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星载激光雷达CALIOP数据处理算法概述

刘东 刘群 白剑 张与鹏

刘东, 刘群, 白剑, 张与鹏. 星载激光雷达CALIOP数据处理算法概述[J]. 红外与激光工程, 2017, 46(12): 1202001-1202001(12). doi: 10.3788/IRLA201746.1202001
引用本文: 刘东, 刘群, 白剑, 张与鹏. 星载激光雷达CALIOP数据处理算法概述[J]. 红外与激光工程, 2017, 46(12): 1202001-1202001(12). doi: 10.3788/IRLA201746.1202001
Liu Dong, Liu Qun, Bai Jian, Zhang Yupeng. Data processing algorithms of the spac-borne lidar CALIOP: a review[J]. Infrared and Laser Engineering, 2017, 46(12): 1202001-1202001(12). doi: 10.3788/IRLA201746.1202001
Citation: Liu Dong, Liu Qun, Bai Jian, Zhang Yupeng. Data processing algorithms of the spac-borne lidar CALIOP: a review[J]. Infrared and Laser Engineering, 2017, 46(12): 1202001-1202001(12). doi: 10.3788/IRLA201746.1202001

星载激光雷达CALIOP数据处理算法概述

doi: 10.3788/IRLA201746.1202001
基金项目: 

国家重点研发计划(2016YFC0200700,2016YFC1400902,2016YFC400905);国家自然科学基金(41775023,61475141);中央高校基本科研业务费专项资金(2017QNA5001);现代光学仪器国家重点实验室创新项目(MOI2017MS01);浙江省公益技术研究项目(2016C33004)

详细信息
    作者简介:

