Projects per year
Abstract
Atmospheric trace gas inversions often attempt to attribute fluxes to a highdimensional grid using observations. To make this problem computationally feasible, and to reduce the degree of underdetermination, some form of dimension reduction is usually performed. Here, we present an objective method for reducing the spatial dimension of the parameter space in atmospheric trace gas inversions. In addition to solving for a set of unknowns that govern emissions of a trace gas, we set out a framework that considers the number of unknowns to itself be an unknown. We rely on the wellestablished reversiblejump Markov chain Monte Carlo algorithm to use the data to determine the dimension of the parameter space. This framework provides a singlestep process that solves for both the resolution of the inversion grid, as well as the magnitude of fluxes from this grid. Therefore, the uncertainty that surrounds the choice of aggregation is accounted for in the posterior parameter distribution. The posterior distribution of this transdimensional Markov chain provides a naturally smoothed solution, formed from an ensemble of coarser partitions of the spatial domain. We describe the form of the reversiblejump algorithm and how it may be applied to trace gas inversions. We build the system into a hierarchical Bayesian framework in which other unknown factors, such as the magnitude of the model uncertainty, can also be explored. A pseudodata example is used to show the usefulness of this approach when compared to a subjectively chosen partitioning of a spatial domain. An inversion using real data is also shown to illustrate the scales at which the data allow for methane emissions over northwest Europe to be resolved.
Original language  English 

Pages (fromto)  32133229 
Number of pages  17 
Journal  Geoscientific Model Development 
Volume  9 
Issue number  9 
Early online date  19 Sep 2016 
DOIs  
Publication status  Published  Sep 2016 
Fingerprint
Dive into the research topics of 'Estimation of trace gas fluxes with objectively determined basis functions using reversible jump Markov Chain Monte Carlo'. Together they form a unique fingerprint.

Advanced computing architecture to support the estimation and reporting of UK GHG emissions
13/11/13 → 13/10/15
Project: Research

GAUGE: Greenhouse Gas UK & Global Emissions (GUAGE)
Palmer, P., Rigby, M. L., Stavert, A. R., Wenger, A., Young, T. D. S. & O'Doherty, S.
30/06/13 → 29/09/17
Project: Research