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7522/j. issn. 1000-0534. 2018. 00096. SATP and SIFT［J］. Plateau Meteorology， 29（2）： 486-497.

Comparative Analysis of Raindrop Size Distribution Retrieval
Techniques Based on Dual Polarization Radar

1， 2
1， 2
ZENG Jing ， ZHANG Yang ， SU Debin ， DONG Yuanchang 4
1， 3
（1. College of Electronic Engineering， Chengdu University of Information Technology， Chengdu 610225， Sichuan， China；
2. State Key Laboratory of Severe Weather Meteorological Science and Technology， Chinese Academy of M
eteorological Sciences， Beijing 100081， China；
3. Key Laboratory for Atmospheric Sounding， China Meteorological Administration， Chengdu 610225， Sichuan， China；
4. Institute of Plateau Meteorology， China Meteorological Administration （CMA） / Heavy Rain and Drought-Flood
Disaster in Plateau and Basin Key Laboratory of Sichuan Province， Chengdu 610072， Sichuan， China）
Abstract： Accurate retrieval of raindrop size distributions （DSDs） based on dual-polarization radar can provide
substantial data for the study of precipitation microphysical properties on a large scale. In order to further im‐
prove DSDs retrieval accuracy， this study proposes a new double-moment normalization method based on the
sixth and seventh moments （M6M7 method）， comparing it with the third and sixth moments method （M3M6
method） and the constrained Gamma model DSD retrieval method （C-G method） from three perspectives： over‐
all results， different rainfall intensities， and different rainfall particle sizes. Utilizing data from six rainfall events
observed by dual-polarization radar and surrounding disdrometers at Heyuan station between May and June
2022， the retrieval results of each algorithm were analyzed. The results demonstrate that during light rain
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（0 mm·h <R≤5 mm·h ）， the M6M7 method exhibits the smallest parameter biases among the three methods.
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As rainfall intensity increases， biases for most parameters （except for the increase of liquid water content （LWC）
and rainfall rate （R）） remain relatively stable， with M6M7 consistently showing the lowest biases across differ‐
ent particle sizes and minimal fluctuation with the increase of particle size. Compared with M6M7 method， the
M3M6 method incorporates specific differential phase shift on propagation （K ） for retrieval. Although K is
dp
dp
noise-sensitive， it has good quality in heavy rain （R>30 mm·h ）， resulting in smaller estimation biases for
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intense rainfall events and a decreasing trend in bias with larger particle sizes （excluding LWC and R）. For mod‐
-1
-1
erate rain （5 mm·h <R≤30 mm·h ）， the C-G method shows small median deviations yet significant fluctuations
in certain parameters. With the increase of rainfall intensity and particle size， its biases shows a trend of first
decreasing and then increase， accompanied by pronounced relative bias instability. Comprehensive evaluation
results demonstrate that the M6M7 method consistently maintains median deviations approaching 0 across all
DSD parameters， while exhibiting significantly tighter error fluctuation ranges. In marked contrast， both the
M3M6 method and C-G method display substantially wider bias variability， with their error distributions span‐
ning broader numerical ranges and demonstrating less stable performance characteristics. The newly proposed
M6M7 method technique demonstrates advantages over traditional approaches， exhibiting enhanced comprehen‐
sive retrieval capabilities with regard to both accuracy and stability， particularly excelling in light-to-moderate
rainfall with consistent accuracy. The M3M6 method proves more effective for heavy rain and storms， while the
C-G method demonstrates unstable retrieval characteristics. The final section demonstrates the retrieval perfor‐
mance of the algorithm integrating both M6M7 and M3M6 methods， verifying its capability to further improve
raindrop size distribution retrieval accuracy.
Key words： raindrop size distribution； retrieval algorithm； dual-polarization radar