  Atmospheric Carbon Monoxide (CO) provides a window on the chemistry of the
atmosphere since it is one of few chemical constituents that can be remotely
sensed, and it can be used to determine budgets of other greenhouse gases such
as ozone and OH radicals. Remote sensing platforms in geostationary Earth orbit
will soon provide regional observations of CO at several vertical layers with
high spatial and temporal resolution. However, cloudy locations cannot be
observed and estimates of the complete CO concentration fields have to be
estimated based on the cloud-free observations. The current state-of-the-art
solution of this interpolation problem is to combine cloud-free observations
with prior information, computed by a deterministic physical model, which might
introduce uncertainties that do not derive from data. While sharing features
with the physical model, this paper suggests a Bayesian hierarchical model to
estimate the complete CO concentration fields. The paper also provides a direct
comparison to state-of-the-art methods. To our knowledge, such a model and
comparison have not been considered before.
