Interpolating fields of carbon monoxide data using a hybrid statistical-physical model. (English) Zbl 1168.62396

Summary: Atmospheric Carbon Monoxide (CO) provides a window to the chemistry of the atmosphere since it is one of few chemical constituents that can be remotely sensed, and can be used to determine budgets of other greenhouse gases, such as ozone and OH radicals. Remote sensing platforms in the 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 the 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 comparisons have not been considered before.


62P12 Applications of statistics to environmental and related topics
62F15 Bayesian inference
86A10 Meteorology and atmospheric physics


spBayes; astsa
Full Text: DOI arXiv


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