Monitoring Carbon Dioxide from Space：Retrieval Algorithm and Flux Inversion Based on GOSAT Data and Using CarbonTracker-China

Monitoring atmospheric carbon dioxide（CO_2） from space-borne state-of-the-art hyperspectral instruments can provide a high precision global dataset to improve carbon flux estimation and reduce the uncertainty of climate projection. Here, we introduce a carbon flux inversion system for estimating carbon flux with satellite measurements under the support of ＂The Strategic Priority Research Program of the Chinese Academy of Sciences—Climate Change： Carbon Budget and Relevant Issues＂. The carbon flux inversion system is composed of two separate parts： the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing（IAPCAS）, and Carbon Tracker-China（CT-China）, developed at the Chinese Academy of Sciences. The Greenhouse gases Observing SATellite（GOSAT） measurements are used in the carbon flux inversion experiment. To improve the quality of the IAPCAS-GOSAT retrieval, we have developed a post-screening and bias correction method, resulting in 25%–30% of the data remaining after quality control. Based on these data, the seasonal variation of XCO_2（column-averaged CO_2dry-air mole fraction） is studied, and a strong relation with vegetation cover and population is identified. Then, the IAPCAS-GOSAT XCO_2 product is used in carbon flux estimation by CT-China. The net ecosystem CO_2 exchange is-0.34 Pg C yr^（-1）（±0.08 Pg C yr^（-1））, with a large error reduction of 84%, which is a significant improvement on the error reduction when compared with in situ-only inversion.

An improved constraint method in optimal estimation of CO2 from GOSAT SWIR observations

We propose an algorithm that combines a pre-processing step applied to the a priori state vector prior to retrievals, with the modified damped Newton method （MDNM）, to improve convergence. The initial constraint vector pre-processing step updates the initial state vector prior to the retrievals if the algorithm detects that the initial state vector is far from the true state vector in extreme cases where there are CO2 emissions. The MDNM uses the Levenberg-Marquardt parameter ~,, which ensures a positive Hessian matrix, and a scale factor a, which adjusts the step size to optimize the stability of the convergence. While the algorithm iteratively searches for an optimized solution using observed spectral radiances, MDNM adjusts parameters ）, and a to achieve stable convergence. We present simulated retrieval samples to evaluate the performance of our algorithm and comparing it to existing methods. The standard deviation of our retrievals adding random noise was less than 3.8 ppmv. After pre-processing the initial estimate when it was far from the true value, the CO2 retrieval errors in the boundary layers were within 1.2 ppmv. We tested the MDNM algorithm＇s performance using GOSAT Llb data with cloud screening. Our preliminary validations comparing the results to TCCON FTS measurements showed that the average bias was less than 1.8 ppm and the correlation coefficient was approximately 0.88, which was larger than for the GOSAT L2 product.

短波红外CO_2反演过程约束研究及初步反演结果

短波红外通道卫星CO2遥感是近年国际研究热点。首先,开展了卫星观测对气溶胶及大气温度的敏感性研究;其次,针对基于最优化理论的非线性迭代反演方法反演过程中的不收敛问题,提出了修正的阻尼牛顿方法(MDNM),并利用模拟数据评估了MDNM方法的有效性;最后,利用GOSAT卫星数据反演CO2的垂直混合比浓度,并与地基TCCON站点数进行比对。研究结果表明:短波近红外CO2卫星遥感受气溶胶散射及温度影响明显;通过地基比对初步验证了MDNM具有良好的精度,两者的相关性R2达到了0.729。