光谱学与光谱分析 |
|
|
|
|
|
Study on Automatic Identification of Aerosols Boundary Layer Height with Local Optimum Model Based on Lidar Data |
TENG Ji-yao1, 2, 3, QIN Kai1, 2, 3*, WANG Yun-jia1, 2, 3, LIN Li-xin1, 2, 3, SUN Xin-hui4 |
1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China 2. National Administration of Surveying, Mapping and Geo-information (NASG) Key Laboratory of Land Environment and Disaster Monitoring, Xuzhou 221116, China 3. Jiangsu Provincial Key Lab of Resources and Environment Information Engineering, Xuzhou 221116, China 4. Wuxi CAS Photonics Co., Ltd.,Wuxi 214135, China |
|
|
Abstract The atmospheric aerosols have significant influence on human health, the environment and the climate system. The atmospheric boundary layer (ABL) reflects processes of the near-surface atmosphere and concentration of pollutants. Ground-based laser radar can monitor the vertical distribution of atmospheric aerosols stably and continuously. It provides dynamic information for timing observations of the ABL and environmental forecasting, if aerosols can be monitored and evaluated using lidar technology. There is a gap in the study of ABL observations during the presence of a residual layer and aerosol intrusion, as well as deficiencies in the accuracy and poor computational efficiency of the gradient method. This paper combines the physical meaning of the latter method with characteristics of a lidar timing chart and local optimum model, which based on space-time proximity. Then a polarization-Mie scattering lidar system is used to observe the vertical distribution of aerosols over time at Taihu observation site, which is in a newly developed area of the city of Wuxi, Jiangsu Province, China. Observation and analysis is carried out for two cases in terms of pollution at the end of 2012. Then corresponding estimation model was built with gradient method and local optimum model based on range-corrected signals. In the case of steady weather and mixed pollution, results of the gradient method and local optimum model were very similar. However, the gradient method has more error in the case of pollution intrusion with the residual layer. The local optimum model based on the space-time proximity theory considers vertical eigenvalues and horizontal correlations, thereby greatly reducing the effects of low clouds, signal interference, weak signals, bi-layered aerosols, and residual layer condition. Compared with the gradient method, the local optimum model had a smaller O(n) and greater stability in computer automatic identification. ABL identification in the case with the residual layer and aerosol intrusion was solved with use of lidar technology and the local optimum model. The accuracy and computational efficiency problems of the gradient method were resolved using automatic operation.
|
Received: 2016-01-04
Accepted: 2016-05-26
|
|
Corresponding Authors:
QIN Kai
E-mail: qinkai20071014@163.com
|
|
[1] DONG Yun-sheng, LIU Wen-qing, LIU Jian-guo, et al(董云升, 刘文清, 刘建国, 等). Acta Optica Sinica(光学学报), 2009, 29(2): 292. [2] Wang Z, Chen L, Tao J, et al. Remote Sensing of Environment, 2010, 114(1): 50. [3] Shi K, Newchurch M J, John B, et al. Applied Optics, 2013, 52(15): 3557. [4] Quan J, Gao Y, Zhang Q, et al. Particuology, 2013, 11(1): 34. [5] He Q S, Mao J T, Chen J Y, et al. Atmospheric Environment, 2006, 40(6): 1064. [6] Liu B, Zhong Z, Zhou J. Journal of Optics A: Pure & Applied Optics, 2007, 9(10): 828. [7] Yan Q, Hua D, Wang Y, et al. Journal of Quantitative Spectroscopy & Radiative Transfer, 2013, 122: 97. [8] Landulfo E, Papayannis A, de Freitas A Z, et al. International Journal of Remote Sensing, 2007, 26(13): 2797. [9] Lammert A, Bsenberg J. Boundary-Layer Meteorology, 2006, 119(1): 159. [10] WANG Dong-xiang, SONG Xiao-quan, FENG Chang-zhong, et al(王东祥, 宋小全, 冯长中, 等). Acta Optica Sinica(光学学报), 2015, (A01): 1. [11] Hennemuth B, Lammert A. Boundary-Layer Meteorology, 2006, 120(1): 181. [12] DONG Mei, WANG Jie, ZHAO Dong, et al(东 梅, 王 界, 赵 冬, 等). Journal of Nangjing University·Natural Sciences(南京大学学报·自然科学版), 2015(3). [13] Lü Li-hui, LIU Wen-qing, ZHANG Tian-shu, et al(吕立慧, 刘文清, 张天舒, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015,35(7):1774. [14] Oram J J, Mcwilliams J C, Stolzenbach K D. Remote Sensing of Environment, 2008, 112(5): 2397. [15] Liu Hongxing, Wang Lei, Kenneth C Jezek. International Journal of Remote Sensing, 2005, 26(21): 4639. [16] Qin K, Wu L, Wong M S, et al. Atmospheric Environment, 2016, 141: 2029. |
