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Published on: April 30, 2018
Correction method for cloud temperature retrieval combining rotational Raman, vibrational Raman, and elastic
Applied Optics
|June 10, 2026
Summary
This study introduces a novel Raman lidar method to accurately measure atmospheric temperature within clouds. By correcting for signal interference, it significantly improves temperature retrieval accuracy, even in challenging thick-cloud conditions.
Area of Science:
- Atmospheric Science
- Remote Sensing
- Lidar Technology
Background:
- Raman lidar is crucial for atmospheric temperature detection.
- Thick clouds compromise in-cloud temperature retrieval accuracy due to signal leakage.
Purpose of the Study:
- To develop and validate a four-channel joint-retrieval method for correcting in-cloud temperature measurements.
- To enhance the accuracy of Raman lidar temperature retrievals under cloudy conditions.
Main Methods:
- Utilized a four-channel approach combining rotational Raman (RR), vibrational Raman (VR), and elastic scattering signals.
- Employed the leakage-immune VR signal to calculate crosstalk coefficients for RR channels.
- Validated the method using simulations and observational data, comparing it with radiosonde and existing techniques.
Main Results:
- Reduced mean absolute error (MAE) to within 1 K compared to radiosonde data in cloudy regions.
- Lowered root mean square error (RMSE) by over 86.34% compared to uncorrected retrievals.
- Achieved a 37.78% RMSE reduction compared to existing methods and provided continuous spatiotemporal temperature data.
Conclusions:
- The proposed four-channel joint-retrieval method effectively corrects for signal biases in cloudy conditions.
- This technique significantly enhances the accuracy and continuity of atmospheric temperature measurements using Raman lidar.
- Calibrated crosstalk coefficients offer reliable corrections for RR signals across different observation periods.
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