Stratospheric temperature measurement with scanning Fabry-Perot interferometer for wind retrieval from mobile Rayleigh Doppler lidar

Temperature detection remains challenging in the low stratosphere, where the Rayleigh integration lidar is perturbed by aerosol contamination and ozone absorption while the rotational Raman lidar is suffered from its low scattering cross section. To correct the impacts of temperature on the Rayleigh Doppler lidar, a high spectral resolution lidar (HSRL) based on cavity scanning Fabry-Perot Interferometer (FPI) is developed. By considering the effect of the laser spectral width, Doppler broadening of the molecular backscatter, divergence of the light beam and mirror defects of the FPI, a well-behaved transmission function is proved to show the principle of HSRL in detail. Analysis of the statistical error of the HSRL is carried out in the data processing. A temperature lidar using both HSRL and Rayleigh integration techniques is incorporated into the Rayleigh Doppler wind lidar. Simultaneous wind and temperature detection is carried out based on the combined system at Delhi (37.371°N, 97.374°E; 2850 m above the sea level) in Qinghai province, China. Lower Stratosphere temperature has been measured using HSRL between 18 and 50 km with temporal resolution of 2000 seconds. The statistical error of the derived temperatures is between 0.2 and 9.2 K. The temperature profile retrieved from the HSRL and wind profile from the Rayleigh Doppler lidar show good agreement with the radiosonde data. Specifically, the max temperature deviation between the HSRL and radiosonde is 4.7 K from 18 km to 36 km, and it is 2.7 K between the HSRL and Rayleigh integration lidar from 27 km to 34 km. ©2014 Optical Society of America OCIS codes: (010.0010) Atmospheric and oceanic optics; (120.0280) Remote sensing and sensors; (280.3340) Laser Doppler velocimetry; (280.3640) Lidar. References and links 1. J. W. Meriwether and A. J. Gerrard, “Mesosphere inversion layers and stratosphere temperature enhancements,” Rev. Geophys. 42, RG3003 (2004). 2. A. Gettelman, P. Hoor, L. L. Pan, W. J. Randel, M. I. Hegglin, and T. Birner, “The extratropical upper troposphere and lower stratosphere,” Rev. Geophys. 49(3), RG3003 (2011). 3. M. P. Baldwin, L. J. Gray, T. J. Dunkerton, K. Hamilton, P. H. Haynes, W. J. Randel, J. R. Holton, M. J. Alexander, I. Hirota, T. Horinouchi, D. B. A. Jones, J. S. Kinnersley, C. Marquardt, K. Sato, and M. Takahashi, “The quasi-biennial oscillation,” Rev. Geophys. 39(2), 179–229 (2001). 4. A. J. Gerrard, Y. Bhattacharya, and J. P. Thayer, “Observations of in-situ generated gravity waves during a stratospheric temperature enhancement (STE) event,” Atmos. Chem. Phys. 11(22), 11913–11917 (2011). 5. M. P. Baldwin and T. J. Dunkerton, “Stratospheric harbingers of anomalous weather regimes,” Science 294(5542), 581–584 (2001). 6. M. P. Baldwin, D. W. J. Thompson, E. F. Shuckburgh, W. A. Norton, and N. P. Gillett, “Weather from the stratosphere?” Science 301(5631), 317–319 (2003). 7. V. Ramaswamy, M. L. Chanin, J. Angell, J. Barnett, D. Gaffen, M. Gelman, P. Keckhut, Y. Koshelkov, K. Labitzke, J. J. R. Lin, A. O’Neill, J. Nash, W. Randel, R. Rood, K. Shine, M. Shiotani, and R. Swinbank, “Stratospheric temperature trends: Observations and model simulations,” Rev. Geophys. 39(1), 71–122 (2001). #215139 $15.00 USD Received 1 Jul 2014; revised 19 Aug 2014; accepted 20 Aug 2014; published 2 Sep 2014 (C) 2014 OSA 8 September 2014 | Vol. 22, No. 18 | DOI:10.1364/OE.22.021775 | OPTICS EXPRESS 21775 8. A. Behrendt, “Temperature measurements with lidar,” in Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, C. Weitkamp, ed (Springer, 2005). 