基于星载SAR观测的改进型NRCS模型研究

doi:10.3969/j.issn.1673-503X.2024.05.013

摘要/Abstract

摘要：

通过不同相态降雨粒子散射衰减特性的回波路径分析, 推导了归一化雷达散射截面(Normalized Radar Cross Section, NRCS)计算公式。利用1998年低纬度热带陆地和海洋实测对流降雨垂直廓线, 根据合成孔径雷达(Synthetic Aperture Radar, SAR)的基于定向模型统计(Model-Oriented Statistical, MOS)地面降水量反演算法验证NRCS优化效果。结果表明: 优化后的NRCS模型能提高6.17%的回波衰减; 回波衰减量的提高表明, 在MOS算法低估的地面降雨量范围内, 优化后的NRCS模型能提高MOS算法的地面降雨量反演精度, 反演的平均误差降低了1.56%。但在MOS算法高估的地面降雨量范围内, 优化后的NRCS模型反而会增大MOS算法的反演误差。因此, 改进型的NRCS模型仅适用于MOS算法低估地面降雨量的情况。

关键词: 星载合成孔径雷达, 反演算法, 归一化雷达散射截面, 降雨垂直廓线

Abstract:

The Normalized Radar Cross Section (NRCS) calculation formula is derived by analyzing the echo paths of the scattering attenuation characteristics of rainfall particles in different phases.Using the vertical profiles of convective rainfall measured over low-latitude tropical land and ocean in 1998, the optimization of NRCS is validated based on the Model-Oriented Statistical (MOS) surface rainfall retrieval algorithm of Synthetic Aperture Radar (SAR). The results show that the optimized NRCS model can improve the echo attenuation by about 6.17%.The improvement of echo attenuation indicates that the optimized NRCS model can improve the surface rainfall retrieval accuracy of MOS algorithm in the range of surface rainfall underestimated by MOS algorithm, and the average retrieval error is reduced by about 1.56%.However, in the range of surface rainfall overestimated by MOS algorithm, the optimized NRCS model will increase the retrieval error of MOS algorithm.Therefore, the improved NRCS model is only applicable to the case where the MOS algorithm underestimates the surface rainfall.

Key words: Spaceborne synthetic aperture radar, Retrieval algorithm, Normalized radar cross section (NRCS), Vertical profile of rainfall

中图分类号:

P412.25

徐振斌,谢亚楠,王瑞. 基于星载SAR观测的改进型NRCS模型研究[J]. 气象与环境学报, 2024, 40(5): 106-111.

Zhenbin XU,Ya XIE,Rui WANG. Research on an improved NRCS model based on spaceborne SAR observation[J]. Journal of Meteorology and Environment, 2024, 40(5): 106-111.

