Error analysis for the lidar backward inversion algorithm.

Area of Science:

• Atmospheric Science
• Remote Sensing
• Lidar Technology

Background:

• The Klett method is a common backward inversion algorithm for solving the lidar equation.
• User-input parameters like boundary extinction and the extinction-to-backscatter relationship introduce uncertainties.
• These uncertainties propagate and affect the accuracy of the inverted extinction profile.

Purpose of the Study:

• To quantify the error sensitivity of the Klett lidar inversion method.
• To establish a relationship between input parameter uncertainties and output extinction profile errors.
• To provide range-dependent error bounds for lidar data analysis.

Main Methods:

• Developed an error sensitivity study for the Klett inversion algorithm.
• Performed a mathematical derivation of error spans for the inverted extinction profile.
• Tested numerical performance for optical depths ranging from 0.01 to 10.
• Utilized synthesized and live elastic-backscatter lidar signals for validation.

Main Results:

• Quantified how errors in boundary extinction and the exponential term impact the inverted extinction profile.
• Derived range-dependent upper and lower error bounds for the extinction profile.
• Demonstrated the application of the error analysis to various atmospheric scenarios.

Conclusions:

• The study provides a framework for understanding and quantifying uncertainties in lidar-derived extinction profiles.
• The derived error bounds are crucial for accurate interpretation of atmospheric data obtained via lidar.
• This research enhances the reliability of lidar remote sensing for atmospheric studies.