Orthogonal retrieval algorithm of atmospheric temperature profiles from pure rotational Raman lidar signals.

Aimed at the stability of calibration coefficients in a general non-orthogonal retrieval algorithm (NRA) of pure rotational Raman lidars (PRRLs), an orthogonal retrieval algorithm (ORA) of atmospheric temperature profiles based on the orthogonal basis function is proposed. This algorithm eliminates the correlation between the calibration coefficients in the NRA to reduce the influence of the number of calibration points and the selection scheme on the calibration coefficients. In this paper, the stabilities of calibration coefficients in the NRA and ORA are compared and analyzed, and the data analysis for atmospheric temperature profiles with a time resolution of minute-level are given, based on the developed Cloud Precipitation Potential Evaluation (CPPV) lidar data and the parallel radiosonde temperature data. The analysis results show that coefficients of variation (CVs) of ORA calibration coefficients are one order of magnitude smaller than those of NRA coefficients. The mean deviation of the ORA retrieval results is roughly reduced by 16.1% compared with the NRA, and the root-mean-square deviation is roughly reduced by 15.0% compared with the NRA. Therefore, the temperature retrieval performance of the ORA is better than that of the NRA.

Optimized method for simultaneous detection of fog/aerosol particle size distribution.

We propose forward/lateral scattering of dual-wavelength (ultraviolet and short-wave near-infrared bands) radiation to simultaneously detect aerosol particles and fog droplet size distribution in an open atmosphere. The size distributions were described using a gamma distribution. A light-scattering detection system was optimized and designed, and the final wavelengths and scattering angles of ∼ 350 nm and ∼ 1100 nm, and 1°, 2°, 12°, and 35°, respectively, were selected. Numerical simulation analyses and measurements were performed for the proposed detection scheme. The results confirmed that the proposed method is feasible and can rapidly acquire the fog droplet spectrum and aerosol particle size spectrum distribution in an open environment. The system structure of the method is simple and easy to implement, with high detection results and accuracy.

Error simulation of atmospheric scattered radiance and its influence on slant visibility based on the SBDART model and Monte Carlo method.

Atmospheric scattered radiance is an important factor affecting slant visibility measurement in the daytime. This paper explores atmospheric scattered radiance errors and their influence on slant visibility measurements. Considering the difficulty in error synthesis of the radiative transfer equation, an error simulation scheme based on the Monte Carlo method is proposed. An error simulation and error analysis for atmospheric scattered radiance was carried out based on the Santa Barbara DISTORT atmospheric radiative transfer (SBDART) model and the Monte Carlo method. The error in aerosol parameters including the single-scattering albedo (SSA), the asymmetry factor, and the aerosol optical depth (AOD), was simulated by a random number and random error under different normal distributions, and the error influence of aerosol parameters on the error in the solar irradiance and 33-layer atmosphere scattered radiance is discussed in detail. The maximum relative deviations of the output scattered radiance at a certain slant direction are 5.98%, 1.47%, and 2.35%, when SSA, the asymmetry factor, and the AOD obey the normal distribution of (0, 5). The error sensitivity analysis also confirms that the SSA is the most sensitive factor affecting atmospheric scattered radiance and the total solar irradiance. Then, according to the error synthesis theory, we investigated the error transfer effect of three error sources related to the atmosphere based on the contrast ratio between the object and the background. The simulation results show that the error in the contrast ratio caused by solar irradiance and scattered radiance is lower than 6.2% and 2.84%, indicating the main role in contributing to the error transfer of slant visibility. Further, the comprehensive process of the error transfer in slant visibility measurements was demonstrated by a set of lidar experiments and the SBDART model. The results provide a reliable theoretical basis for the measurement of atmospheric scattered radiance and slant visibility, which is of great significance to improve the measurement accuracy of slant visibility.

Correction method for temperature measurements inside clouds using rotational Raman lidar.

Rotational Raman lidar is an important technique for detecting atmospheric temperature. However, in cloud regions with strong elastic scattering conditions, elastic scattering crosstalk (ESC) is prevalent due to insufficient out-of-band suppression of the optical filter, resulting significant deviations in temperature retrieval. To address this challenge, a temperature correction technique for optically-thin clouds based on the backscatter ratio is proposed. Using the least-squares method, a temperature correction function is formulated based on the relationship between the ESC and backscatter ratio of clouds. Subsequently, the backscatter ratio is used to correct the rotational Raman ratio of clouds, thereby obtaining the vertical distribution of atmospheric temperature within the cloud layer. The feasibility of this method was assessed through numerical simulations and experimentally validated using a temperature and aerosol detection lidar at the Xi'an University of Technology (XUT). The results indicate that the difference between the retrieved temperature profile under high signal-to-noise ratio conditions and radiosonde data is less than 1.5 K. This correction technique enables atmospheric temperature measurements under elastic scattering conditions with a backscatter ratio less than 115, advancing research on atmospheric structure and cloud microphysics.

Temperature measurement of cloud or haze layers based on Raman rotational and vibrational spectra.

