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HIGH TIME-RESOLVED COMPARISONS FOR IN-DEPTH PROBING OF CMAQ FINE-PARTICLES AND GAS PREDICTIONS
Citation:
Dennis, R L., S J. Roselle, R Gilliam, AND J. Arnold. HIGH TIME-RESOLVED COMPARISONS FOR IN-DEPTH PROBING OF CMAQ FINE-PARTICLES AND GAS PREDICTIONS. Presented at 27th NATO/CCMS International Technical Meeting on Air Pollution Modeling and Its Application, Banff, Alberta, Canada, October 25-29, 2005.
Impact/Purpose:
The goal of this task is to thoroughly characterize the performance of the emissions, meteorological and chemical/transport modeling components of the Models-3 system, with an emphasis on the chemical/transport model, CMAQ. Emissions-based models are composed of highly complex scientific hypotheses concerning natural processes that can be evaluated through comparison with observations, but not validated. Both performance and diagnostic evaluation together with sensitivity analyses are needed to establish credibility and build confidence within the client and scientific community in the simulations results for policy and scientific applications. The characterization of the performance of Models-3/CMAQ is also a tool for the model developers to identify aspects of the modeling system that require further improvement.
Description:
Model evaluation is important to develop confidence in models and develop an understanding of their predictions. Most comparisons in the U.S. involve time-integrated measurements of 24-hours or longer. Comparisons against continuous or semi-continuous particle and gaseous measurements reveal a wealth of information, providing insights into model functioning not possible with integrated measurements. Several comparisons for the U.S. EPA CMAQ model using U.S. Supersite data from 1999, 2001 and 2002 will be presented. Biases stemming from poor simulation of the pbl (meteorology) will be illustrated and apparent good agreement for conservative species for 24-hour integrated measurement will be shown to be due to offsetting errors. Biases stemming from chemistry will be explored with the help of model sensitivity analyses, particularly related to production of total nitrate. Diagnostic indicators will be used to evaluate ozone production. Biases will be judged for their potential to distort control strategy predictions of the model. Two data collection lessons that will be underlined are: the importance of simultaneous, continuous measurements of species to support interpretive, process-oriented analyses, and the necessity of having a full suite of inorganic gases to develop an understanding of model prediction issues. Most networks lack a full suite of gas and particle measurements.
The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce's National Oceanic and Atmospheric Administration (NOAA) under agreement number DW13921548. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reflect their policies or views.