McPrA - A new gas profile inversion algorithm for MAX-DOAS and apply to 50 m vertical resolution

Area of Science:

• Atmospheric chemistry and physics
• Environmental science and monitoring
• Remote sensing and data retrieval

Background:

• Air pollutants pose significant environmental and health risks, necessitating accurate assessment methods.
• The MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) method provides vertical gas profile analysis but is limited by its reliance on a priori profiles.
• Existing MAX-DOAS retrieval algorithms require improvement to enhance the accuracy of trace gas profile assessments.

Purpose of the Study:

• To introduce a novel MAX-DOAS trace gas profile inversion algorithm, McPrA, designed to be less dependent on a priori profile information.
• To improve the accuracy and vertical resolution of trace gas profile retrieval using MAX-DOAS measurements.
• To validate the performance of the McPrA algorithm against established methods and in situ data.

Main Methods:

• Developed the McPrA (Monte Carlo Profile Analysis) algorithm, employing the Monte Carlo method for optimal estimation of trace gases.
• Calculated gas vertical column density using air mass factors derived from SCIATRAN radiative transfer modeling.
• Retrieved trace gas vertical distribution by combining weight functions with a priori profiles, incorporating a normalization process for improved matching and enabling flexible grid modification for high vertical resolution (up to 50 m).

Main Results:

• The McPrA algorithm demonstrated high accuracy in retrieving gas profiles, achieving a correlation coefficient exceeding 0.89 for NO2 in the first layer when compared to in situ data.
• Sensitivity experiments determined optimal retrieval parameters, yielding a degree of freedom greater than 3.0.
• Comparative verification experiments against WRF-Chem and synthetic data confirmed the McPrA algorithm's effectiveness in accurate gas profile retrieval.

Conclusions:

• The McPrA algorithm offers a significant advancement in MAX-DOAS trace gas profile analysis, reducing reliance on a priori data and improving accuracy.
• The algorithm's flexibility in grid modification and high vertical resolution capabilities make it a valuable tool for air quality monitoring.
• McPrA provides a reliable and effective method for assessing air pollutant distributions, contributing to a better understanding of environmental and health risks.