图/表 6

表1

图1

图2

图3

图4

图5

参考文献 16

1	Chernokulsky A , Kozlov F , Zolina O , et al. Observed changes in convective and stratiform precipitation in northern Eurasia over the last five decades[J]. Environmental Research Letters, 2019, 14 (4): 045001. doi: 10.1088/1748-9326/aafb82
2	Fu X F , Lin Y H , Liu G S , et al. Seasonal characteristics of precipitation in 1998 over East Asia as derived from TRMM PR[J]. Advances in Atmospheric Sciences, 2003, 20 (4): 511- 529. doi: 10.1007/BF02915495
3	Marzano F S , Mori S , Weinman J A . Evidence of rainfall signatures on X-band synthetic aperture radar imagery over land[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48 (2): 950- 964. doi: 10.1109/TGRS.2009.2034843
4	Fritz J P , Chandrasekar V . A fully polarimetric characterization of the impact of precipitation on short wavelength synthetic aperture radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50 (5): 2037- 2048. doi: 10.1109/TGRS.2011.2170576
5	Marzano F S, Weinman J A, Mugnai A, et al. Rain retrieval over land from X-band spaceborne synthetic aperture radar: a model study[C]//Proceeding of the Fourth European Conference on Radar Meteorology and Hydrology. Rome, Italy, 2006: 18-21.
6	Marzano F S , Weinman J A . Inversion of spaceborne X-band synthetic aperture radar measurements for precipitation remote sensing over land[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46 (11): 3472- 3487. doi: 10.1109/TGRS.2008.922317
7	Polverari F, Marzano F S, Pulvirenti L, et al. Modeling Ocean wave surface to simulate spaceborne scatterometer observations in presence of rain[C]//Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium. Milan, Italy: IEEE, 2015: 1203-1206.
8	Luo T , Xie Y N , Wang R , et al. An analytic solution to precipitation attenuation expression with spaceborne synthetic aperture radar based on Volterra integral equation[J]. Remote Sensing, 2020, 14 (2): 357.
9	Mori S, Polverari F, Pulvirenti L, et al. High-resolution spatial analysis of a hurricane structure by means of X-band and Ka-band satellite synthetic aperture radar[C]//Proceedings of the Eighth European Conference on Radar in Meteorology and Hydrology. Garmisch-Partenkirchen, Germany, 2014: 1-5.
10	Mori S, Marzano F S, Pierdicca N. X-band synthetic aperture radar methods[M]//Levizzani V, Kidd C, Kirschbaum D B, et al. Satellite Precipitation Measurement. Cham: Springer, 2020: 315-339.
11	张晋广, 赵姝慧, 刘旸, 等. 一次东北冷涡云降水垂直结构特征分析[J]. 气象与环境学报, 2021, 37 (4): 1- 8.
12	申莉莉, 李江波, 王秀明, 等. 京津冀暖季短时强降水环境特征对比分析[J]. 气象与环境学报, 2024, 40 (1): 37- 46.
13	张培昌. 雷达气象学[M]. 北京: 气象出版社, 1988: 14- 15.
14	Liu G S , Fu Y F . The characteristics of tropical precipitation profiles as inferred from satellite radar measurements[J]. Journal of the Meteorological Society of Japan.Ser.Ⅱ, 2001, 79 (1): 131- 143. doi: 10.2151/jmsj.79.131
15	Weinman J A , Marzano F S . An exploratory study to derive precipitation over land from X-band synthetic aperture radar measurements[J]. Journal of Applied Meteorology and Climatology, 2008, 47 (2): 562- 575. doi: 10.1175/2007JAMC1663.1
16	Oh Y , Sarabandi K , Ulaby F T . An empirical model and an inversion technique for radar scattering from bare soil surfaces[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30 (2): 370- 381. doi: 10.1109/36.134086

降雨类型	p_r/(mm· h^-1·km^-1)	p_m/(mm· h^-1·km^-1)	p_s/(mm· h^-1·km^-1)	z₀/km	z_m/km
陆地对流	-0.04	-0.33	-0.21	4.65	6.65
海洋对流	-0.03	-0.43	-0.24	4.65	6.65

[1]	杨磊, 贺宏兵, 杨波, 孟鑫. 基于S波段双线偏振天气雷达的降水粒子相态识别[J]. 气象与环境学报, 2019, 35(4): 127-132.
[2]	吴彬, 魏鸣, 李艳芳, 郑石, ABRO Mohammad Ilyas. 基于变分技术的天气雷达速度退模糊算法的改进研究[J]. 气象与环境学报, 2019, 35(3): 18-28.
[3]	胡鹏宇, 徐爽, 陈传雷, 杨磊, 纪永明, 孙丽. 辽宁省3次暴雪天气过程雷达特征对比分析[J]. 气象与环境学报, 2018, 34(3): 18-27.
[4]	黄思源, 王界, 全彩峰. 2015年宁波地区两次灰霾天气过程气溶胶垂直分布特征分析[J]. 气象与环境学报, 2017, 33(3): 45-51.
[5]	宗蓉,刘春云. 雷达反射率数据质量控制方法初探[J]. 气象与环境学报, 2009, 25(2): 62-67.
[6]	张维全李洋姚欣. CINRAD/SC天气雷达系统“太阳法标校”数据修正问题研究[J]. 气象与环境学报, 2009, 25(1): 35-39.