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Water is the only atmospheric parameter with three-phase states. The study on distribution and variation in three-phase water is of great scientific significance for understanding cloud microphysics, cloud precipitation physics, and water circulation, especially in the fields of artificial weather process. In the Raman lidar detection technology of three-phase water, it is necessary to solve the problem of high-spectral spectroscopic technique to ensure fine extraction of the echo signal and the detection with high signal-to-noise ratio (SNR). Considering the Raman spectrum characteristics of three-phase water, the influences of filter parameters in the Raman channels on the overlapping characteristics are theoretical simulated and discussed in detail, and the SNR is investigated as well. Regarding the fact that optimal solution can be obtained for neither overlapping nor SNR at the same time, an evaluation function method based on the multi-objective programming problem is proposed to analyze the optimal filter parameters. The results show that the minimum overlapping value and the higher system SNR can be obtained when the central wavelength and bandwidth of the filters are determined to be 397.9 nm and 3.1 nm, 403 nm and 5 nm, 407.6 nm and 0.6 nm in solid water, liquid water and water vapor channel, respectively, and thus the optimal design can be realized for synchronous detection Raman spectroscopic system for three-phase water. Further simulation results show that effective detection can reach above 3.6 km in the daytime and over 4 km on sunny days under a system factor of 1800 J·mm·min for three-phase water Raman measurement in the daytime. Furthermore, the obtained overlapping values are applied to accurate retrieval theory for three-phase water profiles. The simulated profiles of atmospheric water vapor, liquid water and ice water indicate that the water vapor, liquid water and solid water content can be increased synchronously in the cloud layer, and their content, distribution characteristics and the corresponding error are also discussed. The above results validate the feasibility of highspectral spectroscopic technique for detecting the synchronous atmospheric three-phase water, and will provide technical and theoretical support for synchronous retrieval of three-phase water by Raman lidar.
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Keywords:
- three-phase water /
- Raman lidar /
- fine detection /
- simulation
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[17] Veselovskii I A, Cha H K, Kim D H, Choi S C, Lee J M 2000 Appl. Phys. B 71 113
[18] Wang Z, Whiteman D N, Demoz B B, Veselovskii I A 2004 Geophys. Res. Lett. 31 121
[19] Liu F C, Yi F, Jia J Y, Zhang Y P, Zhang S D, Yu C M, Tan Y 2012 Chin. Technol. Sci. 55 1224
[20] Reichardt J 2014 J. Atmos. Ocean. Tech. 31 1946
[21] Stillwell R A, Iii R R N, Thayer J P, Shupe M D, Turner D D 2018 Atmos. Meas. Tech. 11 1
[22] Donovan D P, Klein Baltink H, Henzing J S, de Roode S R, Siebesma A P 2015 Atmos. Meas. Tech. Discuss. 8 237
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[24] Wang K R 2012 Optimization Method (Beijing: Science Press) p156 (in Chinese) [王开荣 2012 最优化方法 (北京: 科学出版社) 第156页]
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[1] Jacobson M Z, Pruppacher H R, Klett J D 1998 Clim. Change 38 497
[2] Plakhotnik T, Reichardt J 2017 J. Quant. Spectrosc. Radiat. Transfer. 194 58
[3] Zhang Z H, Zhou Y Q 2010 Meteorol. Mon. 36 83 (in Chinese) [张志红, 周毓荃 2010 气象 36 83]
[4] Su T, Feng G L 2014 Acta Phys. Sin. 63 249201 (in Chinese) [苏涛, 封国林 2014 63 249201]
[5] Ge Y, Shu R, Hu Y H, Liu H 2014 Acta Phys. Sin. 63 204301 (in Chinese) [葛烨, 舒嵘, 胡以华, 刘豪 2014 63 204301]
[6] Li S C, Wang D L, Li Q M, Song Y H, Liu L J, Hua D X 2016 Acta Phys. Sin. 65 143301 (in Chinese) [李仕春, 王大龙, 李启蒙, 宋跃辉, 刘丽娟, 华灯鑫 2016 65 143301]
[7] Sun G D, Qin L A, Zhang S L, He F, Tan F F, Jing X, Hou Z H 2018 Acta Phys. Sin. 67 054205 (in Chinese) [孙国栋, 秦来安, 张巳龙, 何枫, 谭逢富, 靖旭, 侯再红 2018 67 054205]
[8] Foth A, Pospichal B 2017 Atmos. Meas. Tech. 9 1
[9] Wang Y F, Gao F, Zhu C X, He T Y, Hua D X 2015 Acta Opt. Sin. 35 0328004 (in Chinese) [王玉峰, 高飞, 朱承炫, 何廷尧, 华灯鑫 2015 光学学报 35 0328004]
[10] Wang Y F, Fu Q, Zhao M N, Gao F, Di H G, Song Y H, Hua D X 2018 J. Quant. Spectrosc. Radiat. Transfer. 205 114
[11] Stachlewska I S, Costa-Surós M 2017 Atmos. Res. 194 258
[12] Wang H W, Hua D X, Wang Y F, Gao P, Zhao H 2013 Acta Phys. Sin. 62 120701 (in Chinese) [王红伟, 华灯鑫, 王玉峰, 高朋, 赵虎 2013 62 120701]
[13] Yabuki M, Matsuda M, Nakamura T, Hayashi T, Tsuda T 2016 J. Atmos. Sol-Terr Phys. 150 21
[14] Veselovskii I A, Cha H K, Kim D H, Choi S C, Lee J M 2001 Appl. Phys. B 73 739
[15] Bhl J, Seifert P, Myagkov A, Ansmann A 2016 J. Atmos. Ocean. Tech. 16 1
[16] Sakai T, Whiteman D N, Russo F, Turner David D, Veselovskii I A, Melfi S H, Nagai T, Mano Y 2013 J. Atmos. Ocean. Tech. 30 1337
[17] Veselovskii I A, Cha H K, Kim D H, Choi S C, Lee J M 2000 Appl. Phys. B 71 113
[18] Wang Z, Whiteman D N, Demoz B B, Veselovskii I A 2004 Geophys. Res. Lett. 31 121
[19] Liu F C, Yi F, Jia J Y, Zhang Y P, Zhang S D, Yu C M, Tan Y 2012 Chin. Technol. Sci. 55 1224
[20] Reichardt J 2014 J. Atmos. Ocean. Tech. 31 1946
[21] Stillwell R A, Iii R R N, Thayer J P, Shupe M D, Turner D D 2018 Atmos. Meas. Tech. 11 1
[22] Donovan D P, Klein Baltink H, Henzing J S, de Roode S R, Siebesma A P 2015 Atmos. Meas. Tech. Discuss. 8 237
[23] Whiteman D N 2003 Appl. Opt. 42 2593
[24] Wang K R 2012 Optimization Method (Beijing: Science Press) p156 (in Chinese) [王开荣 2012 最优化方法 (北京: 科学出版社) 第156页]
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