## Projects

# Uncertainty Quantification for OCO-2

Our approach for OCO-2 is *simulation-based uncertainty
quantification*. This means that we create ensembles of synthetic true
state vectors, and use them drive a Monte-Carlo experiment that provides a
corresponding ensemble of retrieved state vector estimates. Elements of
these two ensembles are paired, resulting in a joint sample that we use to
model the relationship between the true state and its retrieved
counterpart. We fit a statistical model to the joint sample, and derive
regression-style equations to predict the distribution of the true state
given the retrieved estimate. Then, when we receive an actual OCO-2
retrieval from the mission's data processing chain, we plug it in to the
predictor to obtain the distribution of the true state as a function of the
estimated state.

Comparison among simulation-based pdf (red), retrieved pdf (blue), and true CO\(_2\) concentration (green) for one OCO-2 footprint at Lamont, OK. | Comparison among simulation-based pdf (red), retrieved pdf (blue), and true CO\(_2\) concentration (green) for one OCO-2 footprint at Tsukba, Japan. |

## Selected Publications

Braverman, A., Hobbs, J., Teixeira, J., and Gunson, M. (2021). “Post hoc Uncertainty Quantification for Remote Sensing Observing Systems,” *SIAM/ASA Jour. Uncert. Quantification*. Accepted. Download (CL 21-0606).

Hobbs, J., Braverman, A., Cressie, N., Granat, R., and Gunson, M. (2017). “Simulation-Based Uncertainty Quantification for Estimating Atmospheric CO2
from Satellite Data,” *SIAM/ASA Journal on Uncertainty Quantification*, **5**(1), pp. 956-985. Download.