Post hoc Uncertainty Quantification for Remote Sensing Observing Systems

This article sets forth a practical methodology for uncertainty quantification of physical state estimates derived from remote sensing observing systems. Remote sensing instruments observe parts of the electromagnetic spectrum and use computational algorithms to infer the underlying true physical states. In current practice, many sources of uncertainty are not accounted for in this process, leading to underestimates of uncertainties on quantities of interest. We propose a procedure that combines Monte Carlo simulation experiments with statistical modeling to approximate distributions of unknown true states given point estimates of those states. Our method is carried out post hoc, that is, after the operational processing step. We demonstrate the procedure using four months of data from NASA's Orbiting Carbon Observatory-2 mission and compare it to validation measurements from the Total Column Carbon Observing Network.