Remote Sensing (Apr 2023)
Inversion of Wind and Temperature from Low SNR FPI Interferograms
Abstract
The temperature and wind in the middle and upper atmosphere can be obtained by recording the Doppler shift and broadening of the airglow emission, which is reflected by the interference ring from a ground-based Fabry–Perot interferometer (FPI) system. FPI observations are highly susceptible to weather and the external environment, which seriously affect the signal-to-noise ratio (SNR) of FPI interferograms. An SNR can significantly increase errors in determining the center of the interferogram, leading to inaccurate wind and temperature inversions. The calculation shows that the wind inversion from the interferogram decreases and the temperature increases for larger central errors. In this paper, we propose the maximum standard deviation method (MSDM) with high accuracy and robustness to determine the interference ring center. The performance of the MSDM is better achieved by using more than 100 1D interferogram bins to determine the center of interferograms. The robustness of the MSDM is investigated by computing numerous simulated interferograms with white Gaussian noise and Poisson noise, and compared with the two algorithms of binarization and peak fitting, which are usually used to invert wind and temperature from the interference ring of FPI. The results show that MSDM has higher accuracy and robustness than the other two algorithms. We also simulate the distortion interferogram when the FPI may be illuminated by inhomogeneous background light, which can introduce additional errors in wind and temperature, and the MSDM still performs better. Finally, we invert the wind and temperature from the real airglow interferogram by the Kelan (38.7°N, 111.6°E) FPI, which shows that both the wind and temperature inverted by MSDM better agree well with the FPI product than the other two algorithms. Therefore, the MSDM helps to improve the accuracy and stability to invert the wind and temperature.
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