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Real-Time Bias-Adjusted O3 and PM2.5 Air Quality Index Forecasts and their Performance Evaluations over the Continental United States
Citation:
KANG, D., R. MATHUR, AND S. T. RAO. Real-Time Bias-Adjusted O3 and PM2.5 Air Quality Index Forecasts and their Performance Evaluations over the Continental United States. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, 44(18):2203-2212, (2010).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis 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:
The National Air Quality Forecast Capacity (NAQFC) system, which links NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, provided operational ozone (O3) and experimental fine particular matter (PM2.5) forecasts over the continental United States (CONUS) during 2008. This paper describes the implementation of a real-time Kalman Filter (KF) bias-adjustment technique to improve the accuracy of O3 and PM 2.5 forecasts at discrete monitoring locations. The real-time surface level O3 and PM2.5 forecasts from the NAQFC system were post-processed by the KF bias-adjusted technique using the near real-time hourly O3 and PM 2.5 observations obtained from EPA's AIRNow measurement network. The KF bias-adjusted forecasts were created daily and provided 24-hour hourly bias-adjusted forecasts for O3 and PM2.5 at all AIRNow monitoring sites within the CONUS domain. The bias-adjustment post-processing implemented in this study requires minimal computational cost; requiring less than 10 minutes of CPU on a single processor Linux machine to generate 24-hr hourly bias-adjusted forecasts over the entire CONUS domain. The results show that the real-time KF bias-adjusted forecasts for both O3 and PM2.5 have performed as well as or even better than the previous studies when the same technique was applied to the historical O3 and PM2.5 time series from archived AQF in earlier years. Compared to the raw forecasts, the KF forecasts displayed significant improvement in the daily maximum 8-h O3 and daily mean PM2.5 forecasts in terms of both discrete (i.e., reduced erros, increased correlation coefficients, and index of agreement) and categorical (increased hit rate and decreased false alarm ratio) evaluation metrics at almost all locations during the study period in 2008.
URLs/Downloads:
Atmospheric Environment
Real-Time Bias-Adjusted O3 and PM2.5 Air Quality Index Forecasts and their Performance Evaluations over the Continental United States (PDF, NA pp, 898 KB, about PDF)