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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 models 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.
Mexico City tracks ground-level ozone levels to assess compliance with national ambient air quality standards and to prevent environmental health emergencies. Ozone levels show distinct daily patterns, within the city, and over the course of the year
The equations of a physical constitutive model for material stress within tantalum grains were solved numerically using a tetrahedrally meshed volume. The resulting output included a scalar vonMises stress for each of the more than 94,000 tetrahedra
Storm surge, the onshore rush of sea water caused by the high winds and low pressure associated with a hurricane, can compound the effects of inland flooding caused by rainfall, leading to loss of property and loss of life for residents of coastal ar
For hourly PM2.5 concentration prediction, accurately capturing the data patterns of external factors that affect PM2.5 concentration changes, and constructing a forecasting model is one of efficient means to improve forecasting accuracy. In this stu
The end-Permian mass extinction is the most severe known from the fossil record. The most likely cause is massive volcanic activity associated with the formation of the Permo-Triassic Siberian flood basalts. A proposed mechanism for extinction due to