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EVALUATION OF SEVERAL PM 2.5 FORECAST MODELS USING DATA COLLECTED DURING THE ICARTT/NEAQS 2004 FIELD STUDY
Citation:
MCKEEN, S., S. H. CHUNG, J. WILCZAK, G. A. GRELL, I. DJALALOVA, S. PECKHAM, W. GONG, V. BOUCHET, R. MOFFET, Y. TANG, G. R. CARMICHAEL, R. MATHUR, AND S. YU. EVALUATION OF SEVERAL PM 2.5 FORECAST MODELS USING DATA COLLECTED DURING THE ICARTT/NEAQS 2004 FIELD STUDY. JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES. American Geophysical Union, Washington, DC, 112(D10S20):1-20, (2007).
Impact/Purpose:
The objectives of this task include: (1) to continuously evaluate and analyze the forecast results to provide diagnostic information on model performance and inadequacies to guide further evolution and refinements to the CMAQ model, and (2) extending the utility of the daily air quality forecast model data being produced by NOAA's National Weather Service (NWS) as part of a NOAA/EPA collaboration in air quality forecasting, to EPA mission-oriented activities. These objectives include developing and maintaining a long-term database of air quality modeling results (ozone and PM2.5), performing periodic analysis and assessments using the data, and making the air quality database available and accessible to States, Regions, RPO's and others to use as input data for regional/local scale air quality modeling for policy/regulatory purposes.
Description:
Real-time forecasts of PM2.5 aerosol mass from seven air-quality forecast models (AQFMs) are statistically evaluated against observations collected in the northeastern U.S. and southeastern Canada from two surface networks and aircraft data during the summer of 2004 ICARTT/NEAQS field campaign. The AIRNOW surface network is used to evaluate PM2.5 aerosol mass, the U.S. EPA STN network is used for PM2.5 aerosol composition comparisons, and aerosol size distribution and composition measured from the NOAA P-3 aircraft are also compared. Statistics based on mid-day eight hour averages, as well as twenty four hour averages are evaluated against the AIRNOW surface network. When the eight-hour average PM2.5 statistics are compared against equivalent ozone statistics for each model, the analysis shows that PM2.5 forecasts possess nearly equivalent correlation, less bias, and better skill relative to the corresponding ozone forecasts. An analysis of the diurnal variability shows that most models do not reproduce the observed diurnal cycle at urban and suburban monitor locations, particularly during the nighttime to early morning transition. While observations show median rural PM2.5 levels similar to urban and suburban values, the models display noticeably smaller rural/urban PM2.5 ratios. The ensemble PM2.5 forecast, created by combining six separate forecasts with equal weighting, is also evaluated and shown to yield the best possible forecast in terms of the statistical measures considered. The comparisons of PM2.5 composition with NOAA P-3 aircraft data reveals two important features: 1) the organic component of PM2.5 is significantly under-predicted by all the AQFMs, and 2) those models that include aqueous phase oxidation of SO2 to sulfate in clouds over-predict sulfate levels while those AQFMs that do not include this transformation mechanism under-predict sulfate. Errors in PM2.5 ammonium levels tend to correlate directly with errors in sulfate. Comparisons of PM2.5 composition with the U.S. EPA STN network for three of the AQFMs show that sulfate biases are consistently lower at the surface than aloft. Recommendations for further research and analysis to help improve PM2.5 forecasts are also provided.