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Abstract
In order to achieve a high signal-to-noise ratio by using small laser energy and telescope aperture, we present a detection method based on Rayleigh-Brillouin scattering (RBS) for the measurement of atmospheric temperature without response functions and calibration procedures by using high spectral resolution lidar (HSRL). Different from the traditional HSRL, a Fabry-Pérot interferometer (FPI) with a continuous tunable cavity and polarization optical scheme are employed in a high spectral resolution filter. In order to continuously change the resonant frequency of the FPI, an electro-optical crystal of potassium dideuterium phosphate (DKDP) with two ring electrodes is used as a continuous tunable cavity in the FPI. At each scanned frequency point corresponded with the resonant frequency of the FPI, the received signals of four discrete points on RBS are obtained. Atmospheric temperature is inverted by using a RBS model. The polarization optical scheme is used to suppress the solar background light, and improve the utilization of return signals. In detection experiment of atmospheric temperature, the detection height is 2 km at night and 1.5 km during the day by using a pulsed energy of 30 mJ and telescope diameter of 250 mm. The results are in good agreement with the data detected by radiosonde.
© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
1. Introduction
Atmospheric temperature is an important parameter for the descriptions of atmospheric state, atmospheric climate, atmospheric science, meteorology, environmental change, numerical weather forecast, gravitational wave, and so on [1–6]. During the last few years, more research on the detection method of atmospheric temperature has been carried out for better understanding global warming, urban meteorology and heat island. Because of the characteristics of high spatial-temporal resolution and easy realization in daytime-temperature detection, with the development of laser, spectral measurement, and weak signal detection technologies, the HSRL based on RBS is widely used to detect atmospheric temperature. At present, a Fabry-Pérot etalon (FPE) or atomic (or molecular) absorption cell is employed as a high spectral resolution filter to separate Mie and RBS signals in HSRL. She [7], Hair [8], Zang [9], Liu [10] and Bo [11] realized the detection of atmospheric temperature profile by using Barium atomic or Iodine molecular absorption cells in HSRL, respectively. Hua [12,13] introduced a Ultraviolet Rayleigh-Mie lidar by using of three FPEs for accurate temperature profiling of the troposphere. In the system, two FPEs are located at the same wing of the Rayleigh backscattering spectrum for temperature measurement. To removing the residual Mie component that remained in the Rayleigh signals by measuring the Mie-scattering signal with a third FPE. Zhao [14], Shangguan [15], Xia [16], Shen [17], and Liang [18],proposed a triple-channel FPE technique in HSRL that can simultaneously measure wind, temperature, and aerosol optical properties from the troposphere to low stratosphere. Li [19] reported the actual measurement of atmospheric density and temperature and analyzed the temperature characteristics of the middle atmosphere. However, the above measurement methods based on Rayleigh-Brillouin scattering are employed to detect atmospheric temperature change. That is to say, the definition of response functions and calibration procedures is required to obtain atmospheric temperature.
In HSRL, a RBS spectrum in atmosphere is obtained by continuously changing the frequency of detection laser or the resonant frequency of FPI. Atmospheric temperature can be inverted without response functions and calibration procedures by using a RBS spectrum. Witschas [20] employs a novel method by continuously changing laser frequency (150 MHz and 250 MHz) and using a FPE. The temperature profiles from 2 km to 15 km are obtained by the full width at half maximum (FWHM) of RBS. In this paper, we present an absolute detection method for the atmospheric temperature in the boundary layer by using the FPI with continuous tunable cavity [21] and constant laser frequency. When an electro-optical crystal is employed as the medium in the cavity of a scanning FPI, because of electro-optic effect, a modulated voltage signal can change the refractive index of crystal and then the optical length of interference cavity. Because of the variation in the resonant frequency of FPI, a series of specific frequencies on RBS spectrum can be selected. Atmospheric temperature is inverted by using the spectral information without response functions and calibration procedures. Compared with the gas cavity driven by PZT, the crystal cavity has no moving parts, so it can promise the parallelism of cavity and stability of selected frequency. In addition, the detection capability of HSRL at the daytime is improved by using polarization optical scheme [22] to suppress the solar background light. Compared with the filter method in a traditional HSRL that the intensity of return signal is split into the different transmission channel of a discriminator, the advantage of this filter system is that the intensity of return signal is fully utilized for each discriminator channel, the return signal changes polarization state of the light without loss of intensity when it is incident on the FPI. So the signal noise ratio (SNR) of the system in detection is improved.
