地基CO<sub>2</sub>浓度反演的数据质量控制及快速反演算法

doi:10.3964/j.issn.1000-0593(2025)11-3226-09

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Abstract Accurate monitoring of atmospheric CO₂ concentrations is crucial for climate change mitigation. Ground-based remote sensing provides high spatiotemporal resolution data, yet its accuracy is affected by multiple factors. This study utilizes the SCIATRAN radiative transfer model to simulate radiation transmission processes under various observational conditions, analyzing impacts of a priori profiles, spectral resolution, solar zenith angle (SZA), relative azimuth angle (RAA), temperature, aerosol optical depth (AOD), and boundary layer humidity on CO₂ retrieval. The following data quality control standards are established: (1) During observations, implement angle tolerance limits based on predicted daily CO₂ levels: SZA≤1.5° and RAA≤28° for high-concentration periods, with stricter thresholds of SZA≤1° and RAA≤27° for low-concentration periods. (2) Retrieval processes must incorporate real-time temperature and pressure profile data, such as the ERA5 reanalysis dataset (European Centre for Medium-Range Weather Forecasts) featuring dynamically updated parameters. (3) Urban-type aerosols are adopted, excluding data where AOD>0.3 or humidity>80%. A novel Global-Local Synergistic Optimization (GLSO) algorithm is proposed by integrating the strengths of the Genetic Algorithm (GA) and the Levenberg-Marquardt (L-M) method. Implementation of GLSO on EM27/SUN observation data demonstrates significant improvements: compared with the conventional L-M method, GLSO reduces iteration counts by 40%, decreases CO₂ total column deviation from 0.85% to 0.80%, and achieves a 0.13% discrepancy with TCCON XCO₂ data, outperforming the 0.27% deviation from L-M. Furthermore, the GLSO-derived CO₂ concentrations show less than 1% deviation from official GOSAT CO₂ products. This study establishes a robust framework for enhancing the precision and reliability of ground-based CO₂ monitoring through the optimization of observational protocols and advanced retrieval algorithms.

Key words：Ground-based remote sensing; SCIATRAN; Sensitivity analysis; CO₂ retrieval

Received: 2025-05-15 Accepted: 2025-07-31

ZTFLH:

P407

Corresponding Authors: CHU Xiao-xue E-mail: chuxiaoxue@guet.edu.cn

Cite this article:

LI Shu,YANG Le-yi,CHU Xiao-xue, et al. Data Quality Control and Rapid Retrieval Algorithm for Ground-Based CO₂ Concentration Inversion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(11): 3226-3234.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2025)11-3226-09 OR https://www.gpxygpfx.com/EN/Y2025/V45/I11/3226

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