GPM双频雷达观测的长三角区域雨滴微物理特征分析
Raindrop microphysical characteristics of the Yangtze River Delta based on GPM dual-frequency radar
- 2023年27卷第7期 页码：1615-1627
纸质出版日期： 2023-07-07
DOI： 10.11834/jrs.20221839
扫 描 看 全 文
浏览全部资源
扫码关注微信
摘要
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。
Abstract
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.
关键词
降雨雨滴谱雷达遥感GPM降雨动能
Keywords
rainfalldisdrometerradar remote sensingGPMrainfall kinetic energy
references
An Y Y, Jin F L, Zhang Y F and Zu X M. 2008. Automatic identification methods of ground raindrop spectrum observation and image. Journal of Applied Meteorological Science, 19(2): 188-193
安英玉, 金凤岭, 张云峰, 祖雪梅. 2008. 地面雨滴谱观测的图像自动识别方法. 应用气象学报, 19(2): 188-193 [DOI: 10.3969/j.issn.1001-7313.2008.02.008http://dx.doi.org/10.3969/j.issn.1001-7313.2008.02.008]
Angulo-Martínez M, Beguería S and Kyselý J. 2016. Use of disdrometer data to evaluate the relationship of rainfall kinetic energy and intensity (KE-I). Science of the Total Environment, 568: 83-94 [DOI: 10.1016/j.scitotenv.2016.05.223http://dx.doi.org/10.1016/j.scitotenv.2016.05.223]
Atlas D and Ulbrich C W. 1977. Path- and area-integrated rainfall measurement by microwave attenuation in the 1-3 cm band. Journal of Applied Meteorology, 16(12): 1322-1331 [DOI: 10.1175/1520-0450(1977)016<1322:PAAIRM>2.0.CO;2http://dx.doi.org/10.1175/1520-0450(1977)016<1322:PAAIRM>2.0.CO;2]
Bringi V N, Chandrasekar V, Hubbert J, Gorgucci E, Randeu W L and Schoenhuber M. 2003. Raindrop size distribution in different climatic regimes from disdrometer and dual-polarized radar analysis. Journal of the Atmospheric Sciences, 60(2): 354-365 [DOI: 10.1175/1520-0469(2003)060<0354:RSDIDC>2.0.CO;2http://dx.doi.org/10.1175/1520-0469(2003)060<0354:RSDIDC>2.0.CO;2]
Dai Q and Han D W. 2014. Exploration of discrepancy between radar and gauge rainfall estimates driven by wind fields. Water Resources Research, 50(11): 8571-8588 [DOI: 10.1002/2014WR015794http://dx.doi.org/10.1002/2014WR015794]
Dai Q, Yang Q Q, Han D W, Rico-Ramirez M A and Zhang S L. 2019. Adjustment of radar-gauge rainfall discrepancy due to raindrop drift and evaporation using the weather sesearch and forecasting model and dual-polarization radar. Water Resources Research, 55(11): 9211-9233 [DOI: 10.1029/2019WR025517http://dx.doi.org/10.1029/2019WR025517]
Dai Q, Zhu J X, Zhang S L, Zhu S N, Han D W and Lv G N. 2020. Estimation of rainfall erosivity based on WRF-derived raindrop size distributions. Hydrology and Earth System Sciences, 24(11): 5407-5422 [DOI: 10.5194/hess-24-5407-2020http://dx.doi.org/10.5194/hess-24-5407-2020]
Iguchi T, Seto S, Meneghini R, Yoshida N, Awaka J, Kubota T, Le M, Chandrasekar V, Brodzik S and Kubota T. 2018. GPM/DPR level-2 algorithm theoretical basis document (Version 6). Goddard Earth Sciences Data and Information Services Center, Greenbelt, MD, USA
Jameson A and Kostinski A. 2001. Reconsideration of the physical and empirical origins of Z-R relations in radar meteorology. Quarterly Journal of the Royal Meteorological Society, 127(572): 517-538 [DOI: 10.1256/smsqj.57213http://dx.doi.org/10.1256/smsqj.57213]
Kirankumar N V P, Rao T N, Radhakrishna B and Rao D N. 2008. Statistical characteristics of raindrop size distribution in southwest monsoon season. Journal of Applied Meteorology and Climatology, 47(2): 576-590 [DOI: 10.1175/2007JAMC1610.1http://dx.doi.org/10.1175/2007JAMC1610.1]
Li N and Lu M Q. 2015. A brief introduction to spaceborne precipitation radar. The Science Education Article Collects, (27): 175-177.