9. M. Alpers, R. Eixmann, C. Fricke-Begemann, M. Gerding, and J. Höffner, “Temperature lidar measurements from 1 to 105 km altitude using resonance, Rayleigh, and Rotational Raman scattering,” Atmos. Chem. Phys. 4(3), 793–800 (2004). 10. X. Chu and G. C. Papen, “Resonance fluorescence lidar,” in Laser Remote Sensing, T. Fujii and T. Fukuchi, eds. (CRC, 2005). 11. M. L. Chanin and A. Hauchecorne, “Lidar studies of temperature and density using Rayleigh scattering,” in International Council of Scientific Unions Middle Atmosphere Handbook (National Aeronautics and Space Administration, 1984). 12. M. Gerding, J. Höffner, J. Lautenbach, M. Rauthe, and F.-J. Lübken, “Seasonal variation of nocturnal temperatures between 1 and 105 km altitude at 54° N observed by lidar,” Atmos. Chem. Phys. 8(24), 7465–7482 (2008). 13. W. N. Chen, C. C. Tsao, and J. B. Nee, “Rayleigh lidar temperature measurements in the upper troposphere and lower stratosphere,” J. Atmos. Sol.-Terr. Phys. 66(1), 39–49 (2004). 14. J. P. Vernier, L. W. Thomason, J. P. Pommereau, A. Bourassa, J. Pelon, A. Garnier, A. Hauchecorne, L. Blanot, C. Trepte, D. Degenstein, and F. Vargas, “Major influence of tropical volcanic eruptions on the stratospheric aerosol layer during the last decade,” Geophys. Res. Lett. 38, L12807 (2011). 15. O. E. Bazhenov, V. D. Burlakov, S. I. Dolgii, and A. V. Nevzorov, “Lidar observations of aerosol disturbances of the stratosphere over Tomsk (56.5° N; 85.0° E) in volcanic activity period 2006-2011,” Int. J. Opt. 2012, 1–10 (2012). 16. T. Shibata, M. Kobuchi, and M. Maeda, “Measurements of density and temperature profiles in the middle atmosphere with a XeF lidar,” Appl. Opt. 25(5), 685–688 (1986). 17. J. P. Burrows, A. Richter, A. Dehn, B. Deters, S. Himmelmann, S. Voigt, and J. Orphal, “Atmospheric remote sensing reference data from GOME: Part 2. Temperature-dependent absorption cross-sections of O3 in the 231 −794 nm range,” J. Quant. Spectrosc. Radiat. Transfer 61(4), 509–517 (1999). 18. A. Behrendt and J. Reichardt, “Atmospheric temperature profiling in the presence of clouds with a pure rotational Raman lidar by use of an interference-filter-based polychromator,” Appl. Opt. 39(9), 1372–1378 (2000). 19. A. Behrendt, T. Nakamura, M. Onishi, R. Baumgart, and T. Tsuda, “Combined Raman lidar for the measurement of atmospheric temperature, water vapor, particle extinction coefficient, and particle backscatter coefficient,” Appl. Opt. 41(36), 7657–7666 (2002). 20. A. Behrendt, T. Nakamura, and T. Tsuda, “Combined temperature lidar for measurements in the troposphere, stratosphere, and mesosphere,” Appl. Opt. 43(14), 2930–2939 (2004). 21. Y. Arshinov, S. Bobrovnikov, I. Serikov, A. Ansmann, U. Wandinger, D. Althausen, I. Mattis, and D. Müller, “Daytime operation of a pure rotational Raman lidar by use of a Fabry-Perot interferometer,” Appl. Opt. 44(17), 3593–3603 (2005). 22. E. Eloranta, “High spectral resolution lidar,” In Range-Resolved Optical Remote Sensing of the Atmosphere. C. Weitkamp, ed. (Springer, 2005). 23. G. G. Fiocco, G. Beneditti-Michelangeli, K. Maischberger, and E. Madonna, “Measurement of temperature and aerosol to molecule ratio in the troposphere by optical radar,” Nature 229, 78–79 (1971). 24. 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). 25. R. L. Schwiesow and L. Lading, “Temperature profiling by Rayleigh-scattering lidar,” Appl. Opt. 20(11), 1972– 1979 (1981). 26. H. Shimizu, S. A. Lee, and C. Y. She, “High spectral resolution lidar system with atomic blocking filters for measuring atmospheric parameters,” Appl. Opt. 22(9), 1373–1381 (1983). 