Carbon monoxide mixing ratio inference from gas filter radiometer data

Area of Science:

• Atmospheric Science
• Remote Sensing
• Environmental Monitoring

Background:

• Accurate measurement of tropospheric carbon monoxide is crucial for understanding atmospheric chemistry and climate.
• Existing data reduction methods can be computationally intensive, limiting real-time applications.

Purpose of the Study:

• To develop and validate a new algorithm for real-time data reduction of carbon monoxide concentrations.
• To simplify the complex calculations involved in gas-filter correlation radiometer data analysis.

Main Methods:

• Developed a novel algorithm based on regression analysis and truncated power series representation.
• Implemented the algorithm on a microcomputer for data processing.
• Compared results with traditional line-by-line calculation methods.

Main Results:

• The new algorithm allows for real-time data reduction of nadir measurements.
• Achieved accuracy comparable to rigorous line-by-line calculations.
• Successfully applied the algorithm to MAPS Shuttle and aircraft data.

Conclusions:

• The developed algorithm offers an efficient and accurate method for determining tropospheric carbon monoxide concentrations.
• This advancement facilitates real-time atmospheric monitoring and analysis.
• The technique is validated for use with satellite and aircraft-based measurements.