李南, 卢美圻. 2015. 星载测雨雷达简介. 科教文汇, (27): 175-177 [DOI: 10.3969/j.issn.1672-7894.2015.27.083http://dx.doi.org/10.3969/j.issn.1672-7894.2015.27.083]
Liao L, Meneghini R and Tokay A. 2014. Uncertainties of GPM DPR rain estimates caused by DSD parameterizations. Journal of Applied Meteorology and Climatology, 53(11): 2524-2537 [DOI: 10.1175/JAMC-D-14-0003.1http://dx.doi.org/10.1175/JAMC-D-14-0003.1]
Liu S N and Wang G L. 2020. Study on the impacts of DSD parameters on precipitation estimation using dual-frequency radar. Plateau Meteorology, 39(3): 570-580
刘胜男, 王改利. 2020. DSD参数对双频雷达估测降水的影响研究. 高原气象, 39(3): 570-580 [DOI: 10.7522/j.issn.1000-0534.2019.00092http://dx.doi.org/10.7522/j.issn.1000-0534.2019.00092]
Prigent C. 2010. Precipitation retrieval from space: an overview. Comptes Rendus Geoscience, 342(4/5): 380-389 [DOI: 10.1016/j.crte.2010.01.004http://dx.doi.org/10.1016/j.crte.2010.01.004]
Radhakrishna B, Satheesh S K, Narayana Rao T, Saikranthi K and Sunilkumar K. 2016. Assessment of DSDs of GPM-DPR with ground-based disdrometer at seasonal scale over Gadanki, India. Journal of Geophysical Research: Atmospheres, 121(19): 11792-11802 [DOI: 10.1002/2015JD024628http://dx.doi.org/10.1002/2015JD024628]
Scheleusener P. 1967. Drop size distribution and energy of falling raindrops from a medium pressure irrigation sprinkler. Ph.D. Thesis, Michigan State University, East Lansing, MI, USA
Sun Q Z, Wang C J, Zhao J, Zheng J and Chen J Y. 2011. Research evolution of rainfall erosivity (R) in China. Chinese Agricultural Science Bulletin, 27(4): 1-5
孙泉忠, 王朝军, 赵佳, 郑洁, 陈菊艳. 2011. 中国降雨侵蚀力R指标研究进展. 中国农学通报, 27(4): 1-5
Tokay A, Petersen W A, Gatlin P and Wingo M. 2013. Comparison of raindrop size distribution measurements by collocated disdrometers. Journal of Atmospheric and Oceanic Technology, 30(8): 1672-1690 [DOI: 10.1175/JTECH-D-12-00163.1http://dx.doi.org/10.1175/JTECH-D-12-00163.1]
Uijlenhoet R, Steiner M and Smith J A. 2003. Variability of raindrop size distributions in a squall line and implications for radar rainfall estimation. Journal of Hydrometeorology, 4(1): 43-61[DOI: 10.1175/1525-7541(2003)004<0043:VORSDI>2.0.CO;2http://dx.doi.org/10.1175/1525-7541(2003)004<0043:VORSDI>2.0.CO;2]
Ulbrich C W. 1983. Natural variations in the analytical form of the raindrop size distribution. Journal of Climate and Applied Meteorology, 22(10): 1764-1775 [DOI: 10.1175/1520-0450(1983)022<1764:NVITAF>2.0.CO;2http://dx.doi.org/10.1175/1520-0450(1983)022<1764:NVITAF>2.0.CO;2]
Wischmeier W H and Smith D D. 1958. Rainfall energy and its relationship to soil loss. Eos, Transactions American Geophysical Union, 39(2): 285-291 [DOI: 10.1029/TR039i002p00285http://dx.doi.org/10.1029/TR039i002p00285]
Wischmeier W H and Smith D D. 1978. Predicting rainfall erosion losses: a guide to conservation planning, Agriculture Handbook 537. Department of Agriculture, Washington, DC, US
Wu Z H, Zhang Y, Zhang L F, Lei H C, Xie Y Q, Wen L and Yang J F. 2019. Characteristics of summer season raindrop size distribution in three typical regions of western Pacific. Journal of Geophysical Research: Atmospheres, 124(7): 4054-4073 [DOI: 10.1029/2018JD029194http://dx.doi.org/10.1029/2018JD029194]
Yang Q Q, Dai Q, Han D W, Chen Y H and Zhang S L. 2019. Sensitivity analysis of raindrop size distribution parameterizations in WRF rainfall simulation. Atmospheric Research, 228: 1-13 [DOI: 10.1016/j.atmosres.2019.05.019http://dx.doi.org/10.1016/j.atmosres.2019.05.019]
Zhu J X, Zhang S L, Yang Q Q, Shen Q, Zhuo L and Dai Q. 2021. Comparison of rainfall microphysics characteristics derived by numerical weather prediction modelling and dual-frequency precipitation radar. Meteorological Applications, 28(3): e2000 [DOI: 10.1002/met.2000http://dx.doi.org/10.1002/met.2000]
Zhu Y Q and Liu Y B. 2013. Advances in measurement techniques and statistics features. Advances in Earth Science, 28(6): 685-694
朱亚乔, 刘元波. 2013. 地面雨滴谱观测技术及特征研究进展. 地球科学进展, 28(6): 685-694 [DOI: 10.11867/j.issn.1001-8166.2013.06.0685http://dx.doi.org/10.11867/j.issn.1001-8166.2013.06.0685]
文章被引用时，请邮件提醒。
提交
相关作者
暂无数据
相关机构
湖州中核勘测规划设计有限公司
山东科技大学 测绘与空间信息学院
中国气象局气象探测中心
河海大学 地球科学与工程学院
河海大学 水文水资源学院
选择翻译语言
- 地址：北京市海淀区北四环西路19号 《遥感学报》编辑部 邮编：100190
- 联系电话：010-58887052 Email：nrsb@aircas.ac.cn
- 技术支持由北京北大方正电子有限公司提供 京ICP备20021838号-9
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies
0
回到顶部