Nonlinear-approximation technique for determining vertical ozone-concentration profiles with a...

Area of Science:

• Atmospheric Science
• Remote Sensing
• Spectroscopy

Background:

• Accurate ozone-concentration profiles are crucial for atmospheric research and climate modeling.
• Traditional lidar techniques can be susceptible to noise and aerosol-induced errors.
• Improving the precision of ozone measurements is essential for understanding atmospheric dynamics.

Purpose of the Study:

• To develop and validate a new technique for retrieving ozone-concentration profiles using multiwavelength differential-absorption lidar (DIAL).
• To reduce erroneous fluctuations in ozone profiles caused by signal noise and aerosol inhomogeneity.
• To establish uncertainty boundaries for derived ozone profiles.

Main Methods:

• Utilized a multiwavelength differential-absorption lidar (DIAL) system.
• Developed a novel signal processing technique involving transformation of the off- to on-line signal ratio into an intermediate function.
• Applied analytical approximations, including low-order polynomial fits for low-frequency constituents and trigonometric fits for high-frequency constituents, to separate profile components.
• Estimated dominant measurement errors and established uncertainty boundaries before profile derivation.

Main Results:

• The new DIAL technique effectively reduces erroneous local fluctuations in ozone-concentration profiles.
• Significant reduction in noise and aerosol inhomogeneity-induced errors was observed compared to conventional numerical differentiation methods.
• The trigonometric fit component of the technique successfully corrected profiles in areas with large ozone-concentration gradients.
• Experimental data from the lower troposphere demonstrated the technique's efficacy.

Conclusions:

• The presented DIAL technique offers a significant improvement in the accuracy of ozone-concentration profile retrieval.
• The method provides more reliable atmospheric ozone data by mitigating common measurement errors.
• This advancement has implications for atmospheric monitoring, climate research, and air quality studies.