27. H. Shimizu, K. Noguchi, and C. Y. She, “Atmospheric temperature measurement by a high spectral resolution lidar,” Appl. Opt. 25(9), 1460–1466 (1986). 28. C C. Y. She, R. J. Alvarez II, 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). 29. P. Piironen and E. W. Eloranta, “Demonstration of a high-spectral-resolution lidar based on an iodine absorption filter,” Opt. Lett. 19(3), 234–236 (1994). 30. Z. Liu, I. Matsui, and N. Sugimoto, “High-spectral-resolution lidar using an iodine absorption filter for atmospheric measurements,” Opt. Eng. 38(10), 1661–1670 (1999). 31. J. W. Hair, L. M. Caldwell, D. A. Krueger, and C. Y. She, “High-spectral-resolution lidar with iodine-vapor filters: measurement of atmospheric-state and aerosol profiles,” Appl. Opt. 40(30), 5280–5294 (2001). 32. D. Hua, M. Uchida, and T. Kobayashi, “Ultraviolet Rayleigh-Mie lidar with Mie-scattering correction by FabryPerot etalons for temperature profiling of the troposphere,” Appl. Opt. 44(7), 1305–1314 (2005). 33. D. Hua, M. Uchida, and T. Kobayashi, “Ultraviolet Rayleigh-Mie lidar for daytime-temperature profiling of the troposphere,” Appl. Opt. 44(7), 1315–1322 (2005). 34. W. Huang, X. Chu, J. Wiig, B. Tan, C. Yamashita, T. Yuan, J. Yue, S. D. Harrell, C. Y. She, B. P. Williams, J. S. Friedman, and R. M. Hardesty, “Field demonstration of simultaneous wind and temperature measurements from 5 to 50 km with a Na double-edge magneto-optic filter in a multi-frequency Doppler lidar,” Opt. Lett. 34(10), 1552–1554 (2009). 35. Z. S. Liu, D. C. Bi, X. Q. Song, J. B. Xia, R. Z. Li, Z. J. Wang, and C. Y. She, “Iodine-filter-based high spectral resolution lidar for atmospheric temperature measurements,” Opt. Lett. 34(18), 2712–2714 (2009). #215139 $15.00 USD Received 1 Jul 2014; revised 19 Aug 2014; accepted 20 Aug 2014; published 2 Sep 2014 (C) 2014 OSA 8 September 2014 | Vol. 22, No. 18 | DOI:10.1364/OE.22.021775 | OPTICS EXPRESS 21776 36. Z. Cheng, D. Liu, Y. Yang, L. Yang, and H. Huang, “Interferometric filters for spectral discrimination in highspectral-resolution lidar: performance comparisons between Fabry-Perot interferometer and field-widened Michelson interferometer,” Appl. Opt. 52(32), 7838–7850 (2013). 37. D. Liu, Y. Yang, Z. Cheng, H. Huang, B. Zhang, T. Ling, and Y. Shen, “Retrieval and analysis of a polarized high-spectral-resolution lidar for profiling aerosol optical properties,” Opt. Express 21(11), 13084–13093 (2013). 38. Z. Cheng, D. Liu, J. Luo, Y. Yang, L. Su, L. Yang, H. Huang, and Y. Shen, “Effects of spectr


Introduction
The middle atmosphere is that portion of the Earth's atmosphere between two temperature minima at about 12 km altitude (the tropopause) and at about 85 km (the mesopause), comprising the stratosphere and mesosphere.In spite of intensive research activities over the past decades, the underlying mechanisms for some phenomena in the region, for instance, the stratosphere temperature enhancement and the mesosphere inversion layer, remain poorly understood [1][2][3][4].The troposphere influences the stratosphere mainly through atmospheric waves propagating upward.Recent researches show that the stratosphere organizes the chaotic wave forcing from below to create long-lived changes in its circulation, and exerts impact on the tropospheric weather and climate.Thus, understanding the middle atmosphere is also essential for tropospheric weather prediction [5,6].Rocketsonde data are available through the early 1960s.However, such results are sporadic because the means for exploring the middle atmosphere are expensive.Even decades of rocket launches, radiosonde observations, satellite and aircraft measurements, provide only pieces of the whole picture [7].
