Research on a Data Preprocessing Method for a Vehicle-Mounted Solar Occultation Flux–Fourier Transform Infrared Spectrometer.

A vehicle-mounted solar occultation flux–Fourier transform infrared spectrometer uses the sun as an infrared light source to quantify molecular absorption in the atmosphere. It can be used for the rapid three-dimensional monitoring of pollutant emissions and the column concentration monitoring of greenhouse gases. The system has the advantages of high mobility and a capacity for noncontact measurement and measurement over long distances. However, in vehicle-mounted applications, vehicle bumps and obstacles introduce aberrations in the measured spectra, affecting the accuracy of gas concentration inversion results and flux calculations. In this paper, we propose a spectral data preprocessing method that combines a self-organizing mapping neural network and correlation analysis to reject anomalous spectral data measured by the solar occultation flux–Fourier transform infrared spectrometer during mobile observations. Compared to the traditional method, this method does not need to adjust the comparison threshold and obtain the training spectra in advance and has the advantage of automatically updating the weights without the need to set fixed correlation comparison coefficients. The accurate identification of all anomalous simulated spectra in the simulation experiments proved the effectiveness of the method. In the vehicle-mounted application experiment, 342 anomalous spectra were successfully screened from 1739 spectral data points. The experimental results show that the method can improve the accuracy of gas concentration measurement results and can be applied to a vehicle-mounted solar occultation flux–Fourier transform infrared spectrometer system to meet the preprocessing needs of a high number of spectral data in mobile monitoring. [ABSTRACT FROM AUTHOR]