Meteorological factors have a significant effect on the vertical distribution of aerosols, and the formation, accumulation, and dissipation of heavy pollution processes in winter are usually controlled by meteorological conditions. There is a high correlation between meteorological factors and the vertical structure of aerosols. Obtaining detailed evolution characteristics of meteorological factors is of great research value for studying the process of haze generation and dissipation. Among various meteorological factors, the vertical distribution of temperature plays a crucial role in the aggregation of aerosols in the boundary layer. Currently, ground meteorological stations, meteorological satellites, radiosondes, and microwave radiometers are the main means of detecting vertical temperature profiles, but none of them can achieve high spatiotemporal resolution detection of temperature profiles within the boundary layer. Rotational Raman lidar, as an effective technique for atmospheric temperature measurement, offers high temporal and spatial resolution, which is advantageous for studying atmospheric physical processes within the boundary layer during haze conditions. However, the system’s detection performance within the boundary layer is significantly affected by the inconsistency of the bottom detection blind zone and signal attenuation caused by aerosols within the boundary layer, as well as interference from elastic scattering. To enable atmospheric temperature measurements in the bottom layer of haze conditions, we propose a temperature correction technique based on the backscatter ratio. We hope to effectively obtain the vertical structure of atmospheric temperature within the boundary layer of haze weather through this technique, thereby providing data support for the refined study of atmospheric physical processes under haze conditions.

The core of this technique involves constructing a linear functional relationship between the backscatter ratio and the elastic scattering crosstalk ratio and using the backscatter ratio to correct the rotational Raman ratio. The consistency of the geometric overlap factor of the rotational Raman channels significantly affects the bottom detection performance of the lidar. Therefore, accurately obtaining the ratio of high and low quantum number rotational Raman channels is a prerequisite for implementing rotational Raman temperature measurements. First, we use experimental data under clear sky conditions without haze to calibrate the geometric overlap factor ratio of the rotational Raman channel signals. Then, we calibrate the inversion function using a radiosonde under simultaneous spatial conditions and obtain the theoretical Raman ratio within the haze layer based on the temperature data from the radiosonde. Based on this theoretical ratio and the rotational Raman ratio that includes elastic scattering crosstalk, we calculate the elastic scattering crosstalk ratio. We perform a linear regression analysis on both the backscatter ratio and the elastic scattering crosstalk ratio to derive the corresponding system calibration constant. Finally, using this calibration constant and the measured backscatter ratio, we complete the correction of the rotational Raman ratio, allowing for the retrieval of the true atmospheric temperature data in the elastic scattering region.

Numerical simulation results indicate that inconsistencies in the geometric overlap factor and the elastic scattering crosstalk can lead to retrieval biases in atmospheric temperature measurements within the boundary layer during haze events. After applying dual corrections for the geometric overlap factor and the rotational Raman ratio, the temperature retrieval bias is reduced to less than 0.2 K (Fig. 4). Experimental results show that the corrected temperature profile from the morning of of December 26, 2023 exhibits a high degree of consistency with the radiosonde data obtained under simultaneous spatial conditions, with a maximum temperature bias of less than 1.4 K, while the uncorrected temperature bias is up to 7 K (Fig. 8). On the evening of of December 24, 2023, the maximum temperature bias is less than 0.6 K, while the uncorrected temperature bias is approximately 4 K (Fig. 9). Additionally, continuous observation results clearly illustrate the vertical distribution of the temperature field. A comparison of the backscatter ratios of aerosols reveals a close correlation between the inversion temperature features and the vertical distribution of aerosols (Fig. 10).

In our study, we propose a temperature correction technique based on the backscatter ratio to achieve atmospheric temperature measurements within the bottom layer of haze conditions. Simulation and experimental results indicate that this technique can effectively achieve precise measurements of atmospheric temperature within the boundary layer during haze events, clearly illustrating the vertical structure of atmospheric temperature and the characteristics of inversion. Since the fundamental basis for temperature correction relies on the correlation between the elastic scattering crosstalk ratio and the backscatter ratio, it is crucial to accurately obtain the backscatter ratio and the rotational Raman ratio within the haze layer. Therefore, highly consistent preprocessing of the lidar echo signals is necessary. Additionally, the stability of parameters such as laser energy, the geometric overlap relationship of the light transmission and reception system, the elastic scattering suppression ratio of the rotational Raman channel, and the photoelectric conversion efficiency also influence the implementation of this technique. Variations in these parameters can directly affect system stability, thereby affecting the temperature correction results. Therefore, a lidar system with high stability is an essential prerequisite for conducting atmospheric temperature corrections. Any minor adjustments to system parameters necessitate a reevaluation of the system calibration. In summary, the introduction of this correction technique provides scientific data and technical means for studying atmospheric physical processes within the boundary layer during haze events, facilitating a detailed investigation and analysis of the formation and evolution characteristics of haze.