Polarimetric Radar Signatures and Performance of Various Radar Rainfall Estimators during an Extreme Precipitation Event over the Thousand-Island Lake Area in Eastern China

Yabin Gou,Haonan Chen,Jiafeng Zheng

doi:10.3390/rs11202335

Abstract

Polarimetric radar provides more choices and advantages for quantitative precipitation estimation (QPE) than single-polarization radar. Utilizing the C-band polarimetric radar in Hangzhou, China, six radar QPE estimators based on the horizontal reflectivity (ZH), specific attenuation (AH), specific differential phase (KDP), and double parameters that further integrate the differential reflectivity (ZDR), namely, R(ZH, ZDR), R(KDP, ZDR), and R(AH, ZDR), are investigated for an extreme precipitation event that occurred in Eastern China on 1 June 2016. These radar QPE estimators are respectively evaluated and compared with a local rain gauge network and drop size distribution data observed by two disdrometers. The results show that (i) although R(AH, ZDR) underestimates in the light rain scenario, it performs the best among all radar QPE estimators according to the normalized mean error; (ii) the optimal radar rainfall relationship and consistency between radar measurements aloft and their surface counterparts are both required to obtain accurate rainfall estimates close to the ground. The contamination from melting layer on AH and KDP can make R(AH), R(AH, ZDR), R(KDP), and R(KDP, ZDR) less effective than R(ZH) and R(ZH,ZDR). Instead, adjustments of the α coefficient can partly reduce such impact and hence render a superior AH–based rainfall estimator; (iii) each radar QPE estimator may outperform others during some time intervals featured by particular rainfall characteristics, but they all tend to underestimate rainfall if radar fails to capture the rapid development of rainstorms.

Highlights

Polarimetric radar measurements, including horizontal reflectivity (ZH), radial velocity (Vr), differential reflectivity (ZDR), the copolar correlation coefficient, the differential propagation phase (ΦDP), and the specific differential phase (KDP), have the potential to be used in the areas of severe weather/tornado warnings, cloud microphysics, and rainfall estimation/forecasting, which are all important modern meteorological and hydrological applications [1,2,3,4,5,6,7,8,9]
Some experimental or operational X-band polarimetric radar networks have been deployed near metropolitan urban areas to monitor small-scale severe weather systems [2,3,4,5], which makes the synthesis of polarimetric radars for severe weather diagnosis, warning, and decision-making operations more important than ever
The national multi-radar quantitative precipitation estimation system in the United States and Canada [12] is based on ZH, which has been recently refined by incorporating different Z–R relationships that vary based on climatology

Summary

Introduction

Polarimetric radar measurements, including horizontal reflectivity (ZH), radial velocity (Vr), differential reflectivity (ZDR), the copolar correlation coefficient (ρHV), the differential propagation phase (ΦDP), and the specific differential phase (KDP), have the potential to be used in the areas of severe weather/tornado warnings, cloud microphysics, and rainfall estimation/forecasting, which are all important modern meteorological and hydrological applications [1,2,3,4,5,6,7,8,9]. Some experimental or operational X-band polarimetric radar networks have been deployed near metropolitan urban areas to monitor small-scale severe weather systems [2,3,4,5], which makes the synthesis of polarimetric radars for severe weather diagnosis, warning, and decision-making operations more important than ever Among these operations, radar quantitative precipitation estimation (QPE) plays an indispensable role, and it is a challenging task to obtain accurate structure of the rain rate (R) field [9,11] for flood or mudslide warnings in mountainous areas and waterlogging prevention, especially in densely populated urban areas [3,4,5]. Radar-estimated rainfall has to approximate the gauge measurements, and any uncertainty in gauge measurements may degrade the practical performance of fitted Z–R relationships

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Results

Conclusion