Modeling Hourly Ozone Concentration Fields

TL;DR

This paper introduces a Bayesian hierarchical dynamic linear model for hourly ozone concentrations that captures temporal variations and diurnal cycles, but faces computational challenges limiting its application scope.

Contribution

It develops a flexible, time-varying coefficient model within a Bayesian framework for ozone data, highlighting its strengths and limitations.

Findings

Model captures diurnal and temporal variations in ozone levels.

Computational complexity restricts application to few sites.

Model reveals weaknesses in current approaches.

Abstract

This paper presents a dynamic linear model for modeling hourly ozone concentrations over the eastern United States. That model, which is developed within an Bayesian hierarchical framework, inherits the important feature of such models that its coefficients, treated as states of the process, can change with time. Thus the model includes a time--varying site invariant mean field as well as time varying coefficients for 24 and 12 diurnal cycle components. This cost of this model's great flexibility comes at the cost of computational complexity, forcing us to use an MCMC approach and to restrict application of our model domain to a small number of monitoring sites. We critically assess this model and discover some of its weaknesses in this type of application.