    刘东(1982-),男,副教授,博士生导师,博士,主要从事光学传感与信息处理技术方面的研究。Email:liudongopt@zju.edu.cn

  • 中图分类号: TN958.98

Data processing algorithms of the spac-borne lidar CALIOP: a review

  • 摘要: CALIOP是首个能观测全球大气状态的星载偏振激光雷达,自2006年发射以来,为研究全球气候和环境变化提供了大量的第一手数据资料,在大气遥感领域具有重要的现实意义。文中系统介绍了星载激光雷达CALIOP的功能结构、数据产品以及数据处理过程。重点介绍了CALIOP的层次识别、场景分类和消光反演的算法原理和流程。
  • [1] Winker D M, Vaughan M A, Omar A, et al. Overview of the CALIPSO mission and CALIOP data processing algorithms[J]. Journal of Atmospheric and Oceanic Technology, 2009, 26(11):2310-2323.
    [2] Shi Guangyu, Wang Biao, Zhang Hua, et al. The radiative and climatic effects of atmospheric aerosols[J]. Chinese Journal of Atmospheric Sciences, 2008, 32(4):826-840. (in Chinese)石广玉, 王标, 张华, 等. 大气气溶胶的辐射和气候效应[J]. 大气科学, 2008, 32(4):826-840.
    [3] Lu Naimeng, Min Min, Dong Lixin, et al. Development and prospect of spaceborne LiDAR for atmospheric detection[J]. Journal of Remote Sensing, 2016, 20(1):1-10. (in Chinese)卢乃锰, 闵敏, 董立新, 等. 星载大气探测激光雷达发展与展望[J]. 遥感学报, 2016, 20(1):1-10.
    [4] Illingworth A J, Barker H W, Beljaars A, et al. The EarthCARE Satellite:the next step forward in global measurements of clouds, aerosols, precipitation, and radiation[J]. Bulletin of the American Meteorological Society, 2015, 96(8):1311-1332.
    [5] Chen Weibiao, Liu Jiqiao. Concept design of spaceborne atmospheric aerosol and carbon lidar[C]//The 4th International Symposium on Atmospheric Light Scattering and Remote Sensing (ISALSaRS'15), 2015.
    [6] Zheng Shaoqing, Xu Jun, He Youjiang, et al. Satellite Cloud-Aerosol Lidar-CALIOP:capability, product and its applications[J]. Journal of Environmental Engineering Technology, 2014, 4(4):313-320. (in Chinese)郑韶青, 徐峻, 何友江, 等. 星载激光雷达CALIOP功能、产品和应用[J]. 环境工程技术学报, 2014, 4(4):313-320.
    [7] Hunt W H, Winker D M, Vaughan M A, et al. CALIPSO lidar description and performance assessment[J]. Journal of Atmospheric and Oceanic Technology, 2009, 26(7):1214-1228.
    [8] Liu Gang, Shi Weizhe, You Rui. Cloud-aerosol lidar of America[J]. Spacecraft Engineering, 2008, 17(1):78-84. (in Chinese)刘刚, 史伟哲, 尤睿. 美国云和气溶胶星载激光雷达综述[J]. 航天器工程, 2008, 17(1):78-84.
    [9] Winker D M, Hostetler C A, Vaughan M A, et al. CALIOP algorithm theoretical basis document part 1:CALIOP instrument, and algorithms overview, PC-SCI-202 Part 1, 2.0[R]. Hampton, VA:NASA Langley Research Center, 2006.
    [10] Hostetler C A, Liu Z, Reagan J. CALIOP algorithm theoretical basis document calibration and Level 1 data products. CALIOP Instrument, and Algorithm Overview, PC-SCI-202, 1.0[R]. Hampton, VA:NASA Langley Research Center, 2006.
    [11] Reagan M J, Wang X, Osborn M J. Spaceborne lidar calibration from cirrus and molecular backscatter returns[J].IEEE Trans Geosci Remote Sens, 2002, 40:2285-2290.
    [12] Vernier J P, Pommereau J P, Garnier A, et al. Tropical stratospheric aerosol layer from CALIPSO lidar observations[J]. J Geophys Res, 2009, 114(D4):144-153.
    [13] Powell K A, Hostetler C A, Liu Z, et al. CALIPSO lidar calibration algorithms. part I:nighttime 532-nm parallel channel and 532-nm perpendicular channel[J]. Journal of Atmospheric Oceanic Technology, 2009, 26(10):2015-2033.
    [14] Vaughan M A, Liu Z, Mcgill M J, et al. On the spectral dependence of backscatter from cirrus clouds:Assessing CALIOP's 1064 nm calibration assumptions using cloud physics lidar measurements[J]. Journal of Geophysical Research Atmospheres, 2010, 115:D14:1-17.
    [15] Vaughan M, Garnier A, Liu Z, et al. Chaos, consternation and CALIPSO calibration:new strategies for calibrating the CALIOP 1064 nm channel[C]//Proceedings of the 26th International Laser Radar Conference(ILRC), 2012.
    [16] Powell K A, Vaughan M A, Rogers R R, et al. The CALIOP 532-nm channel daytime calibration:Version 3 algorithm[C]//Proceedings of the 25th International Laser Radar Conference (ILRC), 2010:1367-1370.
