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Space-Time Analysis of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 1 Air Quality Simulations
Citation:
Hogrefe, C., S. Roselle, R. Mathur, S. Rao, AND S. Galmarini. Space-Time Analysis of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 1 Air Quality Simulations. JOURNAL OF AIR AND WASTE MANAGEMENT. Air & Waste Management Association, Pittsburgh, PA, 64(4):388-405, (2014).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′s)Atmospheric Modeling Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
This study presents an evaluation of summertime daily maximum ozone concentrations over North America (NA) and Europe (EU) using the database generated during Phase 1 of the Air Quality Model Evaluation International Initiative (AQMEII). The analysis focuses on identifying temporal and spatial features that can be used to stratify operational model evaluation and to test the extent to which the various modeling systems can replicate such features present in the observations. Using a synoptic map typing approach, it is demonstrated that meteorological conditions associated with specific synoptic patterns have a distinct impact on model performance over both the eastern NA and EU. For example, the root mean square error of simulated daily maximum 8-hr ozone typically was twice as high under high cloud conditions than under low cloud conditions in eastern NA. Furthermore, results show that over both NA and EU the regional models participating in AQMEII were able to better simulate the observed variance in ambient ozone levels than the global model used to derive chemical boundary conditions, although the variance simulated by almost all regional models still is less that the observed variance on all scales. In addition, all modeling systems showed poor correlations with observed fluctuations on the intra-day time scale over both NA and EU. Using observed root mean square differences of daily maximum ozone concentrations between different sites as a criterion, we introduce a methodology to distinguish between locally influenced and regionally representative sites for the purpose of model evaluation. Results show that all models have worse model performance at locally influenced sites. Overall, the analyses presented in this paper show how observed temporal and spatial information can be used to stratify operational model performance and to test the modeling systems' ability to replicate observed temporal and spatial features, especially at scales the modeling systems are designed to capture.
URLs/Downloads:
HOGREFE REVISED FINAL WITH SUPPLEMENTSL ORD-003458_V1.PDF (PDF, NA pp, 3514 KB, about PDF)Journal of the Air and Waste Management Association
