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基于近红外体散射数据的气溶胶尺度谱正则化反演方法

胡帅,  高太长,  李浩,  刘磊,  陈锦源,  李云

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文章导航 > 红外与激光工程  > 2015 >  44(1): 17-26
胡帅, 高太长, 李浩, 刘磊, 陈锦源, 李云. 基于近红外体散射数据的气溶胶尺度谱正则化反演方法[J]. 红外与激光工程, 2015, 44(1): 17-26.
引用本文: 胡帅, 高太长, 李浩, 刘磊, 陈锦源, 李云. 基于近红外体散射数据的气溶胶尺度谱正则化反演方法[J]. 红外与激光工程, 2015, 44(1): 17-26.
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Hu Shuai, Gao Taichang, Li Hao, Liu Lei, Chen Jinyuan, Li Yun. Regularized inversion method for retrieving aerosol size distribution based on volume scattering function data at near-infrared waveband[J]. Infrared and Laser Engineering, 2015, 44(1): 17-26.
Citation: Hu Shuai, Gao Taichang, Li Hao, Liu Lei, Chen Jinyuan, Li Yun. Regularized inversion method for retrieving aerosol size distribution based on volume scattering function data at near-infrared waveband[J]. Infrared and Laser Engineering, 2015, 44(1): 17-26.
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基于近红外体散射数据的气溶胶尺度谱正则化反演方法

  • 1.

    解放军理工大学气象海洋学院,江苏南京211101

基金项目: 

国家自然基金青年基金(41205125);国家自然基金重大仪器专项(41327003)

详细信息
    作者简介:

    胡帅(1990-),男,博士生,主要从事大气探测与大气遥感方面的研究。Email:huhusai2012@hotmail.com

  • 中图分类号: P426.3+1

Regularized inversion method for retrieving aerosol size distribution based on volume scattering function data at near-infrared waveband

  • 1.

    College of Meteorology and Oceanography,PLA University of Science and Technology,Nanjing 211101,China