2. Theory
When a crystal is loaded with an external electric field, the phenomenon that its wavefront phase Δφ varies linearly with voltage is called linear electro-optic effect. When the light propagates parallel to z axis and an electric field is loaded along z axis, the refractive index ellipsoid of new major axes x’, y'and z’ of crystal can be expressed as [23]
In order to continuously change the resonant frequency of FPI, the electro-optical crystal of DKDP with two ring electrodes is used as a continuous tunable cavity in FPI, because a DKDP has the good performances of the high transmittance in the ultraviolet band and large aperture of 20 mm (the maximum aperture can be 100mm). Since the resonant frequency of FPI can be continuously varied with the refractive index change of DKDP caused by electro-optic effect, the influence of moving part on resonant frequency can be eliminated. According to multi-beam interference and electro-optical modulation theories, the power transfer function of the FPI with continuous tunable cavity can be expressed as
The RBS spectrum is described by the standard deviation of Gaussian distribution (σR, σB), Rayleigh scattering peak of A and Brillouin frequency shift (xB), stokes and anti-stokes spectrum, and is represented as their linear superposition, as shown in the following equation [24].
As the refractive index of electro-optical crystal changes with the modulation voltage, the optical cavity length of FPI is altered by using an electro-optical crystal as an interference cavity, and then the resonant frequency is continuous tunable. Scanning the RBS spectrum is realized by using the continuous tunable cavity FPI. At each scanning frequency point, the received signals Ni (i=1, 2, ···, n) of discrete points on the RBS spectrum are
where, NR0 is the received photons number of Rayleigh scattering; NB0 is the received photons number of Brillouin scattering; ξ(υ) is the channels gain; F(υ) is transmission spectrum of FPI; RB(T, P, υ) is RBS spectrum return from model; T and P are atmospheric temperature and pressure, separately; ν is the frequency; and NmCmi is the detected photons number of the residual Mie scattering in scanning channel. In addition, the system noise and frequency-locking error are included in the signal intensities of discrete points. They can be eliminated by the system calibration to obtain noise values and compensation method of frequency shift. The third term on the right side of Eq. (6) will be eliminated by the correction method of residual Mie scattering. Taking four discrete points (Im, xm) (m = 1, 2, 3, 4) with linearity on the RBS spectrum into Eq. (4), it can be transformed into a quaternary quadratic equation, which can be written as where, R(A, σR, xm) and B(A, σB, xB xm) are Rayleigh and Brillouin scattering, separately. By solving Eq. (7), σR can be obtained, and then the atmospheric temperature T can be expressed as3. Experimental setup
Figure 1 shows the experimental setup for the measurement of atmospheric temperature. A linearly and horizontally polarized laser beam transmits into the atmosphere, and the laser extinction ratio is greater than 200:1 at 355 nm. Letting a polarizer and a detector are placed in the x-y plane. Definition of θ is the scattering angle, σ is the azimuth angle. Let a polarizer and a detector be placed in the x-y plane. The azimuth is measured counterclockwise from the x axis, the scattering is measured downward from the z axis, and the pulse laser propagates along the positive z direction, with the parallel component P‖ along the y axis and the perpendicular component S⊥along the x axis. The backscattered beam is generated after the laser interacts with atmospheric molecules and aerosols. The return signal received by a Cassegrain telescope transmits into the filter system. The return signal contains the light with two polarized directions of P and S components due to the depolarization characteristics of aerosol. The collimated return signal is divided into two beams by a polarization beam splitter (PBS). The light vector of solar background light has the characteristics of axial symmetry, uniform distribution, and the same amplitude of vibration in all directions. Therefore, the solar background light in the return signal will be reduced by 1/2 after being reflected by PBS. According to the Mie scattering theory and radiation transmission theory, molecular scattering in the atmosphere will not change the polarization state of the emitted laser beam, the Mie scattering caused by non-spherical particles has depolarization characteristics. Therefore, the return signal of atmospheric molecules transmitting through PBS, and the Mie scattering signal and part of the solar background light which has been depolarized is reflected by PBS. The transmitted beam is the P component polarized light which contain Mie and Rayleigh signals, which is the same as the polarization direction of the pulse laser. Then, it transmits through the quarter-wave plate (QWP) to become circularly polarized light, and the angle between the fast axis of the wave plate and the light vector of the excitation pulsed laser is 45°. The transmitted light (Mie signal) is received by photomultiplier tube (PMT1) to suppress the residual Mie scattering in the four scanning channels, and the reflected light (RBS signal) transmits through QWP again, and becomes S component polarized light. The reflected light that the polarization direction is rotated by 90° is reflected by PBS, then transmitting through a half wave plate (HWP), incidents vertically on the FPI, the transmitted light is received by the PMT2. When the FPI is loaded with four different voltages, the intensities of the four discrete points corresponding to the four resonance frequencies on the RBS spectrum are obtained.
