92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Monday, 23 January 2012: 4:15 PM
Impact of Meteorological Variability on Regional Air Quality, Using 5-Year UH-AQF (2006-2010)
Room 339 (New Orleans Convention Center )
Hyun Cheol Kim, NOAA/OAR/ARL, Silver Spring, MD; and F. Ngan, B. Rappenglueck, P. Lee, R. saylor, and D. W. Byun

Multi-year Air Quality Forecast (AQF) system outputs were analyzed to assess the role of different meteorological parameters on regional air quality simulations. The University of Houston Air Quality Forecast (UH-AQF) system has been operational since June 2006 using the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5) and the Community Multiscale Air Quality (CMAQ), with multi-resolution simulations from 36-km CONUS to 4-km Texas regional domains. A unique advantage of the UH-AQF is that it consists of two systems with different emission assumptions. Each system has been operated independently more than 5 years without any modification of emissions or physical options, so it is suitable to investigate the impact of meteorological variability to regional air quality forecasts under a fixed emission scenario. Impacts of meteorological variables (e.g. surface temperature, wind speed, boundary layer, precipitation, and frontal activities) on forecast pollutant concentrations were investigated using surface observations from numerous observational sources, including the Meteorological Assimilation Data Ingest System (MADIS), the EPA AirNOW/AQS, the Interagency Monitoring of Protected Visual Environments (IMPROVE), and Continuous Ambient Monitoring Stations (CAMS). Results show that the yearly anomaly of key meteorological parameters (e.g. surface temperature anomaly), shows strong correlation with a pollutant's yearly variation (e.g. ozone anomaly), and the model's forecast performance (e.g. ozone bias). For the eastern US, a one-degree change of surface temperature was associated with 2.5-ppbv of afternoon ozone, and model showed best performance in high and low temperature, but showed high bias in the middle range temperature.

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