Atmospheric temperature measurements at altitudes of 5-30 km with a double-grating-based pure rotational Raman lidar

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.

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.

Optical properties of aerosol and cloud particles measured by a single-line-extracted pure rotational Raman lidar

Conventional lidar methods for deriving particle optical properties suffer from the fact that two unknowns (backscatter and extinction coefficients) need to be determined from only one lidar equation. Thus, additional assumptions (constant lidar ratio or Ångström relationship) have to be introduced to settle this problem. In contrast, a single-line-extracted pure-rotational-Raman (PRR) lidar method allows the strict retrieval of backscatter and extinction coefficients without additional assumptions. Based on the observations of our single-line-extracted PRR lidar from February 2016 to December 2017, the optical properties (backscatter coefficient, extinction coefficient and lidar ratio) of continental polluted aerosols, dust aerosols, and cirrus cloud particles over Wuhan (30.5°N, 114.4°E) are well characterized. The mean values of the measured lidar ratios are respectively 60 ± 7 sr for continental polluted aerosols, 47 ± 4 sr for dust aerosols and 22 ± 4 sr for cirrus cloud particles. The backscatter and extinction coefficients measured by the single-line-extracted PRR lidar deviate as a whole by 7-13% and 13-16%, respectively, from those retrieved by the traditional Fernald method. The optical properties measured by the single-line-extracted PRR lidar can serve as observational standards for particle optical properties (backscatter/extinction coefficient and lidar ratio) at 532 nm wavelength.

Pure rotational Raman lidar for full-day troposphere temperature measurement at Zhongshan Station (69.37°S, 76.37°E), Antarctica

A pure rotational Raman lidar (PRRL) for full-day troposphere temperature measurement was deployed in February 2020 at Zhongshan Station (69.37°S, 76.37°E), Antarctica, by the 36^th Chinese National Antarctic Research Expedition. The PRRL emits a 532.23-nm laser light and employs a 203.2-mm telescope to collect atmospheric backscatter. Cubic nonpolarizing beam splitters are introduced to yield a compact optics arrangement. A quasi-single-line-extraction technique is proposed for extracting the molecular Stokes line signals. A lidar container with a window system is customized to house the whole PRRL system for long-term stable operation. An approach using a laser plummet is developed for fast and convenient adjustment of the telescope zenithward. A home-made calibration module is utilized for straightforward visual optics adjustment with ∼35.3-μrad angular positioning accuracy. Both typical daytime and nighttime temperature measurement examples are presented to verify the lidar performance. From a 30-h continuous temperature measurement result, it is found the tropopause is located at ∼10.8 km above ground level with a mean temperature of ∼203 K; significant temperature variability occurs only at the inversion areas, while off which the 1-h temperature profiles are relatively similar in form with an average lapse rate of -8.3 K/km.

Diurnal temperature variations in the lower troposphere as measured by an all-day-operational pure rotational Raman lidar

We describe a pure rotational Raman lidar for measuring the all-day temperature profiles in the lower troposphere. The lidar is made up of a frequency-tripled Nd:YAG laser at 354.82 nm with ∼250mJ pulse energy at the 30 Hz repetition rate, a 200 mm receiving telescope, and narrow-band interference-filter-based detection optics. The lidar performance is shown by measured examples. Under clear sky conditions, with an integration time of 60 min and a vertical resolution of 90 m, the 1-σ statistical uncertainty does not exceed 1 K up to the altitude of ∼4.1km during nighttime, while the corresponding altitude is ∼2.3km at noon. The diurnal temperature variation characteristics have been revealed by the lidar measurements with the 1-σ statistical uncertainty <1K between altitudes ranging from 0.6 to ∼2.0km at Wuhan, China (30.53°N, 114.37°E). The atmospheric temperature shows a strong diurnal oscillation and moderate semidiurnal oscillation at altitudes 0-1.4 km for two days in July 2019 (July period), 0-1.4 km for four days in September 2019 (September period), and 0-0.8 km for three days in January 2018 (January period), respectively. The mean diurnal and semidiurnal amplitudes of the nine days are respectively ∼1.4K and ∼0.5K at the 0.6 km altitude, while the corresponding surface values are ∼4.2K and ∼1.4K, respectively. The diurnal amplitudes tend to weaken with increasing altitude. At altitudes >0.6km, the diurnal amplitude in the September period is less than that in the July period, but greater than that in the January period. The phase delays of the diurnal oscillations are ∼3h in the July period, 5-6 h in the September period, and 5-7 h in the January period compared to those at the surface, respectively. Both the diurnal amplitudes and phase delays indicate a possible seasonal dependence.

