Jenny Brynjarsdottir

Title: Optimal Estimation Versus MCMC for CO2 Retrievals (slides)

Abstract: The Orbiting Carbon Observatory-2 (OCO-2) collects infrared spectra from which
atmospheric properties are retrieved. OCO-2 operational data processing uses optimal
estimation (OE), a state-of-the-art approach to inference of atmospheric properties from
satellite measurements. One of the main advantages of the OE approach is computational
efficiency, but it only characterizes the first two moments of the posterior distribution
of interest. Here we obtain samples from the posterior using a Markov Chain Monte
Carlo (MCMC) algorithm and compare this empirical estimate of the true posterior to
the OE results. We focus on 600 simulated soundings that represent the variability of
physical conditions encountered by OCO-2 between November 2014 and January 2016.
We treat the two retrieval methods as ensemble and density probabilistic forecasts, where
the MCMC yields an ensemble from the posterior and the OE retrieval result provide
the first two moments of a normal distribution. To compare these methods, we apply
both univariate and multivariate diagnostic tools and proper scoring rules. The general
impression from our study is that when compared to MCMC, the OE retrieval performs
reasonablywell for the main quantity of interest, the column-averaged CO2 concentration
XCO2, but not for the full state vector X which includes a profile of CO2 concentrations
over 20 pressure levels, as well as several other atmospheric properties.