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Abstract:
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Tropospheric Carbon Monoxide (CO) is an important trace gas, air quality indicator, and one of the few atmospheric species that can be remotely sensed from space. The Measurement of Pollution in the Troposphere (MOPITT) instrument, on board the Terra satellite, provides CO mixing ratios at seven pressure levels on a global scale and can be used to better understand the transport and transformation of CO. To sensibly validate MOPITT data using in situ observations and for use in assimilation contexts, we propose a hierarchical Bayesian spatio-temporal model based on an advection/diffusion idea. These models and techniques for estimating parameters also have a general use for a variety of environmental problems that include remotely sensed measurements. We present results and discuss the properties of our model and what impact sparse data have on our model estimates.
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