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.

Single-line-extracted pure rotational Raman lidar to measure atmospheric temperature and aerosol profiles

We have built a pure rotational Raman (PRR) lidar system that effectively detects two isolated N₂ molecule PRR line signals and elastic backscatter signals. This system enables all-day temperature profiles to be accurately obtained without calibration, according to the simple two-parameter functional relationship between the temperature and ratio of the two PRR line signals. Based on the derived temperature profiles, the aerosol backscatter and extinction profiles can be further determined strictly from one measured PRR line signal and elastic backscatter signal without additional assumptions. The two aerosol parameters and resultant lidar ratio provide strict standards for the lidar measurements of aerosol.

Fine-filter method for Raman lidar based on wavelength division multiplexing and fiber Bragg grating

Atmospheric temperature is one of the important parameters for the description of the atmospheric state. Most of the detection approaches to atmospheric temperature monitoring are based on rotational Raman scattering for better understanding atmospheric dynamics, thermodynamics, atmospheric transmission, and radiation. In this paper, we present a fine-filter method based on wavelength division multiplexing, incorporating a fiber Bragg grating in the visible spectrum for the rotational Raman scattering spectrum. To achieve high-precision remote sensing, the strong background noise is filtered out by using the secondary cascaded light paths. Detection intensity and the signal-to-noise ratio are improved by increasing the utilization rate of return signal form atmosphere. Passive temperature compensation is employed to reduce the temperature sensitivity of fiber Bragg grating. In addition, the proposed method provides a feasible solution for the filter system with the merits of miniaturization, high anti-interference, and high stability in the space-based platform.

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.

Analytical calibration functions for the pure rotational Raman lidar technique

We present a calibration function in the general analytical form for the tropospheric temperature retrievals using pure rotational Raman (PRR) lidars. The function is derived within the framework of the semiclassical theory and takes into account the collisional broadening of all PRR lines. We analyze via simulation its four simplest nonlinear (three-coefficient) special cases to determine the function that yields the least error, and therefore, is the best-suited for the temperature retrievals. Two of them are proposed for the first time. The comparative analysis of temperature errors showed that all the special cases yield errors less than 0.1 K in modulus, and therefore, can be applied for the tropospheric temperature retrievals. The best function yields the maximum error less than 0.002 K in modulus and five times smaller compared to the commonly used nonlinear calibration function.

Observation and analysis of the temperature inversion layer by Raman lidar up to the lower stratosphere

The vibration-rotational Raman lidar system built in Xi'an, China (34.233°N, 108.911°E) was used to simultaneously detect atmospheric temperature, water vapor, and aerosols under different weather conditions. Temperature measurement examples showed good agreement with radiosonde data in terms of the lapse rates and heights of the inversion layer under the lower stratosphere. The statistical temperature error due to the signal-to-noise ratio is less than 1 K up to a height of 15 km, and is estimated to be less than 3 K below a height of 22 km. High-quality temperature data were collected from 70 nighttime observations from October 2013 to May 2014, and were used to analyze the temperature inversion characteristics at Xi'an, which is a typical city in the northwest of China. The tropopause height over the Xi'an area was almost 17-18 km, and the inversion layer often formed above the cloud layer. In the winter at night, inversions within the boundary layer can easily form with a high occurrence of ∼60% based on 47 nights from 01 November 2013 to 21 January 2014. Continuous observation of atmospheric temperature, water vapor (relative humidity), and aerosols was carried out during one night, and the relevant changes were analyzed in the boundary layer via the joint observation of atmospheric visibility, PM2.5 and PM10 from a ground visibility meter and from a monitoring site, which revealed that the temperature inversion layer has a great influence on the formation of fog and haze during the winter night and early morning.