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A high spectral resolution lidar (HSRL) for simultaneously detecting vertical wind, temperature, and the backscattering ratio in the troposphere is developed. The atmospheric temperature and vertical wind are determined by the Rayleigh scattering spectrum width and Mie scattering spectrum Doppler shift, respectively. The influence of temperature and the backscattering ratio on vertical wind measurement accuracy is also analyzed. The temperature and backscattering ratio affect the wind measurement, which produces the vertical wind offset. A correction considering the effects of the method is conducted considering real-time and on-site temperature profiles and the backscattering ratio to correct wind measurement sensitivity. Measurements of HSRL taken under different weather conditions (fine and hazy days) are demonstrated. Good agreement between the HSRL and the radiosonde measurements was obtained considering lapse rates and temperature inversions. The maximum temperature offsets were 1.3 and 4 K at a height of 1.5 km on fine and hazy days, respectively. Then, real-time and on-site temperature profiles and backscattering ratios were applied to correct the real-time and on-site wind. The corrected wind profiles showed satisfactory agreement with the wind profiles acquired from the calibrated wind lidar. The maximum detection offsets of the retrieved wind speed were reduced from 1 m/s to 0.55 m/s and from 1 m/s to 0.21 m/s, respectively, which were decreases of 0.45 and 0.79 m/s in fine and hazy days after correction of sensitivity. It is evident that the corrected wind method can reduce the influence of temperature and the backscattering ratio on the wind measurement and the offset of vertical wind. The reliability of the method is also proven.

Aimed at addressing the disadvantages of restricted retrieval height caused by signal-to-noise ratio (SNR) differences between high-quantum-number and low-quantum-number pure rotational Raman scattering signals (PRRSs) obtained with the traditional retrieval method, an optimized retrieval method is proposed for atmospheric temperature profiling based on rotational Raman lidar. This method allows independent alternating solutions to high- and low-quantum-number PRRSs, where high-quantum-number PRRS lidar returns are used to solve the channel constant, and low-quantum-number PRRS returns with a high SNR are used for retrieving temperature profiles. The system sensitivity, SNR, and statistical error in temperature measurements by the two methods are first simulated and discussed, and the results are then compared to show that a higher SNR and stable sensitivity can be attributed to stable statistical errors with the optimized method. A further assessment is demonstrated by three sets of lidar data from a multifunctional Raman-Mie lidar system at the Xi'an University of Technology (34.233°N, 108.911°E). The retrieved atmospheric temperature profiles under different weather conditions are compared with radiosonde data; then, the temperature deviations are further evaluated, and a correlation analysis is performed to evaluate the reliability and correctness of the temperature data obtained by the optimized retrieval method. The results show that the effective temperature retrieval height can be greatly improved from 17 to 25 km under clear weather conditions, and a high correlation >0.99 and stable relative deviations of less than 5 K can be obtained up to 25 km. Additionally, the retrieval height can be extended from 8 to 16 km in cloudy weather, and the existence of an inversion layer can be successfully captured as well. It is evident that the proposed optimized method will provide a new and reliable retrieval theorem for atmospheric temperature profiles, and the proposed method is propitious for retrieving temperature profiles over a larger height range, even up to the lower stratosphere. It is also deduced that the proposed algorithm can favorably simplify the spectroscopic system for temperature detection in the future when the channel constant is determined in advance.

Long-term observations of atmosphere aerosol optical properties over Xi’an area have been carried out by a Raman-Mie lidar and a Micro-pulsed 3D Scanning Mie lidar, which were built at Xi’an University of technology with the laser wavelength of 355nm and 532nm, respectively. The Raman-Mie lidar is used for observation of the atmospheric temperature, water vapor and aerosol profiles simultaneously. In order to deeply discuss the temporal-spatial evolution of the mixed-layer within the urban boundary layer (UBL), the method of combining the absolute minimum of first derivative and second derivative of the range-squared-corrected signal (RSCS) of lidar was used to retrieve the mixed-layer depth (MLD). By using continuous observations of 24-hour (THI display), the MLD in temporal and spatial variation are clearly revealed. Also, the results of continuous observations from July 2006 to October 2011 have been analyzed for revealing the seasonal cycle and the annual cycle of the MLD. By analyzing the average MLD, it is obviously shown that the MLD of seasonal cycle is higher in summer than in winter over Xi’an area. Otherwise, by investigating the relationship of atmospheric boundary layer height, relative humidity and temperature, and the dependence characteristics and a general disciplinarian between them are then obtained. The achievement is of great importance for studying the proliferation of urban pollution and obtaining a complete meteorological status of the urban atmosphere.

A new high-accuracy pure rotational Raman (PRR) lidar system at a laser wavelength of 532.25 nm, based on a technique of wavelength division multiplexing (WDM), has been designed for profiling the atmospheric temperature of the low troposphere. A special WDM, which was usually used in fiber communication field, is designed to separate two PRR signals of N2 and O2 for temperature retrieval, and to simultaneously block Mie- and Rayleigh-scattering signals with a rejection rate of large than 107. A numerical calculation is simulated to verify the feasibility of the lidar system, and the results showed that the PRR lidar based on spectroscopic characteristic of the WDM is capable of measuring the atmospheric temperature vertical profiles in the low troposphere, and a statistical temperature error less then 1K was achieved up to a height of 3.3 km and 5 km for daytime and nighttime measurement, respectively, under conditions of 300 mJ laser energy, 25-cm-diameter telescope, 10 min observation time, solar radiance of 3×108 Wm-2sr-1nm-1 and atmospheric backscattering ratio less then 3.4.© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A Rayleigh-Mie scattering lidar system has been developed for temperature profiling of the troposphere using a new multi-cavity Fabry-Perot filter at 355 nm wavelength. The temperature error less than 1 K was obtained at 3.0 km altitude with a compact system.

A new Rayleigh scattering lidar system at eye-safe 355nm ultraviolet wavelength has been developed for measuring vertical profiles of atmospheric temperature in the lower troposphere, based on the high-spectral resolution lidar (HSRL) technique using two narrow-band Fabry-Perot filters. The Doppler broadened width of the Rayleigh backscatter signal was measured for the temperature analysis. The central frequency and the width of the two filters are carefully selected to optimize the detection sensitivity of the filter. In order to reject the intense Mie backscattering component introduced in the Rayleigh signal, which affects the temperature accuracy, the third narrow-band filter has been installed to measure the Mie scattering intensity. A signal processing method has been developed to derive the temperature profile. In a preliminary experiment, it was shown that the temperature sensitivity of the filter is 0.4%/K and the measurement error is about 1K at 2km height.

A Rayleigh–Mie Raman lidar system at a wavelength of 355 nm has been upgraded for simultaneous measurements of atmospheric temperature, relative humidity and aerosol profiles in the troposphere. Temperature is determined from the Rayleigh spectral linewidth. Water vapor is determined from the intensity of the water vapor vibration-Raman line centered at 407 nm. A high-resolution grating is used to separate the water vapor vibration-Raman line and Rayleigh–Mie scattering spectrally from the lidar returns. Two Fabry–Perot filters with a dual-pass optical layout are used to detect the temperature changes. The measurement shows that statistical temperature errors of less than 1 K are obtained up to a height of 3.5 km, and the uncertainties of the water vapor in relative humidity are less than 10% at a height of 2.5 km when using a lidar system with 200-mJ laser energy, a 25-cm-diameter telescope and 3.5-min observation time. The performance of the lidar system is evaluated by comparison with radiosonde measurements. Close agreements are obtained between the lidar and radiosonde measurements.