基于小波去噪算法的全天时大气水汽拉曼激光雷达探测与分析
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摘要
提出了一种基于小波阈值去噪算法的白天太阳背景光滤波抑制方法,实现对拉曼回波信号中真实信号与噪声的分离,并有效滤除白天背景噪声。基于西安理工大学大气水汽探测拉曼激光雷达系统的全天时实测数据,详细讨论了分解层数、小波基函数、阈值函数以及阈值选取方法等因素对白天探测回波信号去噪结果的影响,对去噪前后信号进行分析并对去噪评价函数进行对比,当利用小波基sym6、分解层数为5层,并采用改进阈值函数和改进通用阈值方法的最优条件时,可实现对白天水汽拉曼散射信号和米-瑞利散射信号较好的去噪效果。讨论了小波去噪前后大气水汽混合比反演廓线和激光雷达水汽探测信噪比(SNR)的结果,分析表明利用该去噪系统得到的白天激光雷达水汽探测SNR提高约3.4倍,水汽探测距离可从1.5~2km提高到3km以上。开展全天时激光雷达连续探测实验和去噪处理,获得了24h边界层内大气水汽混合比的连续变化特性,并得到与近地面气象站数据的较一致的结果,充分验证了小波去噪算法应用于全天时大气水汽探测的可行性和有效性。
A method based on the wavelet threshold denoising algorithm is proposed for the suppression of solar background light,so that the separation of the real signal from the noise in the Raman returned signal can be realized and the background noise in daytime can be removed.Based on all-day data measured by atmosphere water vapor Raman lidar system built in Xi'an University of Technology,influences of decomposition level,wavelet function,threshold function,and threshold selection method on the denoising results of returned signal in daytime are discussed.Signals before and after denoising are compared and denoising evaluation functions are compared.We adopt wavelet sym6,decomposition of five layers,improved threshold function,and improved threshold method to obtain the better denoising effect for water vapor Raman and Mie-Rayleigh scattering signals in daytime.Furthermore,profiles of the atmospheric water vapor mixing ratio,and the results of signal-to-noise ratio(SNR)of water vapor are discussed.Results show that SNR for lidar water vapor measurement increases by 3.4 times in the denoising process.and the water vapor detection range can be improved up to over 3 km from 1.5-2 km in daytime.Lidar continuous detection experiments and denosing process are carried out during 24 h.Variation characteristics of the atmospheric water vapor mixing ratio are obtained below boundary layer,and the results agree with data from near-surface weather stations.It is verified the feasibility and effectiveness of the wavelet denoising algorithm used in all-day atmospheric water vapor detection.
A method based on the wavelet threshold denoising algorithm is proposed for the suppression of solar background light,so that the separation of the real signal from the noise in the Raman returned signal can be realized and the background noise in daytime can be removed.Based on all-day data measured by atmosphere water vapor Raman lidar system built in Xi'an University of Technology,influences of decomposition level,wavelet function,threshold function,and threshold selection method on the denoising results of returned signal in daytime are discussed.Signals before and after denoising are compared and denoising evaluation functions are compared.We adopt wavelet sym6,decomposition of five layers,improved threshold function,and improved threshold method to obtain the better denoising effect for water vapor Raman and Mie-Rayleigh scattering signals in daytime.Furthermore,profiles of the atmospheric water vapor mixing ratio,and the results of signal-to-noise ratio(SNR)of water vapor are discussed.Results show that SNR for lidar water vapor measurement increases by 3.4 times in the denoising process.and the water vapor detection range can be improved up to over 3 km from 1.5-2 km in daytime.Lidar continuous detection experiments and denosing process are carried out during 24 h.Variation characteristics of the atmospheric water vapor mixing ratio are obtained below boundary layer,and the results agree with data from near-surface weather stations.It is verified the feasibility and effectiveness of the wavelet denoising algorithm used in all-day atmospheric water vapor detection.
引文
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[2]Soden B J,Held I M.An assessment of climate feedbacks in coupled atmosphere-ocean models[J].Journal of Climate,2006,19(14):3354-3360.
[3]Mears C A,Santer B D,Wentz F J,et al.Relationship between temperature and precipitable water changes over tropical oceans[J].Geophysical Research Letters,2007,34(24):L24709.
[4]King M D,Menzel W P,Kaufman Y J,et al.Cloud and aerosol properties,precipitable water,and profiles of temperature and water vapor from MODIS[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41(2):442-458.
[5]Jia J Y,Yi F.Atmospheric temperature measurements at altitudes of 5-30km with a doublegrating-based pure rotational Raman lidar[J].Applied Optics,2014,53(24):5330-5343.
