The U.S. Weather Research Program convenes expert working groups on a one-time basis to identify critical research needs in various problem areas. The most recent expert working group was charged to “identify and delineate critical meteorological research issues related to the prediction of air quality.” In this context, “prediction” is denoted as “forecasting” and includes the depiction and communication of the present chemical state of the atmosphere, extrapolation or nowcasting, and numerical prediction and chemical evolution on time scales up to several days. Emphasis is on the meteorological aspects of air quality.
The problem of air quality forecasting is different in many ways from the problem of weather forecasting. The latter typically is focused on prediction of severe, adverse weather conditions, while the meteorology of adverse air quality conditions frequently is associated with benign weather. Boundary layer structure and wind direction are perhaps the two most poorly determined meteorological variables for regional air quality prediction. Meteorological observations are critical to effective air quality prediction, yet meteorological observing systems are designed to support prediction of severe weather, not the subtleties of adverse air quality. Three-dimensional meteorological and chemical observations and advanced data assimilation schemes are essential. In the same way, it is important to develop high-resolution and self-consistent databases for air quality modeling; these databases should include land use, vegetation, terrain elevation, and building morphology information, among others. New work in the area of chemically adaptive grids offers significant promise and should be pursued. The quantification and effective communication of forecast uncertainty are still in their early stages and are very important for decision makers; this also includes the visualization of air quality and meteorological observations and forecasts. Research is also needed to develop effective metrics for the evaluation and verification of air quality forecasts so that users can understand the strengths and weaknesses of various modeling schemes. Last, but not of least importance, is the need to consider the societal impacts of air quality forecasts and the needs that they impose on researchers to develop effective and useful products.
Vaisala, Boulder, Colorado
University of Michigan, Ann Arbor, Michigan
Universidad Nacional Autonoma de Mexico, Mexico City, Mexico
University of Iowa, Iowa City, Iowa
University of California—Berkeley, Berkeley, California
Sonoma Technology Inc., Petaluma, California
Lawrence Livermore National Laboratory, Livermore, California
National Oceanic and Atmospheric Administration, Boulder, Colorado
Indiana University, Bloomington, Indiana
Harvard School of Public Health, Boston, Massachusetts
National Oceanic and Atmospheric Administration, Research Triangle Park, North Carolina
Washington State University, Pullman, Washington
National Center for Atmospheric Research, Boulder, Colorado
National Oceanic and Atmospheric Administration, Silver Spring, Maryland
Georgia Institute of Technology, Atlanta, Georgia
Naval Research Laboratory, Monterey, California
*This is an abridged version of the final report of PDT-11. The complete version can be found at http://box.mmm.ucar.edu/uswrp/PDT/eleven/PDT11.pdf.
+On assignment to U.S. Environmental Protection Agency