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Raindrop microphysical characteristics of the Yangtze River Delta based on GPM dual-frequency radar

  • role： First author第一作者
  • Affiliation：

    School of Geography Science, Nanjing Normal University, Nanjing 210023

  • Email：catar1213@foxmail.com
  • Introduction：朱净萱，研究方向为雷达水文研究。E-mail： catar1213@foxmail.com
ZHU Jingxuan1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geography Science, Nanjing Normal University, Nanjing 210023

  • Email：qd_gis@163.com
  • Introduction：戴强，研究方向为遥感水文与灾害水文。E-mail： qd_gis@163.com
DAI Qiang1*,  
  • Affiliation：

    School of Geography Science, Nanjing Normal University, Nanjing 210023

XIAO Yuanyuan1,  
  • Affiliation：

    School of Geography Science, Nanjing Normal University, Nanjing 210023

LIU Chaonan1,  
  • Affiliation：

    China Meteorological Administration, Public Meteorological Service Centre, Beijing 100081

LI Yanpeng2

реферат

The raindrop size distribution (DSD) is used to describe the distribution of raindrop diameters during the rainfall process, which can effectively reflect the microphysical characteristics of raindrops. DSD-derived empirical relationships, including radar reflectivity factor-rainfall intensity (Z-R) and unit rainfall kinetic energy-rainfall intensity (KE-R), are key factors in research fields such as radar quantitative precipitation estimation and soil erosion assessment. At present, the ground disdrometer is generally used to obtain DSD directly at a given site, which is difficult to represent the spatial difference of the large-scale raindrop microphysical process. The dual-frequency precipitation radar (DPR) carried by the global precipitation measurement mission (GPM) core satellite can receive radar echoes of two different bands to obtain more raindrop information, which makes it possible to retrieve spatial three-dimensional DSD parameters. Based on the DSD surface estimations, including the mass weighted mean drop diameter (Dm) and normalised intercept parameter (Nw) of GPM-DRP in its 2ADPR product during the entire 4 years (2017—2020), this study calculated rainfall intensity, unit rainfall kinetic energy, radar reflectivity factor and other parameters of each record, constructed the empirical relationships between Z-R and KE-R in grid scale, and used the observation DSD data of 11 disdrometer stations in the Yangtze River Delta region as a reference to verify and evaluate the reliability of GPM-DPR to estimate DSD parameters and fit microphysical empirical formulas, which is useful for improving the accuracy of large-scale radar rainfall estimation and soil protection decision-making. The results showed that by comparing the DSD estimation of disdrometers and GPM-DPR, it can be found that under the same rainfall intensity class, DPR-derived Dm at most sites is slightly larger than the measured result of the disdrometer at the same location, while DPR-derived Nw is higher than that of the disdrometer. As for rainfall types, the raindrops of disdrometer are mostly stratiform rain, and only a small part is high-intensity convective rain. However, due to radar sensitivity limitation, the DPR-detected raindrops are almost completely distributed in the stratiform. For the empirical formula fitted by rainfall characteristics, the KEs derived from DPR are mainly distributed on both sides of the KE-R empirical formula by corresponding disdromters. In addition, Pearson coefficient of most stations can reach more than 0.60, and at Nantong, Jiaxing and other sites, it even exceed 0.70, which proves that DPR is suitable for inferring the empirical relationship between KE and R. It means that DPR has the ability to infer those rainfall microphysical relationships in place of disdrometers in areas where site data is scarce. What is more, the DPR results perform best when it exceeds 0.5 mm h-1, with small errors and high correlations. Overall, DPR remote sensing has good DSD inversion performance, which is expected to provide new support for large-scale radar quantitative precipitation estimation and soil retention decision-making. However, due to the characteristics of orbital scanning by spaceborne radar, DPR cannot make continuous observations of rainfall events in the same area, which makes it detect a low amount of data in a limited orbital range and limits its application ability to detect rainfall events. On one hand, in order to achieve a more accurate estimation of the rainfall microphysical characteristics, it is necessary to obtain DPR data with a longer duration. On the other hand, the DPR data can be used as a correction tool to be integrated with numerical weath.

ключеви́че слова́

rainfall;Disdrometer;radar remote sensing;GPM;Rainfall Kinetic Energy

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