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Published： 07 July 2023 ，
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朱净萱，戴强，肖媛媛，刘超楠，李雁鹏.2023.GPM双频雷达观测的长三角区域雨滴微物理特征分析.遥感学报，27（7）： 1615-1627
Zhu J X，Dai Q，Xiao Y Y，Liu C N and Li Y P. 2023. Raindrop microphysical characteristics of the Yangtze River Delta based on GPM dual-frequency radar. National Remote Sensing Bulletin， 27（7）：1615-1627
雨滴谱（DSD）描述了降雨过程中雨滴数量随雨滴直径的分布情况，能够有效反映雨滴的微物理特征。基于DSD推导出的雷达反射率因子—降雨强度（
Z-R
）公式和单位降雨动能—降雨强度（
KE-R
）公式是雷达定量降水估计和土壤侵蚀评估的关键因素。目前，DSD获取普遍借助单点测量的地面雨滴谱仪，难以反映大尺度雨滴微物理过程的空间差异。GPM主卫星搭载的双频降水雷达（DPR）可以接收两个不同波段的雷达回波，能够获取更多雨滴信息，为空间三维DSD参数反演提供了可能。本文基于GPM-DRP的DSD反演信息，计算降雨强度、降雨动能和雷达反射率因子等参数，构建
Z
-
R
和
KE
-
R
经验关系，并以长三角地区雨滴谱仪网络观测数据为参考，进行精度评定。结果表明，DPR雷达遥感具有很好的DSD反演性能，有望为大尺度雷达降雨估测与水土保持决策等提供新的支撑。
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 (
D
m
) and normalised intercept parameter (
N
w
) 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
D
m
at most sites is slightly larger than the measured result of the disdrometer at the same location
while DPR-derived
N
w
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.
降雨雨滴谱雷达遥感GPM降雨动能
rainfalldisdrometerradar remote sensingGPMrainfall kinetic energy
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