The correction method is adopted in order to eliminate the influence of residual Mie scattering for the envelope spectrum of RBS. Since the Mie scattering is elastic scattering that the excitation laser wavelength is less than or approximate to the particle size, the frequency spectrum of Mie scattering does not shift or broaden compared to the center frequency of excitation laser. The Mie scattering spectrum can be considered to be the same as the excitation laser spectrum. A continuous tunable cavity FPI was used to obtain the intensity of excitation laser, which can be considered as the intensity of the Mie scattering at each selected frequency point. In the scanning channel off center frequency, the proportion of residual Mie scattering is about 1%. When one scanning channel is set on the center frequency of the envelope spectrum of RBS, the intensity of Mie scattering in this channel can be obtained. Combined with the above proportion, the intensities of residual Mie scattering in the other scanning channels are available.
In order to ensure the detection accuracy of atmospheric temperature, it is necessary to lock the frequency of the excitation pulse laser. In this paper, a temperature-controlled iodine molecular absorption cell is used as the frequency discriminator, and the output frequency of the pulsed laser is locked through the PID control algorithm. Figure 2(a) shows the pulsed laser frequency drift within 25 minutes, the frequency drift is less than ±5.5 MHz. To improve SNR of the lidar will sum up pulses for averaging when collecting data. This method of collecting data will reduce the influence of laser frequency on the experimental results. The result ·of frequency drift is less than ±1.8 MHz in 25 minutes after being smoothed with a window of 1000 points as shown in Fig. 2(b).
4. Experimental setup
4.1 FPI with a continuous tunable cavity
Table 1 shows the optical parameters of the FPI. The optical length of interference cavity can be designed according to the wavelength on the left end of the RBS spectrum and free spectral region. In HSRL, the central wavelength of the excitation pulse laser is usually 354.7 nm. The FWHM of RBS spectrum caused by atmospheric temperature and pressure is 5.5 GHz (The corresponding spectrum width is 2.3 pm). Therefore, the change range of resonant wavelength of FPI should be ± 2.3 pm. According to Eq. (3), the change range of optical length of interference cavity should be ∓ 83.3 nm. In this work, a DKDP is selected as the medium in interference cavity. When being longitudinally modulated, according to the refractive index of DKDP, the geometric length of interference cavity is 8.5 mm corresponding to the resonant wavelength of 354.7 nm. When using the FPI to scan RBS spectrum, we obtained discrete point information on the spectrum to fit the RBS spectrum. Considering the transmittance of the continuous tunable cavity FPI, as well as the insertion loss, end face reflectance, and end face defects of interference cavity, the FWHM was selected to be 200 MHz, transmittance to be 20%. The central wavelength of pulse laser will be locked at the next transmission spectrum center of FPI. Two adjacent transmission spectra can be obtained by scanning twice. In the experiment, the measured interval between the two spectral center frequencies is 11.12 GHz, which is consistent with the designed FSR, as shown in Fig. 3. When the modulation voltage is applied, the central wavelength of the FPI is scanned within the range of 354.7 ± 2.3065×10−3 nm. According to Eq. (3), the resonant frequency of the FPI changes by 0.4919 GHz for every 80 V changing in voltage. The measured value of 0.492 GHz is in good agreement with the theoretical value. In order to reduce the refractive index modulation sensitivity of DKDP caused by thermo-optic effect to the order of 10−4, the temperature control accuracy of the temperature control system needs to meet the order of 10−2. The control accuracy of temperature in the experiment is ±0.02 K, as shown in Fig. 3. The effect of thermo-optical effect on the wavefront phase of the beam passing through DKDP is Δφ = 3×10−4 rad. Therefore, the thermo-optic effect can be ignored.
