Optimization-Based Uncertainty Quantification for Carbon Flux Inversion

Music City Center 

An important use of remote sensing data is in data assimilation where satellite observations are used to constrain a large-scale dynamical system by solving an ultra-high-dimensional inverse problem. While point estimation in such settings is relatively well-established, uncertainty quantification in these problems has been a major open challenge. Here we present a new optimization-based uncertainty quantification framework for computing confidence intervals in large-scale data assimilation. We particularly focus on carbon flux inversion, the problem of inferring the net ecosystem exchange of CO2 by assimilating satellite observations into a global atmospheric transport model. We discuss the algorithmic and computational challenges of computing the confidence intervals in this setting and present custom-made optimizers for solving this problem efficiently. We use this approach to compute one-sided confidence intervals for the Continental US and Northern Hemisphere CO2 fluxes based on simulated GOSAT observations. To the best of our knowledge, these are the first statistically rigorous confidence intervals obtained in a realistic-scale data assimilation problem.

Data assimilation

Inverse problem

Remote sensing

Carbon cycle

Adjoint method