Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion

Andrew Zammit-Mangion*, Noel Cressie, Anita L. Ganesan, Simon O'Doherty, Alistair J. Manning

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

14 Citations (Scopus)
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Atmospheric trace-gas inversion refers to any technique used to predict spatial and temporal fluxes using mole-fraction measurements and atmospheric simulations obtained from computer models. Studies to date are most often of a data-assimilation flavour, which implicitly consider univariate statistical models with the flux as the variate of interest. This univariate approach typically assumes that the flux field is either a spatially correlated Gaussian process or a spatially uncorrelated non-Gaussian process with prior expectation fixed using flux inventories (e.g., NAEI or EDGAR). Here, we extend this approach in three ways. First, we develop a bivariate model for the mole-fraction field and the flux field. The bivariate approach allows optimal prediction of both the flux field and the mole-fraction field, and it leads to significant computational savings over the univariate approach. Second, we employ a lognormal spatial process for the flux field that captures both the lognormal characteristics of the flux field (when appropriate) and its spatial dependence. Third, we propose a new, geostatistical approach to incorporate the flux inventories in our updates, such that the posterior spatial distribution of the flux field is predominantly data-driven. The approach is illustrated on a case study of methane (CH4) emissions in the United Kingdom and Ireland.

Original languageEnglish
Pages (from-to)227-241
Number of pages15
JournalChemometrics and Intelligent Laboratory Systems
Early online date12 Sep 2015
Publication statusPublished - 15 Dec 2015


  • Conditional multivariate models
  • Hamiltonian Monte Carlo
  • Methane emissions
  • Multivariate geostatistics
  • Spatial statistics


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