Table 1. Design parameters of FPI
4.2 Scanning spacing of the FPI
A RBS spectrum line is fitted by measuring four discrete points with different center frequency positions as shown in Fig. 4. Considering that the full width at half maximum of the FPI transmission spectrum is 243 MHz, we need to adjust the frequency position of the four discrete points reasonably and optimize the frequency interval when the RBS spectrum is scanned. A reasonable frequency position can ensure the integrity of the RBS spectrum and effectively detect the energy information of discrete points at different frequency positions, as shown in Table 1. After theoretical calculation and comprehensive consideration, the frequency position of the four discrete points near the full width at half maximum of the RBS spectrum are selected and the modulated voltage value applied to the electro-optic crystal is determined, as shown in Table 2. When the RBS spectrum is scanned, the increment of the modulation voltage applied to the electro-optic crystal is 80 V, and the frequency interval of each discrete point is 500 MHz.
Table 2. Scanning spacing of the FPI
4.3 System simulation
The measurement accuracy of lidar is mainly affected by SNR. By making full utilization of the energy of the return signal, the lidar using small pulse laser energy and aperture telescope can still achieve a high SNR in the measurement of atmospheric temperature. The parameters of polarization HSRL are shown in Table 3.
Table 3. Lidar system parameters
As an active remote sensing detection instrument, high detection altitude and small statistical error are important for the precise detection of atmospheric temperature by lidar, which mainly depends on SNR of the lidar system. In order to evaluate the performance of the lidar system, SNR of the polarization HSRL system is simulated. The important parameters of this system are that the pulsed energy of 30 mJ and the telescope diameter is 250 mm, in addition, the remaining parameters are shown in Table 3, and the filter performance parameters in Table 1 are both used for simulation calculations of SNR. 1000 laser pulses are summed up, and the solar background light noise in daytime and nighttime is respectively taken as 300 Wm-2 sr-1 nm-1 and 10−5 Wm-2 sr-1 nm-1 in the simulation parameters. SNR of the lidar system is simulated and calculated by using the system parameters described above as shown in Fig. 1. In order to ensure the accuracy of atmospheric temperature detection, lidar system is required to be a high SNR (SNR ≥ 100). The detection height in daytime is 1.8 km (SNR = 100), and the detection height at night is 2.5 km (SNR = 100), as shown in Fig. 5(a). The statistical temperature error profile is shown in Fig. 5(b). The temperature measurement error in daytime is less than 1 K up to 1.8 km, and the temperature measurement error in nighttime is less than 1 K up to 2.5 km.
5. Results and discussion
A preliminary experiment is carried out to measure the local atmospheric temperature in Xi'an (108° 59’ 39” E, 34° 15’ 10” N). The sampling rate in the experiment is 50 MS/s, the cumulative number of pulses are 4000, and the observation time is 7 minutes, a narrow-band filter (Tpeak = 60%, Δλ = 1 nm, λ0 = 354.7 nm) is placed in front of PBS1 to initially suppress the solar background light noise signal in daytime. In the experiment, the energy of pulsed laser and the diameter of telescope were 30 mJ and 250 mm, respectively. Detailed experimental parameters are listed in Table 2. Figure 6 is the measurement results at 16:00 (Figs. 6(a), 6(b), and 6(c)) and 21:00 (Figs. 6(d), 6(e), and 6(f)) on August 09, 2020 CST (China Standard Time).
