Abstract
Hydrological calibration of raw weather radar rainfall estimation relies on in situ rainfall measurements. 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. 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. The results suggest a distinct variation of DSDs in different parts of the Mei-Yu front. Compared with statistical radar Z–ARb 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. Polarimetric radar has indisputable advantages with multiparameter detection ability. Several dual-polarization radar estimators are also established by DSD sensor data, and the R(ZH, ZDR) 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.
Highlights
Changes in the spatial and temporal patterns of climate variables associated with global warming will have an influence on regional- and catchment-scale hydrological processes [1]
Several dual-polarization radar estimators are established by during the the Parsivel size distribution (DSD) sensor data, and the R(ZH, ZDR ) 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
We derived scaling law DSD models in different parts of the Mei-Yu front by using the DSD samples measured from Parsivel2 sensors, and DSD-based relations were further derived to improve the accuracy of quantitative radar precipitation estimations
Summary
Changes in the spatial and temporal patterns of climate variables associated with global warming will have an influence on regional- and catchment-scale hydrological processes [1]. Various radar quantitative precipitation estimation (QPE) algorithms, including Z–R relations (Z = ARb , 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 [6,7,8,9,10,11]. Unlike other DSD models (e.g., exponential distributions [16] and gamma distributions [17]), the scaling law allows the Z–R relation to be established without any DSD shapes imposed a priori [18] It provides valuable information about the intrinsic microphysical properties of the drop size distribution and its relations with radar parameters [A, b], which is likely to help improve radar rainfall estimations.
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