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

Area of Science:

• Atmospheric science
• Remote sensing technology
• Spectroscopy

Background:

• Rotational Raman (RR) lidar is a key tool for atmospheric temperature profiling.
• Its effectiveness is limited by cloud and haze, which cause elastic scattering interference.
• This interference impacts the accuracy of pure RR channels.

Purpose of the Study:

• To compare the performance of vibrational-rotational Raman (VR) and RR lidar techniques.
• To evaluate the effectiveness of various calibration functions (CFs) within the VR method.
• To assess the robustness of VR lidar under challenging atmospheric conditions.

Main Methods:

• Simulations incorporating theoretical and random errors were performed for both VR and RR techniques.
• Observational experiments were conducted to validate simulation results.
• Ten calibration functions, including nine novel applications to VR, were evaluated.

Main Results:

• The VR technique significantly enhances detection performance, particularly when the signal-to-noise ratio in low quantum number channels is low.
• All tested calibration functions successfully retrieved atmospheric temperature using the VR method.
• Four-coefficient forward CFs excelled in calibration, while linear CFs were superior for extrapolation.

Conclusions:

• VR lidar overcomes the limitations of RR lidar in cloudy and hazy conditions.
• The VR technique offers improved atmospheric temperature detection capabilities.
• Effective calibration strategies exist for VR lidar, with specific functions suited for different application scenarios.