拉曼激光雷达测量水汽误差源分析研究

大气与环境光学学报 ›› 2018, Vol. 13 ›› Issue (3): 170-177.

拉曼激光雷达测量水汽误差源分析研究

史悦1,2, 谢晨波1*, 谭敏1,2, 王邦新1,2, 吴德成1, 刘东1, 王英俭1,2

(1 中国科学院安徽光学精密机械研究所中国科学院大气光学重点实验室，安徽合肥 230031；
2 中国科学技术大学，安徽合肥 230026)

收稿日期:2017-01-17 修回日期:2017-02-13 出版日期:2018-05-28 发布日期:2018-05-31
通讯作者: 谢晨波 (1976-)，男，安徽霍山人，研究员，博士生导师，主要从事激光雷大气遥感与环境监测方面的研究。 E-mail:cbxie@aiofm.ac.cn
作者简介:史悦 (1992-)，女，黑龙江哈尔滨人，硕士研究生，主要从事激光雷达探测大气数据分析及应用方面的研究。
基金资助:
Supported by National Natural Science Foundation of China(国家自然科学基金，41405032), Key Research Program of the Chinese Academy of Sciences(中国科学院重点部署项目，KJZD-EW-TZ-G06-01), Independent Innovation Project of Anhui Province (安徽省自主创新专项，12Z0104074)

Analysis of Error Sources on Water Vapour Observed by Raman Lidar

SHI Yue1,2, XIE Chenbo1*, TAN Min1,2, WANG Bangxin1,2, WU Decheng1, LIU Dong1, WANG Yingjian1,2

(1 Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China;
2 University of Science and Technology of China, Hefei 230026, China)

Received:2017-01-17 Revised:2017-02-13 Published:2018-05-28 Online:2018-05-31
Supported by:
Supported by National Natural Science Foundation of China(国家自然科学基金，41405032), Key Research Program of the Chinese Academy of Sciences(中国科学院重点部署项目，KJZD-EW-TZ-G06-01), Independent Innovation Project of Anhui Province (安徽省自主创新专项，12Z0104074)

摘要/Abstract

摘要：

水汽与人类的生活息息相关。常规手段测量水汽的时空分辨率较低有效提高局地短期天气预报的可信度。为加强拉曼激光雷达在气象预报中的应用，有必要对其水汽测量误差进行分析研究。通过自研的拉曼激光雷达测量数据，依据误差传递理论，分析水汽混合比的误差。同时，利用无线电探空仪与拉曼激光雷达对比。分析结果表明：激光雷达测量水汽主要包括标定常数、大气透过率修正和测量信号三个误差源。其中，定标常数误差随高度不变约为4%，是1.5 km以下误差的主要来源；大气透过率修正误差随高度升高而增加，洁净天气下对误差的影响小于4%；测量信号误差在洁净天气下3 km高度以内一般小于20%，在3 km高度以上，成为误差的主要来源。比对结果显示：激光雷达计算误差和比对误差一致性较好。上述分析结果对于提高激光雷达在气象预报中的应用起到很好的辅助作用。

关键词: 拉曼激光雷达, 水汽混合比, 误差分析

Abstract:

Water vapour has a closely effect on human being. When detecting water vapour, the traditional tools have low spatio-temporal resolution. Lidar can improve the credibility of weather forecast. According to the theory of error analysis, the error sources of water vapour and their contributions are studied form the experimental data. Meanwhile, the computed relative errors are compared with that calculated relative errors from Raman lidar and radiosonde data. It turns out that the error sources on water vapour observed by Raman lidar includes the calibration constant, the transmission correction and the Raman scattering signals. The calibration constant error is about 4% and constant with height, and it is the major contribution to the total error under 1.5 km height. The transmission correction error increases slowly along with the height and less than 4% in the condition of clean air. The Raman scattering signals error is less than 20% under 3 km height, but it becomes the main source above 3 km height. The comparison shows that the relative error derived by Raman Lidar agrees well with the calculated relative error. In summary, the analysis results above are helpful to promote the application of Raman lidar in weather forecast.

