Statistical properties of differences between low and high resolution CMAQ runs with matched initial and boundary conditions
Numerical simulations of air quality models provide unique and different outputs for different choices of grid size. Thus, an important task is to understand the characteristics of model outcomes as a function of grid size in order to assess the quality of the model as to its fitness for meeting a specific design objective. This type of assessment is somewhat different than that of traditional operational performance and diagnostic type model evaluation. There, the objective is towards assessing errors in numerical models of air quality and utilizing concentration measurements from monitors to provide the bases for guidance towards model improvement and for their assessment of ability to predict and retrospectively map air quality. However, observations used as truth to assess model performance have themselves properties unique to the data collection protocols, siting and spatial density of deployment. In the data assimilation community, the term model error is used for the difference between model output given perfect inputs and the truth (Kalnay, 2003). In this paper, we are concerned with one aspect of this model error, the discrepancy due to discretization of space by choice of grid size in the model. To understand discrepancy due to discretization, outputs from the Community Multiscale Air Quality model (CMAQ) at two resolutions are studied. The lower resolution run is carried out so that its initial and boundary conditions are as similar as possible to those for the higher resolution run, thus minimizing this source of discrepancies and allowing us to isolate discrepancies due to discretization. Differences are analyzed from a statistical perspective by comparing marginal distributions of two outputs and considering spatial variation of the differences. Results indicate sharp increases in spatial variation of the differences for the first few hours of running the model, followed by small increases thereafter. The spatial variation of the differences depends on the individual spatial structure of the original processes, which we show varies with the time of day. We also show that the spatial variations on sub-regions depend on whether the sub-region is in a rural or an urban area.
multiscale air quality modeling cmaq fine scale modeling sub-grid variability spatial variogram