Articles | Volume 13, issue 4
Geosci. Model Dev., 13, 2095–2107, 2020
https://doi.org/10.5194/gmd-13-2095-2020
Geosci. Model Dev., 13, 2095–2107, 2020
https://doi.org/10.5194/gmd-13-2095-2020

Development and technical paper 28 Apr 2020

Development and technical paper | 28 Apr 2020

Bayesian spatio-temporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields

Luke M. Western et al.

Model code and software

INLA_GHG_GMD L. M. Western https://doi.org/10.17605/OSF.IO/53W96

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Short summary
Assessments of greenhouse gas emissions using atmospheric measurements and meteorological models, or top-down methods, are important to verify national inventories or produce a stand-alone estimate where no inventory exists. We present a novel top-down method to estimate emissions. This approach uses a fast method called an integrated nested Laplacian approximation to estimate how these emissions are correlated with other emissions in different locations and at different times.