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Area of Science:

Atmospheric Science
Remote Sensing
Optical Physics

Background:

The Fernald method is the standard for lidar data retrieval but requires a boundary value.
Determining this boundary value typically uses clear atmosphere above the tropopause or the slope method.
The slope method introduces uncertainty due to assumptions of homogeneous aerosol distribution and single-component atmospheric signals.

Purpose of the Study:

To develop a more accurate method for determining lidar boundary values.
To reduce uncertainties associated with the slope method for lidar data retrieval.
To improve the accuracy of lidar data retrieval, particularly when the detection range is below the tropopause.

Main Methods:

A new approach segments lidar signals into uniform sub-signals, avoiding homogeneity assumptions.
Nonlinear two-component fitting is employed to overcome limitations of single-component assumptions.
The method was validated using both simulated and real lidar signal data.

Main Results:

The proposed method yields more accurate boundary values compared to the slope method.
Improved accuracy was observed in lidar data retrieval using the new approach.
The automatic segmentation and two-component fitting effectively determined reference bins and boundary values.

Conclusions:

The novel approach enhances the accuracy of lidar data retrieval below the tropopause.
Automatic segmentation and nonlinear two-component fitting significantly reduce uncertainties in boundary value determination.
This method offers a more robust solution for lidar data processing in challenging atmospheric conditions.