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USE OF REMOTE SENSING AIR QUALITY INFORMATION IN REGIONAL SCALE AIR POLLUTION MODELING: CURRENT USE AND REQUIREMENTS
Citation:
RAO, S.T., R. MATHUR, R. PINDER, JON PLEIM, S. J. ROSELLE, AND B. ROY. USE OF REMOTE SENSING AIR QUALITY INFORMATION IN REGIONAL SCALE AIR POLLUTION MODELING: CURRENT USE AND REQUIREMENTS. Presented at Community Workshop on Air Quality Remote Sensing from Space: Defining and Optimum Observing Strategy, Boulder, CO, February 21 - 23, 2006.
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:
In recent years the applications of regional air quality models are continuously being extended to address atmospheric pollution phenomenon from local to hemispheric spatial scales over time scales ranging from episodic to annual. The need to represent interactions between physical and chemical atmospheric processes occurring at these disparate spatial and temporal scales requires the use of observation data beyond traditional in-situ networks so that the model simulations can be reasonably constrained. This presentation reviews several preliminary applications of remote sensing data in regional air quality modeling using the Community Multiscale Air Quality Model (CMAQ). The results from these early applications are discussed in context of (1) uncertainties in the model and in the remote sensing data and (2) needs for defining a future optimum observing strategy.