    [17] Winker D M, Couch R H, McCormick M P. An overview of LITE:NASA's lidar in-space technology experiment[J]. Proceedings of the IEEE, 1996, 84(2):164-180.
    [18] Abshire J B, Sun X, Riris H, et al. Geoscience Laser Altimeter System (GLAS) on the ICESat mission:on-orbit measurement performance[J]. Geophysical Research Letters, 2005, 32(21):3.
    [19] Vaughan M A, Winker D M, Powell K A. CALIOP algorithm theoretical basis document-part 2:detection and layer properties algorithms, PC-SCI-202 Part 2, 1.0[R]. Hampton, VA:NASA Langley Research Center, 2005.
    [20] Yu Nana. Cloud-aerosol satellite borne lidar data retrieval algorithm preliminary study[D]. Qingdao:Ocean University of China.2012. (in Chinese)于娜娜. 气溶胶/云星载激光雷达数据反演算法初步研究[D]. 青岛:中国海洋大学, 2012.
    [21] Vaughan M A, Powell K A, Kuehn R E. Fully automated detection of cloud and aerosol layers in the CALIPSO lidar measurements[J]. Journal of Atmospheric and Oceanic Technology, 2009, 26(10):2034-2050.
    [22] Liu Z, Omar A H, Hu Y. CALIOP algorithm theoretical basis document-part3:scene classification algorithms, PC-SCI-202 Part 3, 1.0[R]. Hampton, VA:NASA Langley Research Center, 2005.
    [23] Liu Z, Vaughan M A, Winker D M, et al. Use of probability distribution functions for discriminating between cloud and aerosol in lidar backscatter data[J]. Journal of Geophysical Research Atmospheres, 2004, 109(15):1255-1263.
    [24] Hess M, Koepke P, Schult I. Optical properties of aerosols and clouds:the software package OPAC[J]. Bulletin of the American Meteorological Society, 1998, 79(5):831-844.
    [25] Liu Z, Vaughan M, Winker D, et al. The CALIPSO lidar cloud and aerosol discrimination:version 2 algorithm and initial assessment of performance[J]. Journal of Atmospheric Oceanic Technology, 2008, 26(7):1198-1213.
    [26] Liu Z, Kuehn R, Vaughan M, et al. The CALIPSO cloud and aerosol discrimination version3 algorithm and test results[C]//Proceedings of the 25th International Laser Radar Conference(ILRC), 2010:5-9.
    [27] Omar A H, Jae-Gwang W, Winker D M, et al. Development of global aerosol models using cluster analysis of aerosol robotic network (AERONET) measurements[J]. J Geophys Res, 2005, 110(10):10-14.
    [28] Dubovik O, King M D. A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements[J]. Journal of Geophysical Research:Atmospheres, 2000, 105(D16):20673-20696.
    [29] Masonis S J, Anderson T L, Covert D S, et al. A study of the extinction-to-backscatter ratio of marine aerosol during the shoreline environment aerosol study[J]. Journal of Atmospheric and Oceanic Technology, 2003, 20(10):1388-1402.
    [30] Kalashnikova O V, Sokolik I N. Importance of shapes and compositions of wind-blown dust particles for remote sensing at solar wavelengths[J]. Geophysical Research Letters, 2002, 29(10):38-1-38-4.
    [31] Anderson T L, Masonis S J, Covert D S, et al. In situ measurement of the aerosol extinction-to-backscatter ratio at a polluted continental site[J]. Journal of Geophysical Research:Atmospheres, 2000, 105(D22):26907-26915.
    [32] Omar A H, Winker D M, Kittaka C, et al. The CALIPSO automated aerosol classification and lidar ratio selection algorithm[J]. Journal of Atmospheric Oceanic Technology, 2009, 26(10):1994-2014.
    [33] Toshiyuki M, Hajime O, Naoki K, et al. Application of lidar depolarization measurement in the atmospheric boundary layer:Effects of dust and sea-salt particles[J]. Journal of Geophysical Research Atmospheres, 1999, 104(D24):31781-31792.
    [34] Gobbi G P, Barnaba F, Giorgi R, et al. Altitude-resolved properties of a saharan dust event over the mediterranean[J]. Atmospheric Environment, 2000, 34(29-30):5119-5127.
