Simulation-Based Uncertainty Quantification for Estimating Atmospheric CO2 from Satellite Data

Remote sensing of the atmosphere has provided a wealth of data for analyses and inferences in earth science. Satellite observations can provide information on the atmospheric state at fine spatial and temporal resolution while providing substantial coverage across the globe. For example, this capability can greatly enhance the understanding of the space-time variation of the greenhouse gas, carbon dioxide (CO2), since ground-based measurements are limited. NASA's Orbiting Carbon Observatory-2 (OCO-2) collects tens of thousands of observations of reflected sunlight daily, and the mission's retrieval algorithm processes these indirect measurements into estimates of atmospheric CO2. The retrieval is an inverse problem and consists of a physical forward model for the transfer of radiation through the atmosphere that includes absorption and scattering by gases, aerosols, and the surface. The model and other algorithm inputs introduce key sources of uncertainty into the retrieval problem. This article develops a computationally efficient surrogate model that is embedded in a simulation experiment for studying the impact of uncertain inputs on the distribution of the retrieval error.