Simple Monte Carlo methods to estimate the spectra evaluation error in differential-optical-absorption spectroscopy

Area of Science:

• Atmospheric science
• Spectroscopy
• Environmental monitoring

Background:

• Differential-optical-absorption spectroscopy (DOAS) is crucial for measuring atmospheric trace gases.
• Accurate calculation of measurement precision in DOAS remains a challenge.
• Spectral artifacts interfere with accurate trace gas concentration analysis.

Purpose of the Study:

• To develop accurate methods for evaluating DOAS spectral errors.
• To address the misestimation of fitting errors caused by spectral artifacts.
• To improve the statistical soundness of DOAS measurement precision.

Main Methods:

• Introduced residual inspection by cyclic displacement to quantify artifact misinterpretation.
• Applied a statistical bootstrap algorithm to estimate fitting errors robustly.
• Evaluated methods using simulated atmospheric measurement spectra.

Main Results:

• The proposed methods accurately investigate DOAS spectral evaluation errors.
• Residual inspection estimates the impact of false artifact interpretation.
• Bootstrap algorithm provides reliable error estimation even with non-random residuals.
• Combined methods yield spectral evaluation error estimates within ~10% uncertainty.

Conclusions:

• The new approaches significantly enhance the accuracy of DOAS error assessment.
• Improved precision estimation strengthens the reliability of atmospheric trace gas measurements.
• These methods offer a robust solution for a long-standing DOAS data analysis problem.