Data assimilation using an ensemble of models: a hierarchical approach

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Rayner, PDate
2020-03-27Source Title
Atmospheric Chemistry and PhysicsPublisher
COPERNICUS GESELLSCHAFT MBHUniversity of Melbourne Author/s
Rayner, PeterAffiliation
School of Earth SciencesMetadata
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Journal ArticleCitations
Rayner, P. (2020). Data assimilation using an ensemble of models: a hierarchical approach. ATMOSPHERIC CHEMISTRY AND PHYSICS, 20 (6), pp.3725-3737. https://doi.org/10.5194/acp-20-3725-2020.Access Status
Open AccessAbstract
<jats:p>Abstract. One characteristic of biogeochemical models is uncertainty about their
formulation. Data assimilation should take this uncertainty into
account. A common approach is to use an ensemble of models. We must
assign probabilities not only to the parameters of the models but also to the
models themselves. The
method of hierarchical modelling allows us to calculate these
probabilities. This paper describes the approach, develops the algebra
for the most common case and then applies it to the Atmospheric Tracer Transport Model Intercomparison Project (TransCom). We see that the discrimination among models is
unrealistically strong, due to optimistic assumptions inherent in the
underlying inversion. The weighted ensemble means and variances from the
hierarchical approach are quite similar to the conventional values
because the best model in the ensemble is also quite close to the
ensemble mean. The approach can also be used for cross-validation in
which some data are held back to test estimates obtained with the rest. We
demonstrate this with a test of the TransCom inversions holding back
the airborne data. We see a slight decrease in the tropical sink and a
notably different preferred order of models.
</jats:p>
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