Today, as one of the most promising remote sensing techniques, lidar for atmospheric researches has shown its inherited superiorities: including high spatial and temporal resolution, the potential of covering the height region from the boundary layer to the mesosphere, and allowing the detection of variable atmospheric parameters, such as temperature, pressure, density, wind, as well as the trace constituents.Particularly, temperature lidar techniques are approaching the maturity for routine observations [8].Specifically, the resonance fluorescence technique, the Rayleigh integration technique and the rotational Raman technique are combined to cover the height region from the lower thermosphere to ground [9].
The resonance fluorescence technique is restricted to the altitude range between 80 and 105 km, where exist layers of metallic species, such as Fe, Ca, Na, and K atoms or ions [10].
The Rayleigh integration lidar has been proved to be the simplest tool for temperature detection in the mesosphere and stratosphere.Temperature is calculated from the molecular number density by assuming hydrostatic equilibrium.In addition, the top-to-bottom integration retrieval needs a reference point with known temperature at the beginning [11].
However, in the lower stratosphere, the aerosol contamination and ozone absorption makes the lidar backscatter no longer proportional to the molecular number density [12,13].So the Rayleigh backscatter must be corrected carefully for temperature detection.Unfortunately, in the lower stratosphere, recent volcanic eruptions aggravate the aerosol disturbances, which cannot be treated as low and stable background anymore [14,15].The ozone layer, which absorbs ultraviolet energy from the sun, is located primarily in the stratosphere, at altitudes of 15 to 35 km.The impact of O 3 above 30 km on the Rayleigh temperature lidar can be ignored at working wavelength about 350 nm, with an error smaller than 1% [16].And one should note that, the absorption cross section of O 3 varies with wavelength.It is about 30 times lager at 532 nm than that at 355 nm [17].
Generally, to extend the detection altitude downward below 30 km, the rotational Raman technique is used for direct temperature detection, where two portions of pure-rotational Raman signals having opposite dependence on temperature are extracted by using filters with narrow bandwidth.The quite low rotational Raman scattering cross section requires sophisticated filters to suppress the disturbances from Rayleigh scattering and solar radiation.Nowadays, state-of-the-art rotational Raman lidars can be used to retrieve temperature up to 25 km.However, above this altitude, the statistical error usually exceeds 10 K [18][19][20][21].
