[Two Data Inversion Algorithms of Aerosol Horizontal Distributiol Detected by MPL and Error Analysis]

Area of Science:

• Atmospheric Science
• Environmental Monitoring
• Climate Science

Background:

• Atmospheric aerosols significantly impact human health, environment, and climate.
• Micro Pulse Lidar (MPL) is a valuable tool for monitoring aerosol distribution.
• Accurate inversion and error analysis of extinction coefficients are crucial for MPL data processing.

Purpose of the Study:

• To compare the effectiveness of slope and Fernald algorithms for inverting horizontal atmospheric aerosol extinction coefficients using MPL data.
• To analyze the errors associated with each inversion algorithm.
• To determine the most suitable algorithm for near-ground horizontal aerosol distribution detection.

Main Methods:

• Inversion of horizontal MPL data using both slope and Fernald algorithms.
• Comparative analysis of the inverted results.
• Error analysis of the slope and Fernald algorithms, identifying sources of error.
• Correlation analysis with forward-scattering visibility sensor data.

Main Results:

• Both slope and Fernald algorithms accurately present spatial and temporal aerosol distribution.
• Errors in the slope algorithm stem from its theoretical model, while Fernald algorithm errors arise from assumptions.
• Both algorithms showed a high correlation (95%) with forward-scattering visibility sensor data.
• The Fernald algorithm demonstrated greater suitability for horizontal extinction coefficient inversion.

Conclusions:

• Despite existing challenges, both algorithms provide accurate aerosol distribution data.
• The Fernald algorithm is recommended for horizontal extinction coefficient inversion due to its higher suitability.
• MPL technology, with appropriate algorithms, is effective for near-ground aerosol monitoring.