Fig. 6. Comparison of the lidar temperature measurement with radiosonde temperature profiles at 16:00 and 21:00 on August 09, 2020. (a) and (d) Range-corrected lidar signals; (b) and (e) Comparison of the temperature profiles of lidar and radiosonde measurements; (c) and (f) Statistical temperature error.
Figures 6(a) and 6(d) show the range-corrected signals. The area below 0.4 km is the incomplete-overlap zone of telescope reception. The atmospheric temperature profiles at daytime and nighttime are retrieved by using the decoupling model of RBS spectroscopy, as show in Figs. 6(b) and 6(e). As the height increases, the linear decrease of 6.5 K/km is shown on the temperature profiles. The results of lidar are in good agreement with the data acquired by radiosonde. The profiles exhibit the statistical temperature errors in daytime and nighttime are 1.2 K and 1 K respectively, as shown in Figs. 6(c) and 6(f). In order to ensure the accuracy of atmospheric temperature detection, lidar system is required to be a high SNR (SNR ≥ 100). The position of the dashed line in Fig. 6 is represented as SNR=100 in the actual experiment. In the area above the dashed line, the intensity of the return signal gradually weakens and at the same time the noise increases (SNR < 100), there is a large temperature error between the results measured by lidar and the radiosonde, as shown in Fig. 6. We found that the area where the statistical temperature error has a negative slope is below 1 km in height, as shown in Fig. 6(c) and 6(e), because the location of the radiosonde is located in a weather station on the outskirts of the city, which is different from the observation location of the lidar. In addition, the atmospheric boundary layer close to the ground is very active and has strong convection, which will exchange heat with the ground. Therefore, there will be some differences in the temperature near the ground at different locations below 1 km. The State of the atmosphere is relatively stable above 1 km, the SNR gradually decreases as the altitude increases, the temperature error is gradually increases above 1 km and consistent with the simulation results.
The measured lidar data was analyzed after the experiment. In fact, the detection altitude of the lidar system at daytime and nighttime is 1.5 km and 2 km (SNR ≥ 100), respectively, as shown in Fig. 6(b) and 6(c). We found that the actual detection height at day and night are 0.3 km and 0.5 km lower than the simulation results (SNR = 100), respectively, due to the transmittance of optical system of the experiment is less than the simulation. In addition, in the actual observation experiment, the suppression of the solar background light by the lidar system is weakened, and the SNR is reduced. To sum up, this method uses continuous tunable cavity and constant laser frequency FPI to detect the atmospheric temperature profiles in the boundary layer at heights of 2 km and 1.5 km in daytime and nighttime, respectively.
6. Conclusions
This paper proposes a detection method for atmospheric temperature based on RBS without response functions and calibration procedures by using HSRL with the FPI with continuous tunable cavity and polarization optical scheme, in which the high SNR at day and night is achieved by using small pulse energy and aperture telescope. The utilization rate of the return signal has been improved, because of the transmission and reflection spectrum of FPE and the transmission characteristics of polarized light. In addition, because of employing polarization isolation technology, Mie scattered signal and solar background light are greatly suppressed in filter system of HSRL. When a FPI with continuous tunable cavity by using potassium dideuterium phosphate with two ring electrodes is employed to measure spectral information of RBS, atmospheric temperature is absolutely detected. Taking pulse energy of 30 mJ and telescope aperture of 250 mm as an example, the detection experiment of atmospheric temperature profile is carried out. The experimental results show that the detection height at day and night are 1.5 km and 2 km respectively. The results are in good agreement with the data acquired by radiosonde. The backscattering and extinction coefficients of aerosols and molecules can be directly measured by the polarization HSRL without the assumption of lidar ratios. At the same time, the depolarization ratio of aerosol particles can be measured by polarization HSRL, so cloud formation and physical parameters of cloud particles can be studied by this method. The atmospheric temperature and wind speed in the troposphere can be measured by polarization HSRL. The upward movement of the air is hindered by the temperature inversion layer, which causes pollutants to accumulate near the ground, thereby increasing air pollution. In addition, the diffusion rate of atmospheric pollutants will be affected by atmospheric wind speed. Not only is the method used to detect atmospheric temperature, but also to detect the physical characteristics of aerosols and atmospheric wind speed at the same time. To sum up, it can provide references for studying various weather changes, physical effects and boundary layer temperature observations.