The overview above comes to a conclusion that the temperature detection remains challenging in the low stratosphere, where the Rayleigh integration lidar is perturbed by aerosol contamination and ozone absorption while the rotational Raman lidar is suffered from its low scattering cross section.Fortunately, we demonstrate in this work that this dilemma can be resolved by using the so-called high spectral resolution lidar (HSRL).According to different implementations, the HSRL techniques fall into two categories [22].On the one hand, the entire Cabannes line (more precisely, the sum of the Laudau-Placzek line and the Brillouin doublet) is obtained by scanning either the Fabry-Perot interferometer (FPI) or the laser.Then the temperature is calculated from the fitted linewidth of the Cabannes line [23,24].On the other hand, the lidar signal is measured before and passing through a static filter, for instance, a fixed FPI, Michelson interferometers, atomic or molecular absorption cells.It resolves the temperature dependent transmission of the Cabannes line through the filters [25][26][27][28][29][30][31][32][33][34][35].In comparison, the former method is less efficient due to the fact that the ultra-narrow optical filter rejects most energy of the Cabannes scattering at each step of the scanning procedure.However, it shows immunity against the sunlight and Mie contamination [24].Of course, HSRL is also a multi-function technique except for temperature detection [36][37][38] The atmospheric temperature profile is necessary as an input parameter to the Rayleigh Doppler lidar.For example, a 1 K error on the actual temperature inside the sensing volume leads to a relative error of 0.2% of the true LOS wind for ADM-Aeolus [39].In this paper, a HSRL using scanning FPI is incorporated into a mobile Rayleigh Doppler lidar for temperature detection from 18 to 35 km, and the Rayleigh integration lidar is used to retrieve temperature from 30 to 65 km.The combined system permits atmospheric temperature and wind detection simultaneously.

Principle
The key instrument inside the optical receiver of the HSRL used in this work is a cavity tunable FPI.Three piezo-electric actuators are used to tune the cavity while the capacitance sensors fabricated onto the mirror surfaces are used to sense changes in parallelism and cavity length.The FPI is mounted in a sealed cell with high efficiency anti-reflection coated windows and heater assembly around.This eliminates the impact of changes in environmental pressure, temperature and humidity on both the capacitance micrometers and on the optical cavity length.
The transmission function of a perfect parallel plane FPI is where e R is the effective surface reflectance, ν is the optical frequency relative to the center frequency of the laser, θ is the angle of incidence of the light beams on the surfaces from within the interface, μ is the effective refractive index of the interspace, is the free spectral range.p T is the peak transmittance given by ( ) where A is the surface absorptance.For an air-gapped FPI used in this paper, where 1 μ ≈ , the transmission function can be written as ) where ( ) ( ) , e ℜ denotes the real part of a complex number.Utilizing ( ) ( ) of Fourier series can be derived from Eq. ( 3) to describe the FPI transmission: This Fourier-series-type formulation above has been found particularly useful for further evaluation and computation of experimental profiles mainly because it permits simple convolution with other common functions, notably Gaussian and Lorentzian functions [40].
In our lidar system, a multimode fiber delivers the atmospheric backscatter from telescope to receiver.This configuration provides mechanical decoupling and remote placement of the lidar components.Furthermore, the fiber reduces the field of view of the telescope at the input end and defines the divergence of the collimated beam normal to the FPI at the output end.Suppose the incident illumination is uniformly distributed under the function of a modescrambler [41,42], the actual transmission function is simply the sum of all rays from the normal to the half-maximum divergence 0 θ : Substituting the following power-reduction and sum-to-product formulas into Eq.( 5) ( ) ( ) During the scanning procedure of the FPI, if the frequency ν shifts a range about FSR ν Δ , the cosine phase term will change 2nπ rapidly.On the contrary, the variation of the sinc term according to the same change of ν is small enough to be neglected.Therefore, Eq. ( 8) can be approximated as: where, for simplicity, let ( ) ( ) To evaluate the effect of the beam divergence to the transmission function, let n = 30, ( ) 0 sin c 30ϕ = 0.98 is calculated using the system parameters listed in Table 1.
The spectrum of the backscatter is broadened due to the random thermal motions of the air particles.The aerosol backscatter spectrum ν since the Brownian motion of aerosol particles does not broaden the spectrum significantly [43].
In the low pressure altitude, the inelastic Brillouin scattering is negligible and the molecular motion is thermally dominated.Thus the scattering lineshape takes the form of a thermally broadened Gaussian profile [39,44].
