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Mobile Rayleigh Doppler lidar for wind and temperature measurements in the stratosphere and lower mesosphere

Xiankang Dou, Yuli Han, Dongsong Sun, Haiyun Xia, Zhifeng Shu, Ruocan Zhao, Mingjia Shangguan, and Jie Guo

Open Access Open Access

Abstract

A mobile Rayleigh Doppler lidar based on the molecular double-edge technique is developed for measuring wind velocity in the middle atmosphere up to 60 km. The lidar uses three lasers with a mean power of 17.5 W at 355 nm each and three 1 m diameter telescopes to receive the backscattered echo: one points to zenith for vertical wind component and temperature measurement; the two others pointing toward east and north are titled at 30° from the zenith for zonal and meridional wind component, respectively. The Doppler shift of the backscattered echo is measured by inter-comparing the signal detected through each of the double-edge channels of a triple Fabry-Perot interferometer (FPI) tuned to either side of the emitted laser line. The third channel of FPI is used for frequency locking and a locking accuracy of 1.8 MHz RMS (root-mean-square) at 355 nm over 2 hours is realized, corresponding to a systematic error of 0.32 m/s. In this paper, we present detailed technical evolutions on system calibration. To validate the performance of the lidar, comparison experiments was carried out in December 2013, which showed good agreement with radiosondes but notable biases with ECMWF (European Centre for Medium range Weather Forecasts) in the height range of overlapping data. Wind observation over one month performed in Delhi (37.371° N, 97.374° E), northwest of China, demonstrated the stability and robustness of the system.

© 2014 Optical Society of America

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Figures (13)
Fig. 1
Fig. 1 Schematic view of the lidar receiver setup. Three cones are drawn for the eastward, northward and vertical pointing. The east and north pointing are at an angle of 30° from the zenith. The vertical and horizontal components of wind vector are determined by them, as shown in this figure.

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Fig. 2
Fig. 2 (a) Evolution of the wind velocity error versus the full width at half maximum (FWHM) of FPI bandpasses and spectral spacing between the double-edge channels (in units of Rayleigh spectrum width ΔνR at 226.5 K). (b) The Doppler sensitivity, i.e., percentage change in the normalized signal for a velocity of 1 m/s, plotted as a function of spectral spacing (in unit of FWHM of the FPI). T = 226.5 K.

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Fig. 3
Fig. 3 Schematic view of the lidar optical setup: BS, beam splitter; IS, integrating sphere, MF, multimode fiber; FOBS, fiber-optic beam splitter; PMT, photomultiplier tube; FPI, Fabry-Perot interferometer; TR, transient recorder.

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Fig. 4
Fig. 4 Data acquisition timing sequence

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Fig. 5
Fig. 5 Experimental result of integrating sphere stretching effect. The diameter of the integrating sphere used in this experiment is 25 cm. Both the input and exit port areas are 0.785 cm2. The experimental result is a 256 shots average.

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Fig. 6
Fig. 6 Illumination patterns under the function of the integrating sphere. (a) is the output illumination pattern by directly coupled into an multimode fiber and (c) is its normalized intensity distribution; (c) is the output illumination pattern coupled from the integrating sphere and (d) is its normalized intensity.

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Fig. 7
Fig. 7 Stability of the locked transmission and corresponding systemic error in LOS wind velocity. (a) presents the result of short-term tracking and simultaneous wind deviation over 2 hours on Oct. 12; (b) gives the long-term tracking for 11 days and mean statistics standard error in LOS wind velocity.

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Fig. 8
Fig. 8 (Top) Plotted actual atmospheric transmission curves with least-square fitted lines; The curve of locking channel is scanned by portion of the outgoing laser, while the two others are scanned by vertical backscatter from average of 6 km~6.75 km altitude, which are broaden by the random thermal motions of atmospheric particles. Each plot is 600 shots accumulated (~12 second) and the adjacent plots is changed in discrete frequency steps of about 190 MHz; (Bottom) Residual between measurement and fitted lines shape normalized to its peak level; the red line depict the fitting result with a set of Tenti S6 model line shapes subtract the central Gaussian line.

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Fig. 9
Fig. 9 The variation of FPI temperature and wind deviation with respect to time; in the upper right corner, the result of FFT analysis is present.

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Fig. 10
Fig. 10 (a) The USTC Rayleigh Doppler lidar in experiment. (b) Profiles of backscattered signals at 02:00 Am, Dec. 7, 2013. The height resolution is changed from 0.2 km to 1 km at 40 km altitude.

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Fig. 11
Fig. 11 Lidar backscatter ratio, temperature and vertical wind measured by the Rayleigh Doppler lidar (solid line with error bars) on 21 December 2013. The temperature measurement is compared with ECMWF (olive plots), CIRA model (green plots), and radiosonde measurement (blue dashed line). The sparse-line area indicates altitudes with aerosol contribution as measured by the lidar.

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Fig. 12
Fig. 12 Profiles of horizontal wind velocity and direction measured by the Rayleigh Doppler lidar compared with data from radiosonde at LT 22:40, December 21 and at LT 06:54, December 23. The upper altitudes of aerosol loaded region on two days is 27.5 km, as is the sparse-line area presented in this figure. This result is 6000 shots accumulated.

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Fig. 13
Fig. 13 Time-altitude cross section of semi-continuous horizontal wind field observed by mobile Rayleigh Doppler lidar in December 2013. For every night, 6 hours’ measurement is presented in this figure from LT 00:00 to 06:00 with time resolution of half an hour. The maximum velocity and direction error in this experiment are estimated to be 9.2 m/s and 14.3°, respectively.

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Tables (1)

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Table 1 Key parameters of the mobile Rayleigh Doppler Lidar

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Equations (4)

Equations on this page are rendered with MathJax. Learn more.

R(z)= C N 1 (z)- N 2 (z) C N 1 (z)+ N 2 (z) .
V h (z)= λ 2sinθ Δ v d (z)
ε= 1 Θ⋅SNR ,
Δv(T)= 5.1GHz 75.44nm Δl(T),
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