Since the industrialization era, with the continuously growing industrialization, urbanization, and energy consumption, greenhouse gas emissions have risen sharply, thus causing a constant increase in global temperatures. Atmospheric CO₂ is a crucial factor in global warming, and as a major anthropogenic greenhouse gas emission, it has caught continuous attention from the international community. Current high-precision CO₂ observations primarily rely on ground-based measurements and satellite remote sensing. While ground-based observations have advantages such as high accuracy and strong reliability, they are essentially single-point measurements and sparsely distributed globally, unable to provide detection on a global scale. Therefore, atmospheric CO₂ satellite remote sensing has become the main method for high-precision CO₂ monitoring on a global scale. However, with the development of satellite remote sensing from discrete to imaging observation techniques, there has been a substantial increase in remote sensing data volume, and existing retrieval algorithms struggle to meet computational time requirements. In our study, we propose a fast retrieval method for atmospheric CO₂. By constructing a suitable look-up table to replace the time-consuming components in the original algorithm, we aim to achieve fast atmospheric CO₂ retrieval.

We focus on the observational data from China’s Gaofen-5 satellite (GF-5), equipped with the greenhouse gas monitoring instrument (GMI), and present a fast retrieval algorithm for atmospheric CO₂. First, by leveraging the spectral characteristics of GMI, a line-by-line integration method is employed to construct a gas absorption cross-section look-up table suitable for GMI data, thereby expediting the calculation of gas absorption optical thickness. Secondly, via adopting data from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and based on Gaussian line shapes, fitting is performed on aerosol optical thickness profiles to establish a look-up table for Gaussian parameters of aerosol optical thickness, thus facilitating the computation of aerosol optical parameters. Finally, combined with atmospheric environmental parameters and satellite data, the atmospheric XCO₂ results are obtained by utilizing a radiative transfer calculation model and a physical retrieval algorithm, achieving fast retrieval of atmospheric CO₂.

We conduct a comparative validation of retrieval accuracy and computational efficiency by adopting total carbon column observing network (TCCON) site data and GMI observational data. Regarding computational efficiency, the original GMI retrieval algorithm and the proposed improved algorithm are compared in terms of processing time. In the context of single forward model calculation time, the improved algorithm reduces the forward model calculation time by over 85% compared to the original GMI algorithm, leading to an approximately 21.5 times improvement in calculation time. In terms of total computation time, the proposed algorithm achieves a time scale in minutes, significantly lower than the original algorithm’s computation time of over 1.5 h, which represents a substantial improvement in computational efficiency (Table 4). Regarding retrieval accuracy, a comparison is conducted between the retrieval results of the proposed algorithm and the original GMI algorithm. The error in the column concentration of CO₂ between the two algorithms remains within 2×10^-6 [Fig. 4(a)]. The average absolute error of XCO₂ between the two algorithms reaches 0.75×10^-6, with the high consistency of results reaching 85.5% [Fig. 4(b)]. This indicates that the proposed algorithm has a minimal influence on the error in the calculation results of GMI retrieval. By comparing the retrieval results of the original GMI algorithm, the improved algorithm, and TCCON site observational results, it is observed that the concentration discrepancies between the proposed algorithm and TCCON mostly stay within 4×10^-6. The average absolute error in the results is 3.01×10^-6, and the retrieval error is less than 1% (Fig. 5). Furthermore, the retrieval results of both algorithms are generally consistent, meeting the precision requirements for CO₂ retrieval.

To address the inefficiency in atmospheric CO₂ retrieval, we propose a fast atmospheric CO₂ retrieval method by adopting look-up tables for acceleration based on the practical requirements of GMI retrieval calculations. By constructing look-up tables for gas absorption cross-sections, the method achieves fast calculation of atmospheric layer-wise gas absorption optical thickness. Combined with molecular scattering calculations and fitting calculations for aerosol optical thickness based on aerosol parameter look-up tables, it reduces the computational time for time-consuming molecular absorption calculations in radiative transfer. By comparing the original GMI retrieval algorithm and the improved algorithm, the average absolute error between their retrieval results is 0.75×10^-6 with high consistency. When compared to TCCON site observational results, the average absolute error in the retrieval results is 3.01×10^-6, meeting the 1% precision requirement for retrieval accuracy. In terms of computation time, the improved retrieval algorithm significantly reduces the computation time while ensuring retrieval accuracy. The retrieval computation time can be reduced by over 80%, shifting the computational performance from the hourly level to the minute level. By conducting retrieval experiments and result verification, the proposed fast atmospheric CO₂ retrieval algorithm can substantially enhance the retrieval calculation speed while maintaining retrieval accuracy. In the future, this algorithm can be applied to multi-year GMI data at a global scale and other satellite observational data.