Funding
National Natural Science Foundation of China (41875034,41627807).
Acknowledgments
Statistical support was provided by Jingjing Liu and Li Wang. Writing assistance was provided by Jun Wang and Dengxin Hua. Optical Design support was provided by Qing Yan. Detection experiment was provided by Jingzhe Pang, Dong Bao, and Wanlin Zhang.
Disclosures
The authors declare no conflicts of interest.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
References
1. D. Liu, Y. Y. Yang, Z. T. Cheng, H. L. Huang, B. Zhang, T. Ling, and Y. B. Shen, “Retrieval and analysis of a polarized high-spectral-resolution lidar for profiling aerosol optical properties,” Opt. Express 21(11), 13084–13093 (2013). [CrossRef]
2. X. Yu, B. L. Chen, M. Min, X. Y. Zhang, L. L. Yao, Y. M. Zhao, L. D. Wang, F. Wang, and X. B. Deng, “Simulating return signals of a spaceborne high-spectral resolution lidar channel at 532 nm,” Optics Communications 417, 89–96 (2018). [CrossRef]
3. N. C. Wang, X. Shen, D. Xiao, I. Veselovskii, C. F. Zhao, F. T. Chen, C. Liu, Y. H. Rong, J. Ke, B. Y. Wang, and D. Liu, “Development of ZJU high-spectral-resolution lidar for aerosol and cloud: Calibration of overlap function,” Journal of Quantitative Spectroscopy and Radiative Transfer 257, 107338 (2020). [CrossRef]
4. J. C. Shang, T. Wu, C. Y. Yang, R. J. Hu, X. D. He, and Z. P. Chen, “Pressure and temperature retrieval of nitrogen respectively by analysis of spontaneous Rayleigh-Brillouin scattering profiles,” Opt. Commun. 436, 127–133 (2019). [CrossRef]
5. X. S. Xia, H. Yuan, J. B. Liu, B. D. Gai, X. L. Cai, J. W. Guo, Y. O. Jin, and F. T. Sang, “Method to improve the resolution of a non-parallel Fabry-Perot etalon,” Appl. Opt. 57(29), 8757–8765 (2018). [CrossRef]
6. F. H. Shen, J. Ji, C. B. Xie, Z. Wang, and B. X. Wang, “High-spectra-resolution Mie Doppler lidar based on a two-stage Fabry-Perot etalon for tropospheric wind and aerosol accurate measurement,” Appl. Opt. 58(9), 2216–2225 (2019). [CrossRef]
7. C. Y. She, R. J. Alvarez, L. M. Caldwell, and D. A. Krueger, “High-spectral-resolution Rayleigh-Mie lidar measurement of aerosol and atmospheric profiles,” Opt. Lett. 17(7), 541–543 (1992). [CrossRef]
8. J. W. Hair, L. M. Caldwell, and D. A. Krueger, “High-spectral-resolution lidar with iodine-vapor filters: measurement of atmospheric-state and aerosol profiles,” Appl. Opt. 40(30), 5280–5294 (2001). [CrossRef]
9. Z. M. Zang, X. Shen, Z. F. Zheng, Y. P. Zhang, Y. D. Zhou, N. C. Wang, L. Wu, and D. Liu, “Design of a high-spectral-resolution lidar for atmos-pheric temperature measurement down to the near ground,” Appl. Opt. 58(35), 9651–9661 (2019). [CrossRef]
10. Z. S. Liu, D. Wu, and J. T. Liu, “Low-altitude atmospheric wind measure-ment fromthe combined Mie and Rayleigh backscattering by doppler lidar with an iodine filter,” Appl. Opt. 41(33), 7079–7086 (2002). [CrossRef]
11. G. Y. Bo, B. Liu, and Z. Q. Zhong, “Rayleigh-Raman-Mie Lidar for Atmos-pheric Temperature and Aerosol Profiles Measurement,” Acta Phys. Sin-ch. ED. 30, 27–33 (2010). [CrossRef]
