高质量的CO2反演结果有助于准确认知其源汇信息、 预测未来气候变化趋势及提升全球碳循环的理解。 基于非线性最小二乘光谱拟合技术, 研究地基高分辨率傅里叶变换红外光谱反演环境大气中CO2浓度的反演产品质量优化方法。 借助O2窗口的反演结果, 将CO2柱浓度转化为柱平均干空气摩尔分数（column-averaged dry air mole fraction, XCO2）, 能有效修正系统共有误差; 采用一种经验修正模型, 能有效修正与大气质量因子相关的虚假日变化; 通过建立一定的红外光谱筛选法则, 能有效控制XCO2反演产品质量。 以一天的典型观测结果为例, 对产品质量优化前后的反演结果进行了对比, 优化后反演误差减小了60%, 以正午为中心两边各取一小时计算了XCO2的反演精度, 为0.071%（相当于0.28 ppm）, 符合TCCON(total carbon column observing network)规定的<0.1%精度范围。

CO2 retrievals with high quality facilitate resolving the sources and sinks of CO2 are helpful in predicting the trend in climate change and understanding the global carbon cycle. Based on a nonlinear least squares spectral fitting algorithm, we investigate the optimization method for CO2 products derived from ground-based high resolution Fourier transform infrared spectra. The CO2 vertical column densities (VCDs) are converted into column-averaged dry air mole fraction XCO2 by using the fitted O2 VCDs, and thus the system errors (e. g. pointing errors, ILS errors, zero-level offset) are corrected greatly. The virtual daily variation which is related to air mass factor is corrected with an empirical model. The spectra screening rule proposed in this paper can greatly improve the XCO2 quality. The CO2 retrievals before and after the optimized method are compared using a typical CO2 daily time series. After using the optimized method, the fitting error is reduced by 60%, and the two-hours-averaged precision is ~0.071% (equals to ~0.28 ppm), which is perfectly in line with the TCCON (the total carbon column observing network) threshold, i. e., less than 0.1%.