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

Area of Science:

• Atmospheric Science
• Remote Sensing
• Spectroscopy

Background:

• Rotational Raman (RR) lidar is a key tool for atmospheric temperature profiling.
• Its effectiveness is limited by elastic scattering interference in cloudy or hazy conditions.
• A vibrational-rotational Raman (VR) technique offers a potential solution.

Purpose of the Study:

• To compare the performance of VR and RR lidar techniques for atmospheric temperature measurement.
• To evaluate the effectiveness of various calibration functions (CFs) within the VR method.
• To assess the impact of theoretical and random errors on both techniques.

Main Methods:

• Simulations were conducted to analyze intrinsic and random errors for VR and RR lidar.
• Observational experiments were performed to validate simulation results.
• Ten calibration functions (CFs), including nine novel ones for VR, were evaluated.

Main Results:

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

Conclusions:

• The VR lidar technique provides a robust alternative to RR lidar for atmospheric temperature measurements, especially under challenging atmospheric conditions.
• The selection of appropriate calibration functions is crucial for optimizing VR lidar performance.
• VR lidar demonstrates improved accuracy and reliability in atmospheric temperature profiling.