  • 摘要: 基于0.86 um 波段气溶胶的体散射数据,提出了一种新的尺度谱反演方法。根据大气气溶胶尺度谱特征,将尺度谱函数n(r)分解为趋势变化函数H(r)和细节变化函数(r),并构造了一组新的基函数对(r) 进行参数化逼近,然后严格照Mie 散射理论,采用Tikhonov 正则化对尺度谱函数进行了反演。采用城市型、乡村型和海洋型气溶胶的尺度谱实测数据进行反演仿真,结果表明,在粒径0.2~10 um 区间、噪声不大于50%的条件下,实际与反演的尺度谱曲线相关系数高于0.98,表现出良好的抗噪声能力;针对小尺寸段(r0.2 um)反演结果的不稳定性,提出了小尺寸段的荣格分布修正法与细模态参数补偿法,模拟结果表明,两种方法对尺度谱修正效果较理想,在0.1~10 um 区间,实际与反演尺度谱曲线相关系数大于0.97。与基于遗传算法的尺度谱反演方法相比,该方法效率高,耗时短,且对尺度谱函数细节变化特征反演较好。
    Abstract: Based on volume scattering data at 0.86 um, a new method for retrieving aerosol size distribution functions was put forward. According to the characteristics of aerosol size distribution, size distribution function n (r) was broken into two part, whole-trend function H (r) and detail-describing function (r), and a new series of basis functions were advanced and employed to approach (r). To overcome ill-posed nature in retrieval process, Tikhonov regularization method was combined with Mie scattering theory to strengthen the capabilities to void the influence of measurement noise and errors caused by numerical integration. Retrieval simulations are performed with size distribution data measured by Anhui Institute of Optics and Fine Mechanics, which represents three different kinds of aerosol, urban, rural and oceanic. Results show that, when radius of particles is larger than 0.2 um, the curve of retrieved size distribution function nearly coincides with that of actual size distribution, and their correlation coefficient is larger than 0.98 on condition that measurement noise isn't larger than 50%. Good robustness is also exhibited in the model put forward here; however when radius is smaller than 0.15 um, there is some deviation when measurement noise is large. To overcome this problem, Junge-correction method and accumulation-mode-compensation method were put forward, corrected results of retrieval size distributions show great consistency with actual size distribution. Compared with retrieval method based on intelligent algorithm, method here is more effective and less time-consuming, and has advantage in retrieving the detail characteristics of aerosol size distribution.
  • [1]
    [2] Mao Jietai, Zhang Junhua, Wang Meihua. Summary comment on research of atmospherical aerosol in China [J]. Acta Meterologica Sinca, 2002, 60(5): 625-634. (in Chinese) 毛节泰, 张军华, 王美华. 中国大气气溶胶研究综述[J]. 气象学报, 2002, 60(5): 625-634.
    [3] Sheng Peixuan, Mao Jietai, Li Jianguo, et al. Atmospheric Physics [M]. Beijing: Peking University Press, 2003: 27-28. (in Chinese) 盛裴轩, 毛节泰, 李建国, 等. 大气物理学[M]. 北京: 北京大学出版社, 2003: 27-28.
    [4]
    [5] Zhang Lisheng, Shi Guangyu. The simulation and estimation of radiative properties and radiative forcing due to sulfate and soot aerosols [J]. Chinese Journal of Atmospheric Sciences, 2001, 25(2): 231-242. (in Chinese) 张立盛, 石广玉. 硫酸盐和烟尘气溶胶辐射特性及辐射强迫的模拟估算[J]. 大气科学, 2001, 25(2): 231-242.
    [6]
    [7] Zhang X Y, Wang Y Q, Zhang X C, et al. Atmospheric aerosol compositions in China: spatial/temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols [J]. Atmospheric Chemistry and Physics, 2012, 12(21): 779-799.
    [8]
    [9] Mao Jietai, Li Chengcai. Observation study of aerosol radiative properties over China [J]. Acta Meterologica Sinca, 2005, 63(5): 622-635. (in Chinese) 毛节泰, 李成才. 气溶胶辐射特性的观测研究[J]. 气象学报, 2005, 63(5): 622-635.
    [10]
    [11]
    [12] Curtis D B, Meland B, Aycibin M. A laboratory investigation of light scattering from representative components of mineral dust aerosol at a wavelength of 550 nm [J]. Journal of Geophysical Research, 2008, 113: D08210.
    [13]
    [14] Setzer D E. Comparison of measured and predicted aerosol scattering functions[J]. Applied Optics, 1969, 8(3): 905-911.
    [15]
    [16] Xu Li, Fan Xiaobiao, Shi Guangyu, et al. Shape and composition of aerosol in tropospheric and stratospheric atmosphere[J]. Acta Meterologica Sinca, 1998, 56(5): 551-559. (in Chinese) 许黎, 樊小标, 石广玉, 等. 对流层平流层气溶胶粒子形态与化学组成[J]. 气象学报, 1998, 56(5): 551-559.
    [17]
    [18] Hudson P K, Gibson E R, Young M A, et al. Coupled infrared extinction and size distribution measurements of several clay components of mineral dust aerosol [J]. Journal of Geophysical Research, 2008, 113: D01201.
    [19] Zhang Chengchang, Zhou Wenxian. Introduction to Atmospheric Aerosol [M]. Beijing: Meteorology Press, 1995: 66-86. (in Chinese) 章澄昌, 周文贤. 大气气溶胶教程[M]. 北京: 气象出版社, 1995: 66-86.
    [20]
    [21] Castagner J L, Bigio I J. Particle Sizing with a fast polar nephelometer[J]. Applied Optics, 2007, 46(10): 527-532.
    [22]
    [23] Han Yong, Wang Jianti, Rao Ruizhong, et al. Progress in the study of physic-optics characteristics of atmospheric aerosol[J ]. Acta Physica Sinica, 2008, 57(11): 7396-7405. (in Chinese)韩永, 王健体, 饶瑞中, 等. 大气气溶胶物理光学特性研究进展[J]. 物理学报, 2008, 57(11): 7396-7405.
    [24]
    [25] Rao Ruizhong. Modern Atmospheric Optics [M]. Beijing: Science Press, 2012: 547-576. (in Chinese) 饶瑞中. 现代大气光学[M]. 北京: 科学出版社, 2012: 547-576.
    [26]
    [27]
    [28] Phillips D L. A technique for the numerical solution of certain integral equations of the first kind [J]. J Assoc Comput Mach, 1961, 9(13): 84-97.
    [29] Twomey S. Information content in remote sensing [J]. Applied Optics, 1974, 13(11): 942-945.
    [30]
    [31]
    [32] Twomey S. Introduction to the Mathematics of Inversion in Remote Sensing and Indirect Measurements [M]. Amster-dam: Elsevier, 1977.
    [33] Bertero M, Mol C D, Pike E R, et al. Linear inverse problems with discrete data I: General formulation and singular system analysis[J]. Inv Probl, 1985, 4(2): 301-330.
    [34]
    [35] Bertero M, Mol C D, Pike E R, Linear inverse problems with discrete data II: Stability and regularisa-tion [J]. Inv Probl, 1988, 4(2): 573-594.
    [36]
    [37]
    [38] Box G P, Sealey K M, Box M A, et al. Inversion of Mie extinction measurements using analytic eigenfunction theory
    [39] Curry B P. Constrained eigenfunction method for the inversion of remote sensing data: application to particle size determination from light scattering measurements[J]. Applied Optics, 1989, 28(7): 1345-1355.
    [J]. Journal of the Atmospheric Science, 1992, 49 (22): 2074-2081.
    [41] Curry B P, Kiech E L. Determination of the particle size distribution from blind inversion of synthetic data [C]// Proceedings, Chemical Research, Development, and Engineering Center, Scientific Conference on Obscuration and Aerosol Research, 1985: CRDEC-SP-86019.
    [42]
    [43] Capps C D, Henning R L, Hess G M. Analytic inversion of remote-sensing data [J]. Applied Optics, 1982, 21 (19): 3581-3587.
    [44]
    [45] Jones M R, Curry B P, Brewster M Q, et al. Inversion of light-scattering measurements for particle size and optical constants: theoretical study[J]. Applied Optics, 1994, 33(18): 4025-4034.
    [46]
    [47]
    [48] Jones M R, Leong K H, Brewster M Q, et al. Inversion of light-scattering measurements for particle size and optical constants: experimental study[J]. Applied Optics, 1994, 33(18): 4035-4041.
    [49] Lienert B R, Porter J N, Sharma S K. Aerosol size distributions from genetic inversion of polar nephelometer data [J]. Journal of Atmospheric and Oceanic Technology, 2003, 20(24): 1403-1409.
    [50]
    [51]
    [52] Ligon D A, Chen T W, Gillespie J B. Determination of aerosol parameters from light scattering data using an inverse Monte Carlo technique [J]. Applied Optics, 1996, 35 (4): 4297-4303.
    [53] Ye M, Wang S M, Lu Y, et al. Inversion of particle-size distribution from angular light scattering data with genetic algorithms[J]. Applied Optics, 1999, 38(12): 2677-2685.
    [54]
    [55] Li Xuebin, Hu Shunxing, Zhu Wenyue, et al. Characteristics of aerosol size distribution in Hefei, Yuexi, Xaimen and Beijing [J]. Journal of Atmospheric and Environmental Optics, 2011, 6(2): 154-162. (in Chinese) 李学彬, 胡顺星, 朱文越, 等. 合肥、岳西、厦门、北京气溶胶谱分布分析[J]. 大气与环境光学学报, 2011, 6(2): 154-162.
    [56]
    [57]
    [58] H C Van De Hulst. Light Scattering by Small Particles [M]. New York: Wiley, 1957: 12-100.
    [59]
    [60] Wang Y F, Zhang Y, Lukyanenko D V. Recovering aerosol particle size distribution function on the set of bounded piecewise-convex functions [J]. Inverse Problems in Science and Engineering, 2012, 21(2): 339-354.
    [61]
    [62] Tikhonov A N, Arsenin V Y. Solution of Ill-Posed Problems[M]. New York: Wiley, 1977.
    [63]
    [64] Mller D, Wandinger U, Ansmann A. Microphysical particle parameters from extinction and backscatter lidar data by inversion with regularization: theory [J]. Applied Optics, 1999, 38(12): 2346-2357.
    [65]
    [66] Li Xuebin, Huang Yinbo, Xu Chidong, et al. Measurement of refractive index for aerosol particle in Xiamen [J]. Optics and Precision Engineering, 2008, 16(10): 1831-1835. (in Chinese) 李学彬, 黄印博, 徐赤东, 等. 厦门地区气溶胶折射率的测量[J]. 光学精密工程, 2008, 16(10): 1831-1835.
    [67]
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  • 收稿日期:  2014-05-05
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基于近红外体散射数据的气溶胶尺度谱正则化反演方法

