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Published on: August 29, 2019
Information-theoretic method for the inversion of the lidar equation.
Applied Optics
|June 16, 2010
Summary
A novel method reconstructs aerosol extinction coefficients from lidar data. This minimum cross-entropy approach effectively uses prior and observed information for accurate atmospheric profiling.
Area of Science:
- Atmospheric Science
- Optical Remote Sensing
- Information Theory
Background:
- Accurate retrieval of aerosol optical properties is crucial for climate modeling and atmospheric studies.
- Monostatic single-wavelength lidar systems provide valuable data but are susceptible to noise, complicating data inversion.
- Existing methods for reconstructing aerosol extinction coefficients often struggle with noisy data and limited information incorporation.
Purpose of the Study:
- To develop a robust method for reconstructing aerosol volume extinction coefficient profiles from noisy lidar data.
- To implement an information-theoretic approach, specifically minimum cross-entropy (MCE), for solving the lidar inversion problem.
- To demonstrate the efficacy of the MCE method using synthetic lidar data.
Main Methods:
- Utilized the principle of minimum cross-entropy (MCE) for inverse problem solving.
- Incorporated prior knowledge (initial extinction estimate) and observed lidar data into the MCE framework.
- Developed a numerical procedure based on the ellipsoid algorithm for MCE reconstruction.
Main Results:
- Successfully reconstructed aerosol volume extinction coefficient profiles from noisy synthetic lidar data.
- The MCE method demonstrated an objective and rational approach to lidar data inversion.
- Numerical examples validated the utility and efficacy of the developed inversion technique.
Conclusions:
- The minimum cross-entropy method offers a powerful solution for retrieving aerosol extinction coefficients from lidar measurements.
- The ellipsoid algorithm provides an efficient and robust computational tool for this atmospheric remote sensing application.
- This approach enhances the reliability of aerosol profiling, contributing to improved atmospheric research and monitoring.
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