Prognostic meteorological models are often used in a retrospective mode to provide inputs to the air quality models used in environmental planning. These inputs govern the advection, diffusion, chemical transformation, and eventual deposition of pollutants within regional air quality models such as CMAQ (Community Multi-scale Air Quality modeling system) and more local-scale assessments as from AERMOD. Before initiating the air quality simulations, it is important to quantify the biases and errors associated with the meteorological modeling.

The goal of the meteorological evaluation is to move toward an understanding of how the bias and error of the meteorological input data impact the resultant AQ modeling. Typically, there are two specific objectives: 1) determine if the meteorological model output fields represent a reasonable approximation of the actual meteorology that occurred during the modeling period (the “operational” evaluation), and 2) identify and quantify the existing biases and errors in the meteorological predictions in order to allow for a downstream assessment of how air quality modeling results are affected by issues associated with the meteorological data. (the “phenomenological” evaluation).

This analysis looks at the performance of the Penn State University / National Center for Atmospheric Research mesoscale model known as MM5 for two separate years (2001 and 2002) at two separate model resolutions (36 and 12km). The model evaluation is summarized for the entire domains, individual subregions within the domain, and certain individual sites to assess the suitability of the data to drive CMAQ and AERMOD applications at those scales. The operational evaluation includes statistical comparisons of model/observed pairs (e.g., bias, index of agreement, root mean square errors, etc.) for multiple meteorological parameters (e.g., temperature, water vapor mixing ratio, winds, etc.). The phenomenological evaluation is based on existing air quality conceptual models and assesses performance for varied phenomena such as trajectories, low-level jets, frontal passages, and airmass residence time and uses a different universe of statistics such as false alarm rates and probabilities of detection. In the end both MM5 datasets are shown to have strengths and weaknesses and several caveats are provided to the air quality modelers who plan to used these data.