More importantly, it provides valuable information about the intrinsic microphysical properties of the drop size distribution and its relations with radar parameters, which is likely to help improve radar rainfall estimations.īy using the scaling theory, Hazenberg et al. Unlike other DSD models (e.g., exponential distributions and gamma distributions ), the scaling law allows the Z– R relation to be established without any DSD shapes imposed a priori. Other scholars establish Z–R relations by relating coefficients to DSD fitting models based on a scaling law formulation. Earlier studies tend to obtain the parameters in Z– R relations via different mathematical fitting techniques, but the statistical results sometimes cause severe deviations. Both Z and R are functions of raindrop size distributions, which can be directly derived from given DSD samples. Various radar quantitative precipitation estimation (QPE) algorithms, including Z– R relations ( Z = AR b, where Z is the radar reflectivity factor and R is the rain rate), as well as polarimetric radar algorithms highly depend on surface-based DSD measurements. Over the last few decades, weather radars have been principally used to collect rainfall variability because of their good areal coverage as well as high-resolution measurements both in time and space. Investigating the response of regional rainfall events, especially extreme precipitation, will have significant implications on climate prediction. revealed the interannual variability of heavy rain in central and southern China due to the variation of large-scale environmental conditions. found an increase in both frequency and intensity of heavy rain since 1979 in the northeastern United States. According to the Global Climate Observing System (GCOS), precipitation is getting more severe with drastic changes in most of the cities worldwide. Several dual-polarization radar estimators are also established by DSD sensor data, and the R( Z H, Z DR) estimator is proven to be more accurate than traditional Z–R relations in Mei-Yu frontal rainfall, with potential applications for operational C-band polarimetric radar.Ĭhanges in the spatial and temporal patterns of climate variables associated with global warming will have an influence on regional- and catchment-scale hydrological processes. Polarimetric radar has indisputable advantages with multiparameter detection ability. Compared with statistical radar Z–A R b relations obtained by mathematical fitting techniques, the use of a DSD model fitting based on a scaling law formulation theoretically shows a significant improvement in both stratiform (33.9%) and convective (2.8%) rainfall estimations of the Mei-Yu frontal system, which indicates that using a scaling law can better reflect the DSD variations in different parts of the Mei-Yu front. The results suggest a distinct variation of DSDs in different parts of the Mei-Yu front. To improve the radar precipitation estimation in different parts of the Mei-Yu front, a scaling method was adopted to formulate the DSD model and further derive the Z– R relations. Raindrop size distribution (DSD) was collected during three typical Mei-Yu rainstorms in July 2014 using three particle size velocity (Parsivel) DSD sensors along the Mei-Yu front in Nanjing, Chuzhou, and the western Pacific, respectively. Hydrological calibration of raw weather radar rainfall estimation relies on in situ rainfall measurements.
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