Next Article in Journal
Global Ionospheric Model Accuracy Analysis Using Shipborne Kinematic GPS Data in the Arctic Circle
Next Article in Special Issue
Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories
Previous Article in Journal
Automated Cloud and Cloud-Shadow Masking for Landsat 8 Using Multitemporal Images in a Variety of Environments
Previous Article in Special Issue
Pixel Size and Revisit Rate Requirements for Monitoring Power Plant CO2 Emissions from Space
Open AccessArticle

Accelerated MCMC for Satellite-Based Measurements of Atmospheric CO2

Finnish Meteorological Institute, 00560 Helsinki, Finland
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91125, USA
Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH 44106-7058, USA
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(17), 2061;
Received: 26 June 2019 / Revised: 14 August 2019 / Accepted: 26 August 2019 / Published: 2 September 2019
(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)
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. View Full-Text
Keywords: OCO-2; Markov Chain Monte Carlo; carbon dioxide; aerosols; retrieval; uncertainty quantification OCO-2; Markov Chain Monte Carlo; carbon dioxide; aerosols; retrieval; uncertainty quantification
Show Figures

Graphical abstract

MDPI and ACS Style

Lamminpää, O.; Hobbs, J.; Brynjarsdóttir, J.; Laine, M.; Braverman, A.; Lindqvist, H.; Tamminen, J. Accelerated MCMC for Satellite-Based Measurements of Atmospheric CO2. Remote Sens. 2019, 11, 2061.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop