WOMBAT v1.0: A fully Bayesian global flux-inversion framework

Zammit‐Mangion, Andrew; Bertolacci, Michael; Fisher, Jenny A.; Stavert, Ann R.; Rigby, Matthew; Cao, Yi; Cressie, Noel

doi:10.5194/gmd-2021-181

Cited by 4 publications

(2 citation statements)

References 61 publications

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“…For a country to keep to its COP21 commitment, its government needs to know where the carbon sources are that can be decreased, and where the carbon sinks are that can be enhanced. These sources and sinks are more-or-less uncertain, and their estimation requires spatial-statistical methods (e.g., Michalak et al, 2004, Zammit-Mangion et al, 2021a. Planet Earth also faces a resources 'grand challenge' of producing enough food, water, energy, and shelter for its inhabitants.…”

Section: Grand Challenges That Need Spatial Statisticsmentioning

confidence: 99%

“…Several of these basis functions can be constructed by 'driving' the atmospheric model with pulses at different times and in different regions, which can then be used to model a temporal sequence of spatial fields of CO 2 (in parts per million). This approach to basis-function modelling of CO 2 is ubiquitous in the atmospheric sciences (e.g., Enting, 2002), and it was developed into a fully Bayesian statistical framework by Zammit-Mangion et al (2021a). Another example of physically motivated basis functions was provided by Wikle et al (2001), who used the equatorial normal mode orthogonal basis functions to model tropical ocean surface winds.…”

Section: Orthonormalitymentioning

confidence: 99%

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Basis-Function Models in Spatial Statistics

Cressie¹,

Sainsbury-Dale²,

Zammit‐Mangion³

2022

Preprint

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Spatial statistics is concerned with the analysis of data that have spatial locations associated with them, and those locations are used to model statistical dependence between the data. The spatial data are treated as a single realisation from a probability model that encodes the dependence through both fixed effects and random effects, where randomness is manifest in the underlying spatial process and in the noisy, incomplete, measurement process. The focus of this review article is on the use of basis functions to provide an extremely flexible and computationally efficient way to model spatial processes that are possibly highly non-stationary. Several examples of basis-function models are provided to illustrate how they are used in Gaussian, non-Gaussian, multivariate, and spatio-temporal settings, with applications in geophysics. Our aim is to emphasise the versatility of these spatial statistical models and to demonstrate that they are now centre-stage in a number of application domains. The review concludes with a discussion and illustration of software currently available to fit spatial-basis-function models and implement spatial-statistical prediction.

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Section: Grand Challenges That Need Spatial Statisticsmentioning

confidence: 99%

Section: Orthonormalitymentioning

confidence: 99%

Basis-Function Models in Spatial Statistics

Cressie¹,

Sainsbury-Dale²,

Zammit‐Mangion³

2022

Preprint

Self Cite

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The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies

et al. 2021

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Abstract. Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry–transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG and reactive species fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source Python-based tool to estimate the fluxes of various GHGs and reactive species both at the global and regional scales. It will allow for running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow for a comprehensive assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and we demonstrate how it operates in a simple academic case.

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WOMBAT v1.0: a fully Bayesian global flux-inversion framework

et al. 2022

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Abstract. WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are spatio-temporally correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and variances on non-fossil-fuel CO2 fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019). We also find that WOMBAT's predictions of out-of-sample retrievals obtained from the Total Column Carbon Observing Network (TCCON) are, for the most part, more accurate than those made by the MIP participants.

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WOMBAT v1.0: A fully Bayesian global flux-inversion framework

Cited by 4 publications

References 61 publications

Basis-Function Models in Spatial Statistics

Basis-Function Models in Spatial Statistics

The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies

WOMBAT v1.0: a fully Bayesian global flux-inversion framework

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