You are here:
Implementation of Real-Time Bias-Adjusted O3 and PM2.5 Air Quality Forecasts and their Performance Evaluations during 2008 over the Continental United States
Citation:
KANG, D., R. MATHUR, AND S. T. RAO. Implementation of Real-Time Bias-Adjusted O3 and PM2.5 Air Quality Forecasts and their Performance Evaluations during 2008 over the Continental United States. Chapter 3, Douw G. Steyn and S. T. Rao (ed.), Air Pollution Modeling and its Applications XX. Springer Netherlands, , Netherlands, C(3.1):283-288, (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 Oceanic and Atmospheric Administration (NOAA), in partnership with the United States Environmental Protection Agency (EPA), is operationally implementing an Air Quality Forecast (AQF) system. This program, which couples NOAA's North American Mesoscale (NAM) weather prediction model with EPA's Community Multiscale Air Quality (CMAQ) model, has provided forecasts of ozone (03) mixing ratios since 2004. Developmental PM2.5 forecasts were initiated in 2005 (Mathur et al., 2008). The modeling domain for both the operational and developmental predictions currently covers the continental U.S. (CONUS).