Accelerated MCMC for Satellite-Based Measurements of Atmospheric CO2

Abstract

Markov Chain Monte Carlo (MCMC) is a powerful and promising tool for assessing the uncertainties in the Orbiting Carbon Observatory 2 (OCO-2) satellite’s carbon dioxide measurements. Previous research in comparing MCMC and Optimal Estimation (OE) for the OCO-2 retrieval has highlighted the issues of slow convergence of MCMC, and furthermore OE and MCMC not necessarily agreeing with the simulated ground truth. In this work, we exploit the inherent low information content of the OCO-2 measurement and use the Likelihood-Informed Subspace (LIS) dimension reduction to significantly speed up the convergence of MCMC. We demonstrate the strength of this analysis method by assessing the non-Gaussian shape of the retrieval’s posterior distribution, and the effect of operational OCO-2 prior covariance’s aerosol parameters on the retrieval. We further show that in our test cases we can use this analysis to improve the retrieval to retrieve the simulated true state significantly more accurately and to characterize the non-Gaussian form of the posterior distribution of the retrieval problem.

Neural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia

“…Climate change is one of the greatest challenges to the future of Earth, and it stems from global warming, which is accelerated by anthropogenic emissions of greenhouse gases (Lamminpää et al, 2019). The major warming effects are caused by atmospheric CO 2 emissions, and significant amounts of these emissions are contributed by fossil fuel combustion and some industrial activities, such as the calcination of limestone during cement production (Hutchins et al, 2017).…”

Forward Model Emulator for Atmospheric Radiative Transfer Using Gaussian Processes And Cross Validation

“…In recent years, the uncertainty quantification and statistics community has benefited enormously by utilizing the surrogate model by (Hobbs et al, 2017) to explore the OCO-2 retrieval in numerous applications (Brynjarsdóttir et al (2018), (Lamminpää et al, 2019), Nguyen and Hobbs (2020), Hobbs et al (2021), Patil et al (2022)). We remark that for similar purposes, our emulator can be used as an even faster surrogate.…”

“…(Gurney et al (2002), Patra et al (2007), Liu et al (2017), Palmer et al (2019), Crowell et al (2019), Peiro et al (2022), Byrne et al (2022)). Recent advancements of applying Markov Chain Monte Carlo (MCMC, Brynjarsdóttir et al (2018), Lamminpää et al (2019)) for non-Gaussian posterior characterization, and Simulation Based Uncertainty Quantification (Turmon and Braverman (2019), Braverman et al (2021)) for capturing the overall uncertainty in the retrieval pipeline have been successfully deployed to address persisting retrieval errors. These methods, although comprehensive, suffer equally from computational speed issues as they require extensive amounts of FP evaluations.…”

Objective frequentist uncertainty quantification for atmospheric CO$_2$ retrievals

“…The noise can nevertheless be approximated well using an additive Gaussian noise term with zero mean and variance proportional to the mean signal. Following [15] and [25], we let Σ ε be diagonal with elements…”