Improving Error Estimates for Evaluating Satellite-Based Atmospheric CO2 Measurement Concepts through Numerical Simulations

Abstract

To assess the accuracy of satellite monitoring of anthropogenic CO2 emissions, inversions of satellite data in SWIR are usually combined with the assimilation of the total CO2 column into a Kalman filter that reconstructs the sources and sinks of atmospheric CO2. To provide error estimates of the total CO2 column for multi-month assimilation experiments of simulated satellite data, we parametrise these errors using linear regressions. These regression are obtained from a database that links meteorological situations, albedos, and aerosols to the errors in the inversion of the total CO2 column based on simulated satellite data for those conditions. The errors in this database are explicitly computed using the Bayesian estimation formalism, and the linear regressions are optimised by selecting appropriate predictors and predictants. For different levels of measurement noise, error simulations are performed over a period of several months using the albedo and aerosol data from MODIS.