Regularizing active set method for retrieval of the atmospheric aerosol particle size distribution function

Area of Science:

• Atmospheric Science
• Remote Sensing
• Computational Physics

Background:

• Determining aerosol particle size distribution involves solving ill-posed integral equations.
• Existing regularization methods like Phillips-Twomey can be unstable with limited or noisy observational data.
• Nonnegativity of particle size distribution is a crucial physical constraint often overlooked.

Purpose of the Study:

• To develop a novel regularizing active set algorithm for aerosol particle size distribution retrieval.
• To address the ill-posed nature of the problem and enforce nonnegativity constraints.
• To evaluate the algorithm's efficiency and feasibility using synthetic and real-world field data.

Main Methods:

• Investigated the active set method for inverse problems.
• Developed a regularizing active set algorithm incorporating nonnegativity constraints.
• Validated the algorithm with synthetic data and field measurements from a CE 318 Sun photometer.

Main Results:

• The proposed regularizing active set algorithm demonstrates efficiency and feasibility.
• Successfully retrieved aerosol particle size distribution functions.
• Showcased improved stability and nonnegativity enforcement compared to traditional methods.

Conclusions:

• The regularizing active set algorithm is a robust method for aerosol particle size distribution retrieval.
• This approach effectively handles ill-posed problems with incomplete and contaminated data.
• The study provides a valuable tool for atmospheric remote sensing applications.