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Iterative inversion method for retrieving the optical properties of aerosol under cloud by ground-based scanning
Optics Express
|August 13, 2025
Summary
This study introduces a new iterative proximal calibration algorithm for micro infrared lidar (mIRLidar) to accurately measure under-cloud aerosols. The method improves extinction coefficient accuracy, overcoming limitations of traditional calibration techniques.
Area of Science:
- Atmospheric Science
- Optical Remote Sensing
- Aerosol Science
Background:
- Dense cloud cover hinders lidar calibration, challenging under-cloud aerosol inversion.
- Traditional Fernald height calibration methods are limited in low-visibility conditions.
Purpose of the Study:
- To develop an iterative proximal calibration algorithm for micro infrared lidar (mIRLidar).
- To improve the accuracy of aerosol extinction coefficient inversion under low and dense cloud cover.
Main Methods:
- Utilized an iterative proximal calibration algorithm combining slope method and Fernald's backward integral equation.
- Leveraged the observational mode characteristics of the mIRLidar.
- Conditioned the iterative process to minimize relative error.
Main Results:
- The iterative algorithm progressively reduced the relative error between calculated and true extinction coefficients.
- Demonstrated improved accuracy in under-cloud aerosol inversion compared to traditional methods.
Conclusions:
- The proposed iterative proximal calibration algorithm effectively overcomes limitations of traditional Fernald calibration.
- This method enhances the accuracy of aerosol inversion in challenging atmospheric conditions with dense clouds.
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