12. D. X. Hua, M. Uchida, and Takao Kobayashi, “Ultraviolet high-spectral-resolution Rayleigh-Mie lidar with a dual-pass Fabry-Perot etalon for measuring atmospheric temperature profiles of the troposphere,” Opt. Lett. 29(10), 1063–1065 (2004). [CrossRef]
13. D. X. Hua, M. Uchida, and T. Kobayashi, “Ultraviolet Rayleigh-Mie lidar with Mie-scattering correction by Fabry-Perot etalons for temperature profiling of the troposphere,” Appl. Opt. 44(7), 1305–1314 (2005). [CrossRef]
14. R. C. Zhao, H. Y. Xia, X. K. Dou, and D. S. Sun, “Correction of temperature influence on the wind retrieval from a mobile Rayleigh Doppler lidar,” Chinese Phys. B 24, 234–240 (2015). [CrossRef]
15. M. J. ShangGuan, H. Y. Xia, X. K. Dou, C. Wang, J. W. Qiu, P. Y. Zhang, Z. F. Shu, and X. H. Xue, “Comprehensive wind correction for a Rayleigh Doppler lidar from atmospheric temperature and pressure influences and Mie contamination,” Chinese Phys. B 24(9), 094212 (2015). [CrossRef]
16. H. Y. Xia, X. K. Dou, M. J. Shangguan, R. C. Zhao, D. Solar, C. Wang, J. Qiu, Z. Shu, X. Xue, Y. Han, and Y. Han, “Stratospheric temperature measurement with scanning Fabry-Perot interferometer for wind retrieval from mobile Rayleigh Doppler lidar,” Opt. Express 22(18), 21775–21789 (2014). [CrossRef]
17. F. H. Shen, C. B. Xie, and C. Q. Qiu, “Fabry-Perot etalon-based ultra-violet trifrequency high-spectral-resolution lidar for wind, temperature, and aerosol measurements from 0.2 to 35 km altitude,” Appl. Opt. 57, 9328–9340 (2018). [CrossRef]
18. P. Zhang and K. Liang, “Improved method for gas temperature and pressure retrieval in Brillouin lidar remote sensing,” Appl. Opt. 60(3), 652–661 (2021). [CrossRef]
19. Y. J. Li, X. Lin, and Y. Yang, “Temperature characteristics at altitudes of 5-80 km with a self-calibrated Rayleigh-rotational Raman lidar: A summer case study,” Journal of Quantitative Spectroscopy and Radiative Transfer 188, 94–102 (2017). [CrossRef]
20. B. Witschas, C. Lemmerz, and O. Reitebuch, “Daytime measurements of atmospheric temperature profiles (2-15 km) by lidar utilizing Rayleigh-Brillouin scattering,” Opt. Lett. 39(7), 1972–1975 (2014). [CrossRef]
21. J. Wang, M. Yuan, N. Chen, T. He, J. Liu, Q. Yan, T. He, L. Wang, W. Xin, and D. Hua, “Continuous tunable cavity Fabry-Perot interferometer by using potassium dideuterium phosphate with two ring electrodes,” Appl. Opt. 58(16), 4425–4430 (2019). [CrossRef]
22. J. Wang, J. Z. Pang, N. Chen, W. L. Zhang, J. J. Liu, L. Wang, Q. Yan, and D. X. Hua, “Detection of atmospheric temperature by using polarization high-spectral-resolution lidar,” Appl. Opt. 60(8), 2109–2117 (2021). [CrossRef]
23. L. H. Jin, E. Kondoh, and K. Takizawa, “Tunable electro-optic crystal Fabry-Perot filter,” in Sixth International Symposium on Precision Engineering Measurements and Instrumentation, 75443S (December 2010), Proc. SPIE 7544.
24. J. Wang, J. Pang, N. Chen, H. Qi, J. Liu, Q. Yan, Li Wang, and D. Hua, “Simultaneous measurement of gas temperature and pressure based on Rayleigh-Brillouin scattering in kinetic region of less than one standard atmospheric pressure,” Optics Communications 485, 126721 (2021). [CrossRef]