Note that, are the half-width at the 1/e intensity level of the spectra of the outgoing laser and the Rayleigh backscatter, respectively.Where, k is the Boltzmann's constant, a T is the atmosphere temperature, m is the average mass of the atmospheric molecules, and λ is the laser wavelength.The transmission of the aerosol backscatter is a convolution of the transmission function of the FPI and the spectrum of aerosol backscatter:


, the convolution in Eq. ( 12) can be expressed analytically [45]: Comparison of Eq. ( 4), ( 9) and ( 13) shows that, even considering the divergence of the incident light, the convolution is derived by concise multiplication of each successive term by where is the global parameter for all kinds of mirror defects [40].Since the convolution of two Gaussian functions yields still a Gaussian function.Finally, the transmission function of aerosol backscatter can be written as Similarly, the transmission of the Rayleigh backscatter is If aerosols are exist, the photon number corresponding to the mixed backscatter collected by the telescope is described as follow: where L E is the energy of the laser pulse, o η accounts for the optical efficiency of the transmitted signal, q η is the quantum efficiency, h is the Planck constant, 0 A is the area of the telescope, ( )  The backscatter is detected using photomultiplier tubes (HAMAMATSU Model R7400P-03) and acquired with transient recorders (Licel Model TR 20-160), which provides 10 5 dynamic range by combination of A/D and photo counting functions.It is a great challenge to control the frequency drift of the laser to an order of 1 MHz during the FPI scanning process on a mobile platform, since the scanning process may take few minutes or even one hour, depending on the number of sampling steps and the dwell time at each frequency step.So, a secondary solid FPI is used to monitor the frequency drift of the outgoing laser.Then the frequency drift can be measured and compensated in the data processing.
To obtain the transmission of atmospheric backscatter through the FPI, the cavity spacing is scanned linearly over 20 GHz (100 sampling steps).At each step, the time-gated backscatter is summed up for 100 laser shots.After frequency drift compensation, the transmission curves at different altitudes are analyzed by applying a least squares fit procedure to Eq. ( 19).Then, temperature profile is calculated from the linewidths of the fitted curves.It is worth a mention that temperature values derived at different altitudes are independent of each other, since no response function need to be established in the retrieval.
As the inset shown in Fig. 3, the optical receiver is linked using fused fiber couplers, which improve the compactness and stabilization of the system.The FPIs are sealed against pressure change introduced by the air conditioning inside the trucks.Furthermore, the temperature fluctuation of the optical receiver is controlled under 0.01 K.  To validate the performance of the HSRL for low stratospheric temperature detection, comparison experiment is carried out at 6:54 Am, on Dec. 23, 2013.Temperature profiles derived from HSRL, Rayleigh integration (RI) lidar and radiosonde are plotted in Fig. 7.The temperature difference between HSRL and RI lidar, as well as the difference between HSRL and radiosonde are also shown.All the results show good agreement in the altitude from 26 km to 36 km, with a max deviation of 2.7 K. In the lower altitude, the temperature profile from RI lidar deviates from the results from HSRL and radiosonde obviously with a max value of 22.8 K, which may due to the aerosol contamination (as shown in Fig. 7) and the ozone absorption.On the contrary, acceptable agreement between HRSL and radiosonde is achieved with a max deviation of 4.7 K from 18 km to 36 km.Suppose the photon counts obey the Possion statistics at each step during the FPI scanning and the transmission in Fig. 6(a) can be approximated as a Gaussian shape, the standard deviation in estimating the transmission bandwidth in the best fit is where F ν Δ is the half-width at the 1/e intensity level of the transmission curve under estimation in Fig. 6(a).N is the total photon counts at a given altitude [47].
