Analysis of temperature detection performance of vibration-rotation Raman lidar based on different calibration functions

Rotational Raman (RR) lidar is widely employed for atmospheric temperature detection, but its detection capability is constrained in cloudy or hazy conditions due to the interference of elastic scattering leakage to the pure RR channels. To address this limitation, a vibrational-rotational Raman (VR) temperature measurement technique has been proposed and validated. This study compares the performance of VR and RR techniques through simulations and observational experiments. The simulations account for both the theoretical (intrinsic) errors of the methods and the impact of random errors. Additionally, this study evaluates the performance of ten calibration functions (CFs) in the VR method, including nine applied to VR for the first time. The results indicate that when the signal-to-noise ratio of the low quantum number channel is low, the VR technique can effectively improve the detection performance of the system. For the VR technique, all tested CFs can successfully retrieve atmospheric temperature. Among them, the four-coefficient forward CFs achieve the best calibration performance within the calibration interval, while the linear CF performs best in extrapolation scenarios.