Diurnal Carbon Monoxide Retrieval from FY-4B/GIIRS Using a Novel Machine Learning Method

Abstract

Carbon monoxide (CO) is one of the primary reactive trace gases in the Earth’s atmosphere and plays an important role in atmospheric chemistry. The Geostationary Interferometric Infrared Sounder (GIIRS) onboard the FY-4 series satellites is currently the only geostationary hyperspectral thermal infrared sensor capable of monitoring the unprecedented hourly CO concentrations in East Asia during both daytime and nighttime. In this study, we presented a radiative transfer model-driven machine learning approach to quickly convert CO spectral features extracted from FY-4B/GIIRS into CO total columns. We built machine learning models for land and ocean regions separately from July 2022 to June 2023, and these models reproduced more than 97.77% (land) and 98.49% (ocean) of the CO column variance in the training set. We estimated the absolute uncertainty of the retrieved CO column based on error propagation theory and found that it is dominated by GIIRS measurement noise. We compared the machine learning retrieval results with optimal estimation and ground-based Fourier transform infrared measurements, and the results reveal the consistent spatial distribution and temporal variation across these different datasets. Our results confirm that the machine learning method has the potential to provide reliable CO products without the computationally intensive iterative process required by traditional retrieval methods. The diel cycle and monthly variation of CO over land and ocean demonstrate the value of GIIRS in monitoring the long-range transport of anthropogenic pollutants and biomass burning emissions.