Key words: Raman lidar, water vapour mixing ratio, error analysis

中图分类号:

史悦谢晨波谭敏王邦新吴德成刘东王英俭. 拉曼激光雷达测量水汽误差源分析研究[J]. 大气与环境光学学报, 2018, 13(3): 170-177.

SHI Yue, XIE Chen-Bo, TAN Min, WANG Bang-Xin, WU De-Cheng, LIU Dong, WANG Ying-Jian. Analysis of Error Sources on Water Vapour Observed by Raman Lidar[J]. Journal of Atmospheric and Environmental Optics, 2018, 13(3): 170-177.

参考文献

[1] Shine K P, Sinhala A. Sensitivity of the Earth′s climate to height dependent changes in the water vapor mixing ratio [J]. Nature, 1991, 354(6352): 382-384.
[2] Wang Yingjian, Hu Shunxing, Hu huanling, et al. Atmospheric Parameters Measurement by Lidar [M]. Beijing: Science Press, 2018: 8-13, 277-282(in Chinese).
王英俭, 胡顺星, 胡欢陵, 等. 激光雷达大气参数测量 [M]. 北京: 科学出版社, 2014: 8-13, 277-282.
[3] Cheng Z T, Liu D, Zhang Y P, et al. Generalized high-spectral-resolution lidar technique with a multimode laser for aerosol remote sensing [J]. Opt. Express, 2017, 25(2): 979-993.
[4] Lv M, Liu D, Li Z Q, et al. Hygroscopic growth of atmospheric aerosol particles based on lidar, radiosonde, and in situ measurements: case studies from the Xinzhou field campaign [J]. Jounal of Quantitative Spectroscopy and radiative Transfer, 2017, 188: 60-70.
[5] Tao Zongming, Ma xiaomin, Liu dong, et al. Statistical distribution of PM2.5 mass concentration profiles at west suburb of Hefei city in 2014[J]. Acta Optica Sinica, 2016, 36(6): 0601001(in Chinese).
陶宗明, 麻晓敏, 刘东, 等. 2014年合肥西郊PM2.5质量浓度廓线统计分布 [J]. 光学学报, 2016, 36(6): 0601001.
[6] Shang Zhen, Xie Chenbo, Zhong Zhiqing, et al. Raman lidar for measurement of tropospheric water vapor [J]. Infrared and Laser Engineering, 2016, 45(12): 1211003(in Chinese).
尚震, 谢晨波, 钟志庆, 等. 用于测量流层水汽的拉曼激光雷达 [J]. 红外与激光工程, 2016, 45(12): 1211003.
[7] Xie C B, Shang Z, Tan M, et al. Application of Raman Lidar for the spatial and vertical distribution of aerosol and water vapor in Beijing China [C]. 22nd International Symposium Atmospheric and Ocean Optics: Atmospheric Physics, 2016, 10035: 100352T.
[8] Sheng Peixuan, Mao Jietai, Li Jianguo, et al. Atmosphere Physics [M]. Beijing: Peking University Press, 2011: 18-25, 416-438(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，2011: 18-25，416-438.
[9] Li Tao, Qi Fudi, Yue Guming, et al. Raman lidar system for the measurements of water vapor mixing ratio in the atmosphere [J]. Chinese Journal of Atmospheric Sciences, 2000, 24(6): 843-854(in Chinese).
李陶, 戚福第, 岳古明, 等. 大气中水汽混合比Raman激光雷达探测 [J]. 大气科学, 2000, 24(6): 843-854.
[10] Liu B, Wu D C, Fan A Y, et al. Development of a mobile Raman–Mie lidar system for all time water vapor and aerosol detection [J]. Jounal of Quantitative Spectroscopy and radiative Transfer, 2011, 112: 230-235.
[11] Blackmore W H, Lukes M M. Implementation of the VIZ-B2 radiosonde at NWS upper-air stations [C]. 10th Symp. on Meteorological Observations and Instrumentation, Phoenix, American Meteorological Society, 1998: 22-27.