A XCO2 Retrieval Algorithm Coupled Spatial Correlation for the Aerosol and Carbon Detection Lidar

We describe the approach to estimating the atmospheric carbon dioxide (CO2) for the Aerosol and Carbon Detection Lidar (ACDL) onboard the Atmospheric Environment Monitoring Satellite (AEMS). The method estimates the optimal state vector by maximizing the measurement posterior probability under a given prior state vector probability distribution. A priori constraint considering the spatial correlation is used as regularization to solve the ill-posed problem. We ran a series of observing system simulation experiments to demonstrate the critical outcome and character percentage uncertainty reduction. The results show that the state vector uncertainty can be reduced by & SIM; 10% near the surface for the single sounding. The CO2 column-averaged dry air mole fraction (XCO2 ) derived by this algorithm is more stable than that obtained by the conventional algorithm and enables the monitoring of concentration changes for the multiple soundings.