Incremental approach to the optimal network design for CO2 surface source inversion

Abstract

An algorithm for observational network design to obtain maximum constraints on CO2 flux estimate uncertainty by using inverse modeling and global transport model is presented. The extension to an existing network is made by adding new station locations one by one, based on their positive impact on the flux estimate uncertainty reduction. The approach is named Incremental Optimization (IO). This extension procedure performs equally well or better compared to the well known techniques, such as the simulated annealing, used in combinatorial optimization. We could reduce the estimated total CO2 flux uncertainty by 59%, 47%, 35%, and 29% relative to a reference network by using IO with additions of 3, 5, 12, and 20 stations, respectively. IO is shown to be efficient in studying various network configurations and re-emphasizes the need for CO2 measurements in continental Africa, South America, and Asia.