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A PILOT STUDY FOR NEAR REAL-TIME AEROSOL MODELING AND AIR QUALITY CHARACTERIZATION
Citation:
HOGREFE, C., W. HAO, K. CIVEROLO, J. KU, G. SISTIA, J. O. YOUNG, G. L. WALTER, A. GILLILAND, AND K. L. SCHERE. A PILOT STUDY FOR NEAR REAL-TIME AEROSOL MODELING AND AIR QUALITY CHARACTERIZATION. Presented at EPA Science Forum 2005, Washington, DC, May 16 - 18, 2005.
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:
The poster will present the objectives and initial results of a pilot study conducted as a partnership between the U.S. Environmental Protection Agency (U.S. EPA), National Oceanic and Atmospheric Administration (NOAA), and the New York State Department of Environmental Conservation (NYSDEC). The primary objectives of this study are to implement, operate, and evaluate an automated, numerical, model-based air quality forecast system to provide daily predictions of O3 and PM2.5 and to assess the integrated use of modeled and observed concentrations to better characterize the spatial and temporal variations of air quality over New York. Based on simulations for summer 2004 and winter 2005, we will present an overview on the operational aspects such as data transfers, computing power, data storage, and scientific questions, such as the merits and demerits of Community Multiscale Air Quality (CMAQ) model-based forecasts in comparison to traditional approaches.