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Atmospheric Carbon Monoxide (CO) provides a window on the chemistry of theatmosphere since it is one of few chemical constituents that can be remotelysensed, and it can be used to determine budgets of other greenhouse gases suchas ozone and OH radicals. Remote sensing platforms in geostationary Earth orbitwill soon provide regional observations of CO at several vertical layers withhigh spatial and temporal resolution. However, cloudy locations cannot beobserved and estimates of the complete CO concentration fields have to beestimated based on the cloud-free observations. The current state-of-the-artsolution of this interpolation problem is to combine cloud-free observationswith prior information, computed by a deterministic physical model, which mightintroduce uncertainties that do not derive from data. While sharing featureswith the physical model, this paper suggests a Bayesian hierarchical model toestimate the complete CO concentration fields. The paper also provides a directcomparison to state-of-the-art methods. To our knowledge, such a model andcomparison have not been considered before.

Interpolating fields of carbon monoxide data using a hybrid statistical-physical model