[1] |
DUAN Ming-xuan1, LI Shi-chun1, 2*, LIU Jia-hui1, WANG Yi1, XIN Wen-hui1, 2, HUA Deng-xin1, 2*, GAO Fei1, 2. Detection of Benzene Concentration by Mid-Infrared Differential
Absorption Lidar[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3351-3359. |
[2] |
WANG Jie1, 2, 3, LIU Wen-qing1, 2, 4, ZHANG Tian-shu1, XIA Jian-dong5, DENG Wei5, HU Wen-jie5. Collaborative Observation of Vertical Structures of Ozone and Aerosol in a Dust Episode Based on Lidar[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2258-2265. |
[3] |
BAI Jie1, 2, NIU Zheng1, 2*, BI Kai-yi1, 2, WANG Ji1, 2, HUANG Yan-ru2, 3, SUN Gang1. Bi-Directional Reflection Characteristic of Vegetation Leaf Measured by Hyperspectral LiDAR and Its Impact on Chlorophyll Content Estimation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1598-1605. |
[4] |
LI Feng1, LIN Jing-jing2, YUN Jie3, ZHANG Shuai4*, WANG He5, ZHANG Hai4, TAO Zong-ming6. Analysis on Variation Characteristics of Air Pollution in Jining City Based on Lidars Networking Observation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3467-3475. |
[5] |
ZHANG Shuai1, WANG Ming1, SHI Qi-bing1, YE Cong-lei1, LIU Dong2. Study on the Haze Process in Huainan City From October 2019 to March 2020 Observed by Raman-Mie Aerosol Lidar[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2484-2490. |
[6] |
LI Bo1, 2, PU Ya-zhou1, WANG Nan3, WANG Yu-feng1, DI Hui-ge1, SONG Yue-hui1, HUA Deng-xin1*. A Method for Assimilating the Raman Lidar Detecting Temperature in WRF on Simulating the Short-Time Heavy Rainfall[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2110-2115. |
[7] |
HONG Guang-lie1, LIANG Xin-dong1, 2, LIU Hao1*, ZHANG Hua-ping1, 2, SHU Rong1, 2. Detection of CO2 Average Concentration in Atmospheric Path by CW Modulated Differential Absorption Lidar[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(12): 3653-3658. |
[8] |
TAN Min1, 2, 3, WANG Bang-xin1, 2, 3*, ZHUANG Peng1, 2, 3, ZHANG Zhan-ye1, 2, 3, LI Lu1, 2, 3, CHU Yu-fei1, 2, 3, XIE Chen-bo1, 2, WANG Ying-jian1, 2. Study on Atmospheric Temperature and Water-Vapor Mixing Ratio Based on Raman Lidar[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(05): 1397-1401. |
[9] |
ZHANG Tong, QU Xing-hua, ZHANG Fu-min*. The Slight Vibrations Compensation of FMCW Ranging Method Based on Three Light Paths Structure[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(04): 1007-1011. |
[10] |
WU Zi-yang1, 2, XIE Pin-hua1, 2, 3*, XU Jin2, LI Ang2, ZHANG Qiang1, 2, HU Zhao-kun2, LI Xiao-mei2, TIAN Xin1, 2. Study on the Distribution of NO2 Slant Column Density in Atmospheric Boundary Layer of Hefei City Based on Imaging Differential Absorption Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(03): 720-726. |
[11] |
LIU Yan-wen1, SUN Xue-jin1*, ZHANG Chuan-liang1, LI Shao-hui1, ZHOU Yong-bo2, LI Yu-lian1. Research on Lidar Temperature Measurement Method Based on Fizeau Interferometer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(10): 3302-3307. |
[12] |
WANG Jie1, 2, 3, LIU Wen-qing1, 2, ZHANG Tian-shu1*, WAN Xue-ping3, GAO Jie3, LI Ling3, MA Na3. Mapping for Horizontal Aerosol Density Field by a Portable Dual-FOV Lidar[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(09): 2664-2669. |
[13] |
YANG Hui1, MA Xiu-bing2, SUN Yan-fei1, WANG Tie-dong1, QING Feng1, ZHAO Xue-song3. 2D Fluorescence Spectra Measurement of 6 Kinds of Bioagents Simulants by Short-Range Lidar[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(03): 802-806. |
[14] |
HONG Guang-lie1, LI Jia-tang2, KONG Wei1*, SHU Rong1. Summarization of Differential Absorption Liadr for Profiling Atmospheric Water Vapor Overseas[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(02): 340-348. |
[15] |
LI Ming-yang1,2, FAN Meng1*, TAO Jin-hua1, SU Lin1, WU Tong1,3, CHEN Liang-fu1, ZHANG Zi-li4. The Space-Borne Lidar Cloud and Aerosol Classification Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(02): 383-391. |
|
|
|
|