In the calibration, transmission of the laser pulse through the FPI is measured dozens of times and averaged, allowing us ignore the error in estimating the bandwidth of the curves in Fig. 4. Comparing Eq. ( 15) with Eq. ( 16), one can approximate that ( ) ( ) . So the statistical error of the obtained temperature profile from each FPI is calculated as The measurement on the two FPI channels is uncorrelated, reducing the final statistical uncertainty by a factor of 2 .The error bar of the temperature profile derived from HSRL is shown in Fig. 7.As we mentioned at the beginning, the HSRL built in this work is for correcting the temperature and pressure effects on the wind retrieval from the Rayleigh Doppler lidar [39].It is worth a mention that the pressure profiles are taken from the standard atmosphere when radiosonde data are not available.As shown in Fig. 7, in the wind retrieval, temperature values under and above the altitude of 35km are adopted from HSRL and RI lidar, respectively.Examples of wind detection in the altitude from 15 km to 60 km are shown in Fig. 8. Simultaneous radiosonde results are plotted for comparison.The wind speed and direction derived from Rayleigh Doppler lidar and radiosonde agree with each other in the two cases.The radiosonde data are sporadic on Dec. 24, 2013, which is due to the low GPS signal tracked by the ground antenna.

Conclusion and future research
Temperature profile plays an important role in atmospheric research.In addition, it is the input parameter to other remote sensing lidars.High spectral resolution technique and Rayleigh integration technique were integrated into one lidar to correct the temperature effect on the Rayleigh Doppler lidar.The combined system permits simultaneous detection of temperature and wind profiles from stratosphere to lower mesosphere.Despite the aerosol contamination and ozone absorption, temperature derived from HSRL shown good agreement with the radiosonde data in the lower stratosphere.
We noticed that the pulse duration decreases from 6.8 ns to 5.0 ns as the pulse energy decaying from 350 mJ to 150 mJ, so the spectral width of the outgoing laser pulse should be monitored.For future research, one channel of the FPI will be used to extend the temperature detection to the troposphere, where the spectrum of the molecular backscatter cannot be approximated as a simple Gaussian function, the Brilliouin doublet must be considered.
This work is on the assumption that atmospheric temperature is stable during the scanning of the FPI and the vertical component of the atmosphere wind is neglected.In the future, we need to shorten the scanning time without sacrificing the signal-to-noise ratio.Since high power laser and large telescope are adopted, we are going to use adaptive optics to improve the fiber-coupling efficiency [48].
The normalized spectra of aerosol backscatter ( ) M I ν and molecule backscatter (Laudau-Placzek line) ( ) R I ν are approximated by Gaussian functions normalized to unit area:

(
the surface defect of the parallel mirrors can also approximated as a Gaussian distribution overlap factor at range R , R β and M β are the Rayleigh and Mie volume backscatter coefficients, d τ is the detector's response time attenuation coefficient.#215139 -$15.00USD Received 1 Jul 2014; revised 19 Aug 2014; accepted 20 Aug 2014; published 2 Sep 2014 (C) 2014 OSA 8 September 2014 | Vol.22, No. 18 | DOI:10.1364/OE.22.021775| OPTICS EXPRESS 21781telescope to the optical receiver.At the other end of the multimode fiber, the light is split and collimated into three beams: one for energy monitoring and the other two beams passing through a cavity-scanning FPI (ICOS Model ET116FS-1068) for measuring transmission curves.The FPI is designed for Doppler lidar at the beginning.It consists of three subchannels with different cavity spacing.The left and right channels used for double-edged technique in the Doppler lidar are both for temperature detection now.And the third (locking) channel is used to monitor the frequency drift of laser relative to FPI.

Fig. 3 .
Fig. 3. Schematic setup of the HSRL Lidar with system-level optical frequency control, and interior view of the compact receiver (inset, lower right corner).

Fig. 6 .
Fig. 6. (a): Measured transmission curves of the backscatter (from 18 km) through the two channels (circle) and their best fit results (dash line), (b): Residual between the measured transmissions and the fit results, (c): Profiles of transmitted backscatter along altitude at given frequencies labeled in (a).

Fig. 8 .
Fig. 8. Profiles of wind speed and direction derived from Doppler lidar (solid line) and radiosonde (circle).
September 2014 | Vol.22, No. 18 | DOI:10.1364/OE.22.021775| OPTICS EXPRESS 21784 from 18 km to 36 km are found and averaged.Center frequencies of the left and the right channels can be calculated using the parameters ( 0