  • 1. 解放军理工大学气象海洋学院,江苏南京211101
    • 作者简介:

    胡帅(1990-),男,博士生,主要从事大气探测与大气遥感方面的研究。Email:huhusai2012@hotmail.com

  • 收稿日期: 2014-05-05
  • 修回日期: 2014-06-10
  • 刊出日期: 2015-01-25
基金项目:

国家自然基金青年基金(41205125);国家自然基金重大仪器专项(41327003)

  • 中图分类号: P426.3+1

摘要: 基于0.86 um 波段气溶胶的体散射数据,提出了一种新的尺度谱反演方法。根据大气气溶胶尺度谱特征,将尺度谱函数n(r)分解为趋势变化函数H(r)和细节变化函数(r),并构造了一组新的基函数对(r) 进行参数化逼近,然后严格照Mie 散射理论,采用Tikhonov 正则化对尺度谱函数进行了反演。采用城市型、乡村型和海洋型气溶胶的尺度谱实测数据进行反演仿真,结果表明,在粒径0.2~10 um 区间、噪声不大于50%的条件下,实际与反演的尺度谱曲线相关系数高于0.98,表现出良好的抗噪声能力;针对小尺寸段(r0.2 um)反演结果的不稳定性,提出了小尺寸段的荣格分布修正法与细模态参数补偿法,模拟结果表明,两种方法对尺度谱修正效果较理想,在0.1~10 um 区间,实际与反演尺度谱曲线相关系数大于0.97。与基于遗传算法的尺度谱反演方法相比,该方法效率高,耗时短,且对尺度谱函数细节变化特征反演较好。

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