[6]LüL H,Liu W Q,Zhang T S,et al.Characteristics of boundary layer height in Jing-Jin-Ji area based on lidar[J].Laser&Optoelectronics Progress,2017,54(1):010101.吕立慧,刘文清,张天舒,等.基于激光雷达的京津冀地区大气边界层高度特征研究[J].激光与光电子学进展,2017,54(1):010101.
[7]Xu J W,Liu D,Xie C B,et al.Multi-wavelength fitting simulation and inversion of atmospheric aerosol spectrum distribution[J].Acta Optica Sinica,2017,37(10):1001006.徐继伟,刘东,谢晨波,等.大气气溶胶谱分布的多波长拟合模拟反演研究[J].光学学报,2017,37(10):1001006.
[8]Cheng Z T,Liu D,Liu C,et al.Multi-longitudinalmode high-spectral-resolution lidar[J].Acta Optica Sinica,2017,37(4):0401001.成中涛,刘东,刘崇,等.多纵模高光谱分辨率激光雷达研究[J].光学学报,2017,37(4):0401001.
[9]Li Y J,Song S L,Li F Q,et al.High-precision measurements of lower atmospheric temperature based on pure rotational Raman lidar[J].Chinese Journal of Geophysics,2015,58(7):2294-2305.李亚娟,宋沙磊,李发泉,等.基于纯转动Raman激光雷达的中低空大气温度高精度探测[J].地球物理学报,2015,58(7):2294-2305.
[10]Wang Y F,Gao F,Zhu C X,et al.Raman lidar for atmospheric temperature,humidity and aerosols up to troposphere height[J].Acta Optica Sinica,2015,35(3):0328004.王玉峰,高飞,朱承炫,等.对流层高度大气温度、湿度和气溶胶的拉曼激光雷达系统[J].光学学报,2015,35(3):0328004.
[11]Froidevaux M,Higgins C W,Simeonov V,et al.ARaman lidar to measure water vapor in the atmospheric boundary layer[J].Advances in Water Resources,2013,51:345-356.
[12]Xie C B,Zhou J,Yue G M,et al.New mobile Raman lidar for measurement of tropospheric water vapor[J].Acta Optica Sinica,2006,26(9):1281-1286.谢晨波,周军,岳古明,等.新型车载式拉曼激光雷达测量对流层水汽[J].光学学报,2006,26(9):1281-1286.
[13]Tian L,Guo S L,Bu L B,et al.Stratosphere temperature inversion algorithm of Rayleigh lidar using wavelet-denosing[J].Infrared and Laser Engineering,2012,41(3):649-654.田力,郭胜利,卜令兵,等.利用小波降噪的瑞利激光雷达平流层温度反演[J].红外与激光工程,2012,41(3):649-654.
[14]Wu S H,Liu Z S,Liu B Y.Enhancement of lidar back scatters signal-to-noise ratio using empirical mode decomposition method[J].Optics Communications,2006,267(1):137-144.
[15]Tian P F,Cao X J,Liang J N,et al.Improved empirical mode decomposition based denoising method for lidar signals[J].Optics Communications,2014,325:54-59.
[16]Yin S H,Wang W R.Denoising lidar signal by combining wavelet improved threshold with wavelet domain spatial filtering[J].Chinese Optics Letters,2006,4(12):694-696.
[17]Zhou Z R,Hua D X,Wang Y F,et al.Improvement of the signal to noise ratio of Lidar echo signal based on wavelet de-noising technique[J].Optics and Lasers in Engineering,2013,51(8):961-966.
[18]Liu B N,Huang Q B,Zhang W M,et al.Invesitgation and experiments of wavelet thresholding in ensemble-based background error variance[J].Acta Physica Sinica,2017,66(2):020505.刘柏年,皇群博,张卫民,等.集合背景误差方差中小波阈值去噪方法研究及试验[J].物理学报,2017,66(2):020505.
[19]Mao J D,Hua D X,Wang Y F,et al.Noise reduction in lidar signal based on wavelet packet analysis[J].Chinese Journal of Lasers,2011,38(2):0209001.毛建东,华灯鑫,王玉峰,等.基于小波包分析的激光雷达信号消噪算法的研究[J].中国激光,2011,38(2):0209001.
[20]Sun L,Zhang Z L,Tan L L,et al.Denoising method of dynamic grating Moirésignal based on wavelet threshold[J].Infrared and Laser Engineering,2010,39(3):576-580.孙磊,张志利,谭立龙,等.采用小波阈值的时变光栅莫尔信号去噪方法[J].红外与激光工程,2010,39(3):576-580.
[21]Fang H T,Huang D S.Noise reduction in lidar signal based on discrete wavelet transform[J].Optics Communications,2004,233(1/2/3):67-76.