Simultaneous estimation of aerosol cloud concentration and spectral backscatter from multiple-wavelength lidar data

Area of Science:

• Atmospheric Science
• Optical Physics
• Signal Processing

Background:

• Accurate characterization of atmospheric aerosols is crucial for understanding climate and air quality.
• Lidar (Light Detection and Ranging) systems provide valuable data for aerosol studies.
• Estimating both concentration and spectral backscatter dependence simultaneously presents a significant challenge.

Purpose of the Study:

• To develop a sequential algorithm for estimating the concentration and backscatter coefficient spectral dependence of atmospheric aerosols.
• To model the range and time dependence of aerosol concentration.
• To analyze the wavelength and time dependence of the aerosol backscatter coefficient.

Main Methods:

• A sequential algorithm integrating a Kalman filter and a maximum-likelihood estimator.
• Modeling aerosol concentration dependence using an orthonormal basis set expansion.
• Utilizing data from a rapidly tuned lidar system, including pre-release ambient lidar return.
• Simultaneous estimation of state model parameters, concentration, and backscatter coefficients.

Main Results:

• The algorithm successfully estimates the range and time dependence of aerosol concentration.
• It also accurately determines the spectral dependence of the aerosol backscatter coefficient over time.
• The integrated approach allows for continuous information exchange between estimators.
• Demonstrated effectiveness on atmospheric backscatter long-wave infrared (CO2) lidar data.

Conclusions:

• The presented sequential algorithm offers a robust method for analyzing optically thin atmospheric aerosols.
• It enables simultaneous estimation of key aerosol properties, improving data processing efficiency.
• This approach enhances the understanding of aerosol dynamics and optical characteristics using lidar technology.