This research demonstrates the utility of supplemental observational data (i.e., from a dense mesonet) in an urban area to improve short-term, high-resolution meteorological model forecasts. In this work, the Fifth-Generation Pennsylvania State University/National Center for Atmospheric Research Mesoscale Model (MM5) is configured for a domain with 4-km horizontal grid spacing to provide 6-h forecasts for the Washington, D.C., metropolitan area. These forecasts include nudging of surface observations from the AWS and DCNet mesonets in a pre-forecast period to improve the initialization and the short-term forecast. There are nearly 300 surface stations that generally report hourly data in the forecast domain, and some of these observation sites are separated by only 1-2 km. Various experiments are conducted to devise a strategy to use the dense mesonet data to improve the short-term MM5 forecasts at this scale. These experiments include withholding data, lumping observations from the same grid cell together (e.g., “super-obbing”), and defining a general nudging technique (radius of influence, nudging weights, and variables to nudge). Additional analysis is performed to assess data quality, representativeness, and reliability. The ultimate goal is to use these MM5 forecast fields as background to provide an improved pollutant plume estimate (i.e., using HYSPLIT) over what could be achieved without the additional observational data. A preliminary demonstration of progress on this research will be presented.

Disclaimer: The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce's National Oceanic and Atmospheric Administration (NOAA) and under agreement number DW13921548. This work constitutes a contribution to the NOAA Air Quality Program. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reflect their policies or views.