Pure rotational Raman lidar is often used for atmospheric temperature profile measurements. However, high elastic scattering suppression ratios (>107) are required for temperature measurement in clouds and haze, which imposes stringent requirements on spectral separation techniques. To solve this problem, a lidar measurement technique based on vibrational and rotational Raman spectra is proposed. Using nitrogen vibrational and rotational Raman scattering to obtain temperature profiles under strong elastic scattering, combined with the dual-rotational Raman temperature measurements under weak elastic scattering, a vertical distribution of atmospheric temperature including cloud and haze layers, can be obtained. The feasibility of the method was verified by numerical simulation. The Raman lidar for temperature measurements was established in Xi'an University of Technology, and the obtained temperature results show good agreement with the radiosonde measurements. The proposed method combines the high sensitivity of the dual-rotational Raman method and the high Mie-scattering suppression of the vibrational Raman method, thus further improving the adaptability of Raman lidar to cloudy and hazy air conditions and supporting atmospheric and cloud physics research.

Measurement method for slant visibility with slant path scattered radiance correction by lidar and the SBDART model.

Different from the existing methods for estimating averaged slant visibility by lidar and the traditional Koschmieder visibility formula, a measurement method for slant visibility is fundamentally proposed in this paper that considers the correction of slant path scattered radiance. Lidar is adopted to provide aerosol parameters, including optical depth and scattering parameters, and the SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer) model is used to solve the radiative transfer equation to obtain the corresponding radiance distribution; thus, the corrected apparent brightness contrast between the object and background along the slant path is used to achieve accurate slant visibility. Based on the measurement principle of slant visibility, a theoretical simulation and an analysis of the slant path scattered radiance are performed, and the resulting slant visibility is studied in detail in this paper.

Improved algorithm of aerosol particle size distribution based on remote sensing data.

The optical parameters (extinction or backscatter coefficients) of multi-wavelength beams can be used for the retrieval of the aerosol particle size distribution (APSD). An improved algorithm for APSD and aerosol microphysical parameters (AMPs) is studied and discussed by using only multi-wavelength extinction coefficients data. The regularized algorithm and prior value are combined for the retrieval of APSD and AMPs. The regularization algorithm, based on minimum discrepancy principle and averaging procedure, is used for the retrieval of fine-mode APSD and an averaging procedure that can achieve stable outputs is proposed. The 1% averaging result near the minimum of the discrepancy is selected and verified. Based on the inversion results of fine mode from the regularization algorithm, the lognormal distribution with a prior value (model radius) is applied to reconstruct the coarse mode of APSDs through fitting the data. The comprehensive application of the regularization algorithm and averaging process improves the stability of the inversion in the fine mode, and the use of the prior value broadens the inversion radius range of APSD. The complex refractive index need not be assumed for this method. The inversion error for different types of aerosols is analyzed and studied. The reliability of the algorithm is tested and verified by many typical APSDs and the measured APSDs by particle size spectrometer in different pollution days. The algorithm sensitivity analysis is also provided and discussed. The algorithm can obtain reliable inversion of APSD and AMPs with large radius range.

Optimized retrieval method for atmospheric temperature profiling based on rotational Raman lidar.

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.

Precise size distribution measurement of aerosol particles and fog droplets in the open atmosphere.

A precise measurement for the aerosol particle size distribution and fog droplet size distribution simultaneously in the open atmosphere is proposed. The extinction coefficient and small-angle forward scattering measurement are integrated into the detection for particle size distribution in the open atmosphere, and can achieve the fine detection of the particles in the atmosphere with radius between 0.1 to 30 µm. The key technology including optimal scattering angle in small-angle forward scattering measurement and optimal wavelengths selection are discussed and solved in detail. The fourteen different particle size distributions including aerosol size distributions and fog droplet size distributions are used for the determination of optimal forward scattering angle and wavelengths. The optimal forward scattering angle is calculated to be 1.1°. Seven wavelengths for extinction coefficients and five wavelengths for forward scattering coefficients are chosen for the retrieval of particle size distribution in the measurement. The regularization inversion of optical parameters for the retrieval of particle size distribution is described. The aerosol particle size distributions measured by particle spectrometer and actual fog particle size distributions are used for the method test and the reconstructions of particle size distributions. The inversion results show that the method can achieve the precise measurements of aerosol particle size distribution and fog droplet size distribution. The error influence on the inversion results of distributions is discussed. Based on the sensitivity analysis of inversion results, the feasibility of measurement in the real atmosphere is analyzed and discussed, and the scheme of detection system is provided.

Research on an adaptive filter for the Mie lidar signal.

The statistical properties of the noise in the Mie lidar signal are analyzed by the statistical hypotheses testing method. Based on this, an adaptive filter is proposed to eliminate the noise. The least mean square error algorithm is used to achieve optimal filtering, in which the mean square error is minimized by adjusting the filter's weight matrix. The validity of the adaptive filter is verified by numerical simulation and experimental data retrieving. In the numerical simulation, the signal-to-noise ratio of the adaptive filter is larger than that of the wavelet transform filter, and the mean square error of the output of the adaptive filter is less than the wavelet transform filter. In experimental data retrieving, the filtered lidar signals of the adaptive filter and wavelet transform filter are used to retrieve the extinction coefficient respectively in different weather conditions. The amplitude of the ripples in the extinction coefficient profile of the adaptive filter is less than that of the wavelet transform filter. Additionally, the adaptive filter's extinction coefficient profile is smoother than that of the wavelet transform filter. The detail of the extinction coefficient is displayed more clearly in the profile of the adaptive filter. The research result is of great importance for improving the accuracy of lidar data retrieving.

