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.