Among the myriad factors influencing climate change, the interaction between clouds and aerosols is the most uncertain element in global climate dynamics, and is widely acknowledged as a formidable challenge in atmospheric science. High-spectral-resolution lidar (HSRL) observations of vertical distribution characteristics of clouds and aerosols, independent of assumptions about cloud vertical structure and lidar ratio, hold immense scientific potential for future research on cloud-aerosol interactions. In HSRL systems, active frequency locking technology is typically employed to match the emitted laser wavelength with the etalon, thus ensuring system parameter stability. However, changes in the working environment or hardware failures can reduce the locking precision to significantly degrade the high-spectral-resolution detection performance. Therefore, real-time calibration of molecular transmittance, correction of detection results, and enhancement of detection accuracy are of paramount significance.

In the HSRL system, the molecular transmittance is determined by the collaboration of multiple components such as the emitted laser and the etalon. Ground-based lidar observations are susceptible to environmental changes, which require timely calibration for accurate inversion. The atmosphere is replete with a multitude of components such as clouds and aerosols, necessitating stratified identification. Distinguishing between clouds, aerosols, and clean areas in the atmosphere lays the foundation for calibrating molecular transmittance. We introduce an online method for calibrating molecular transmittance, which avoids interference from clouds and aerosols by stratified identification to achieve online calibration of molecular transmittance. Since the proposed HSRL can directly invert atmospheric optical property parameters without assuming a lidar ratio, the utilization of a scattering ratio threshold method for classification provides unique advantages.

The experiment selects calibration cases in three distinct atmospheric conditions in Beijing, including clear, dusty, and cloudy conditions. Under these different atmospheric states, molecular transmittance calibration can be performed by following atmospheric stratified identification, which demonstrates that this method can calibrate molecular transmittance in various weather conditions. To verify the accuracy of the HSRL detection results, we compare the HSRL with the widely employed sun photometer. The observation results in Beijing are inverted by adopting both fixed molecular transmittance and online calibration parameters. When fixed parameters are leveraged for inversion, the correlation coefficient of the detection results of the two instruments is 0.92, and the root mean square error is 0.136. After conducting correction with this method, some inversion errors are effectively corrected, the correlation coefficient reaches 0.94, and the root mean square error decreases to 0.078. The detection results obtained from the inversion show higher consistency with that of the sun photometer.

We initially analyze the molecular transmittance error based on the fundamental principles and detection methods of HSRL. In the HSRL system, where an iodine molecule absorption cell serves as a spectral etalon, the systematic error in molecular transmittance primarily results from the frequency fluctuation of the emitted laser and temperature instability in the molecular absorption cell. Meanwhile, an online calibration method for molecular transmittance is proposed to rectify the influence caused by system instability. Unlike the calibration method that fixes clean atmospheric areas, this method exploits the HSRL characteristics that can simply and accurately invert the backscatter ratio, and employs the backscatter ratio as the basis for stratified identification. Additionally, after selecting clean areas in the atmosphere, the molecular transmittance is calibrated by adopting these clean areas. This leads to the result that transmittance calibration cannot be limited to clear weather, thus supplementing the calibration method in non-clear weather conditions. Finally, based on the observation results of the HSRL system in Beijing, an analysis of its observation results is conducted to demonstrate the effectiveness of this method in enhancing detection accuracy.