    [35] Tetsu S, Takashi S, Keiichiro H, et al. Raman lidar and aircraft measurements of tropospheric aerosol particles during the Asian dust event over central Japan:Case study on 23 April 1996[J]. Journal of Geophysical Research, 2003, 108(D12):DOI10.1029/2002JD003150.
    [36] Barnaba F, Gobbi G P. Modeling the aerosol extinction versus backscatter relationship for lidar applications:maritime and continental conditions[J]. Journal of Atmospheric Oceanic Technology, 2004, 21(3):428-442.
    [37] Reagan J A, Thome K J, Powell D M. Lidar aerosol ratio:measurements and models[C]//Geoscience and Remote Sensing Symposium, 2001. IGARSS'01. IEEE 2001 International, 2001:84-87.
    [38] Hu Y. Depolarization ratio-effective lidar ratio relation:Theoretical basis for space lidar cloud phase discrimination[J]. Geophysical Research Letters, 2007, 34(11):224-238.
    [39] Hu Y, Liu Z, Winker D, et al. Simple relation between lidar multiple scattering and depolarization for water clouds[J]. Optics Letters, 2006, 31(12):1809-1811.
    [40] Hu Y, Vaughan M, Liu Z, et al. The depolarization-attenuated backscatter relation:CALIPSO lidar measurements vs. theory[J]. Optics Express, 2007, 15(9):5327-5332.
    [41] Hu Y, Winker D, Vaughan M. CALIPSO/CALIOP cloud phase discrimination algorithm[J]. Journal of Atmospheric and Oceanic Technology, 2009, 26(10):2293-2309.
    [42] Hu Y X, Winker D, Yang P, et al. Identification of cloud phase from PICASSO-CENA lidar depolarization:a multiple scattering sensitivity study[J]. Journal of Quantitative Spectroscopy Radiative Transfer, 2001, 70(4-6):569-579.
    [43] Hu Y, Hosteler L. Using backscattered circular component for shape determination:A theoretical study[J]. Journal of Quantitative Spectroscopy Radiative Transfer, 2003, 79:757.
    [44] Lu Xiaomei, Jiang Yuesong. Statistical properties of clouds over Beijing derived from CALIPSO lidar measurements[J]. Chinese Journal of Geophysics, 2011, 54(10):2487-2494. (in Chinese)路小梅,江月松. 偏振激光雷达探测的北京地区云的统计特性分析[J]. 地球物理学报, 2011, 54(10):2487-2494.
    [45] Collis R T H, Russell P B. Lidar Measurement of Particles and Gases by Elastic Backscattering and Differential Absorption[M]. Berlin, Heidelberg:Springer, 1976:71-151.
    [46] Klett J D. Stable analytical inversion solution for processing lidar returns[J]. Applied Optics, 1981, 20(2):211-220.
    [47] Fernald F G. Analysis of atmospheric lidar observations:some comments[J]. Applied Optics, 1984, 23(5):652-653.
    [48] Young S A, Vaughan M A, Kuehn R E, et al. CALIOP algorithm theoretical basis document-part4:extinction retrieval Algorithms, PC-SCI-202 Part 4, 1.0[R]. Hampton, VA:NASA Langley Research Center, 2008.
    [49] Young S A, Vaughan M A. The retrieval of profiles of particulate extinction from Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) data:algorithm description[J]. Journal of Atmospheric and Oceanic Technology, 2009, 26(6):1105-1119.
    [50] National Aeronautics and Space Administratio. CALIPSO:data user's guide-data product descriptions-lidar Level 3 aerosol profile monthly product[EB/OL].[2017-08-27].https://www-calipso.larc.nasa.gov/resources/calipso_users_guide/datasummaries/l3/index.php.
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出版历程
  • 收稿日期:  2017-08-05
  • 修回日期:  2017-11-27
  • 刊出日期:  2017-12-25

星载激光雷达CALIOP数据处理算法概述

doi: 10.3788/IRLA201746.1202001
    作者简介:

    刘东(1982-),男,副教授,博士生导师,博士,主要从事光学传感与信息处理技术方面的研究。Email:liudongopt@zju.edu.cn

基金项目:

国家重点研发计划(2016YFC0200700,2016YFC1400902,2016YFC400905);国家自然科学基金(41775023,61475141);中央高校基本科研业务费专项资金(2017QNA5001);现代光学仪器国家重点实验室创新项目(MOI2017MS01);浙江省公益技术研究项目(2016C33004)

  • 中图分类号: TN958.98

摘要: CALIOP是首个能观测全球大气状态的星载偏振激光雷达,自2006年发射以来,为研究全球气候和环境变化提供了大量的第一手数据资料,在大气遥感领域具有重要的现实意义。文中系统介绍了星载激光雷达CALIOP的功能结构、数据产品以及数据处理过程。重点介绍了CALIOP的层次识别、场景分类和消光反演的算法原理和流程。

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