gradient of the velocity (gradV), whereas, for QVPs, combining profiles of ZH, ρHV and ZDR 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 ∼200 m when using VPs and ∼250 m when using QVPs.
Highlights
The melting level (ML) is defined as the altitude of the 0 ◦C constant temperature surface (American Meteorological Society, 2021b)
– We proposed a profile, generated from radial velocities taken at vertical incidence, which proved to be a helpful variable for the ML estimation
– We performed a numerical comparison of the vertical profiles (VPs) and quasi-vertical profiles (QVPs) of reflectivity to demonstrate the consistency of the measurements involving the elevation angle of the scans
Summary
The melting level (ML) is defined as the altitude of the 0 ◦C constant temperature surface (American Meteorological Society, 2021b). Corrections due to the BB are necessary as it generates a region of enhanced reflectivity due to the melting of hydrometeors, which cause an overestimation of rainfall rates (Cheng and Collier, 1993; Rico-Ramirez and Cluckie, 2007). In this case, the ML location is necessary to delimit the BB and apply algorithms that mitigate the effects of this error source in radar QPE (Sánchez-Diezma et al, 2000; Smyth and Illingworth, 1998; Vignal et al, 1999). Attenuation correction algorithms are applied in the rain region, and this requires knowledge of the height of the ML (Bringi et al, 2001; Islam et al, 2014; Park et al, 2005; Rico-Ramirez, 2012)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
