Radar beam broadening and QPE performance of radar polarimetric algorithms

Weather radar offers a unique mean of characterizing rainfall variability over a range of scales and with the space-time resolutions required for a large variety of hydrological applications based on monitoring of precipitation over a catchment area. Polarization diversity radar methods can reduce the effect on radar precipitation estimates caused by drop size variability, allowing progress on radar rainfall estimation and on its hydro-meteorological applications. Shape and size distribution of raindrops govern characteristics of polarization diversity measurements. Their effects, are embodied in dual-polarization radar measurements of the reflectivity factor (usually at horizontal polarization, Zh), differential reflectivity (Zdr, ratio between reflectivities at horizontal and vertical polarizations), and specific differential phase shift (Kdp, the difference between the phases of the radar signals at orthogonal polarizations per unit distance). Based on the above three measurements, a number of algorithms have been developed to estimate rainfall. These algorithms were derived assuming equilibrium raindrop shapes determined by surface tension forces and hydrostatic and aerodynamic pressures due to airflow around the raindrop. In the literature, several shape–size relationships have been proposed to describe the shape of a raindrop and their impact on radar rainfall algorithm have been analyzed. Radar measurements are always affected by errors arising from different sources of uncertainty that result in a misrepresentation of the rainfall field in the spatial and temporal domains. From an operational point of view, errors and uncertainty in radar rainfall estimates result both from errors in basic polarimetric measurements and from the process of retrieving the rainfall estimation falling to the ground using relationships based on the above three radar measurements. The main causes of errors in the translation from radar measurements to rainfall can be approximately classified into three categories (Zawadzki 1984): a) random errors related to radar signal noise, statistical sampling variability, advection and drop sorting, drop size distribution, coalescence, break-up, and evaporation; b) systematic errors that could arise from biases in azimuth and elevation angles, calibration biases, dry and wet radome effects, ground clutter, partial beam blocking, and propagation effects; and c) range-dependent errors, which are mainly caused by beam width, beam tilting, Earth curvature, beam broadening with distance, vertical variation in the reflectivity profile, and sampling of precipitation at increasing altitude. In this context, the presence of a bright band as well as a complex topography can further increase errors affecting radar rain measurements. The errors listed in category c) are mainly determined by the characteristics of the antenna, which define the intrinsic features of the radar beam and propagation geometry. As the distance from the radar site increases, the radar sampling volume increases with the square of the range. Precipitation intensities often vary widely on small scales and, consequently, this progressive beam broadening makes it more unlikely that a radar measurement volume is homogeneously filled by hydrometeors. Under these conditions, the drop size distribution cannot be considered uniform within the radar sampling volume, and this situation will affect the corresponding rainfall radar measurements. Literature showed that for a horizontal scanning antenna the largest impact of nonuniform beam filling is on the differential phase and cross-correlation coefficient. Therefore, with the increase of gradients inside the measurement volume the radar measurements are affected in different ways and, consequently, the rainfall estimates obtained by different algorithms will be affected differently. This paper investigates the effects of beam broadening on the polarimetric measurements of rainfall using drop size distribution profiles obtained by a micro rain radar (MRR) installed in the historic center of Rome during the Special Observation Period 1 of the “Hydrological cycle in Mediterraneam Experiment” (HyMeX SOP 1), used jointly a nearby 2D videodrometer..The study leads to two important results. First, the beam-broadening effect introduces a variability in range described in terms of an increasing normalized standard error of about 20% on average due to a de-correlation between radar and ground rain rate related to the rain variability in the increasing size of the radar beam. Secondly, it is not always possible to define the best radar rainfall algorithm over the entire domain of the rain's natural variability.