Variations in the water vapor distribution and the associated effects on fog and haze events over Xi'an based on Raman lidar data and back trajectories.

A combination of more than two years of water vapor lidar data with back trajectory analysis using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model was used to study the long-range transport of air masses and the water vapor distribution characteristics and variations over Xi'an, China (34.233° N, 108.911° E), which is a typical city in Northwest China. High-quality profiles of the water vapor density were derived from a multifunction Raman lidar system built in Xi'an, and more than 2000 sets of profiles with >400 nighttime observations from October 2013 to July 2016 were collected and used for statistical and quantitative analyses. The vertical variations in the water vapor content were discussed. A mutation height of the water vapor exists at 2-4 km with a high occurrence rate of ∼60% during the autumn and winter seasons. This height reflects a distinct stratification in the water vapor content. Additionally, the atmospheric water vapor content was mainly concentrated in the lower troposphere, and the proportion of the water vapor content at 0.5-5 km accounted for 80%-90% of the total water vapor below 10 km. Obvious seasonal variations were observed, including large water vapor content during the spring and summer and small content during the autumn and winter. Combined with back trajectory analysis, the results showed that markedly different water vapor transport pathways contribute to seasonal variations in the water vapor content. South and southeast airflows dominated during the summer, with 30% of the 84 trajectories originating from these areas; however, the air masses during the winter originated from the north and local regions (64.3%) and from the northwest (27%). In addition, we discussed variations in the water vapor during fog and haze weather conditions during the winter. A considerable enhancement in the mean water vapor density at 0.5-3 km exhibited a clear positive correlation (correlation coefficient >0.8) with the PM2.5 and PM10 concentrations. The results indicate that local airflow trajectories mainly affect water vapor transport below the boundary layer, and that these flows are closely related to the formation of fog and haze events in the Xi'an area.

Optimization design of spectral discriminator for high-spectral-resolution lidar based on error analysis.

Accurate aerosol optical properties could be obtained via the high spectral resolution lidar (HSRL) technique, which employs a narrow spectral filter to suppress the Rayleigh or Mie scattering in lidar return signals. The ability of the filter to suppress Rayleigh or Mie scattering is critical for HSRL. Meanwhile, it is impossible to increase the rejection of the filter without limitation. How to optimize the spectral discriminator and select the appropriate suppression rate of the signal is important to us. The HSRL technology was thoroughly studied based on error propagation. Error analyses and sensitivity studies were carried out on the transmittance characteristics of the spectral discriminator. Moreover, ratwo different spectroscopic methods for HSRL were described and compared: one is to suppress the Mie scattering; the other is to suppress the Rayleigh scattering. The corresponding HSRLs were simulated and analyzed. The results show that excessive suppression of Rayleigh scattering or Mie scattering in a high-spectral channel is not necessary if the transmittance of the spectral filter for molecular and aerosol scattering signals can be well characterized. When the ratio of transmittance of the spectral filter for aerosol scattering and molecular scattering is less than 0.1 or greater than 10, the detection error does not change much with its value. This conclusion implies that we have more choices for the high-spectral discriminator in HSRL. Moreover, the detection errors of HSRL regarding the two spectroscopic methods vary greatly with the atmospheric backscattering ratio. To reduce the detection error, it is necessary to choose a reasonable spectroscopic method. The detection method of suppressing the Rayleigh signal and extracting the Mie signal can achieve less error in a clear atmosphere, while the method of suppressing the Mie signal and extracting the Rayleigh signal can achieve less error in a polluted atmosphere.

Correction technology of a polarization lidar with a complex optical system.

A complex optical system used in polarization lidars often modifies the input polarization of the return signal so that it may significantly impact depolarization estimates and introduce errors to polarization lidar measurements. In most cases, retardation, depolarization, and misalignment of the system exist at the same time and interact with each other. Polarization effects of the system cannot be represented by a simple correction coefficient, so they cannot be removed using a traditional calibration method. Detailed analysis and correction technologies were provided to remove systematic biases in estimating depolarization values from a polarization lidar owing to multiple optical components. The Mueller matrices from an emitter to a receiver were calculated, and the expression for an aerosol depolarization parameter including system polarization effects was derived and obtained. In addition, the correction algorithm based on the Mueller matrix was introduced and provided. A polarization lidar was established, and the polarization characteristics of its optical components were measured with a laboratory ellipsometer; then, the Mueller matrix of the receiver was calculated and obtained. Lidar observations were performed, and our correction algorithm was applied to lidar field data. The results show that the correction method can significantly remove systematic polarization effects.