In a landmark paper, Stauffer and Seaman (J. Appl. Meteor., 1994) developed a multiscale nudging strategy for mesoalpha-scale (i.e., 30 km horizontal grid spacing) and mesobeta-scale (i.e., 10 km horizontal grid spacing) meteorological modeling simulations using the fifth-generation Pennsylvania State University/National Center for Atmospheric Research Mesoscale Model (MM5). Their approach was based on a combination of nudging toward gridded analyses that were regularly spaced in time (i.e., “analysis nudging”) and nudging toward asynoptic observations that were irregular in space and time (i.e., “observation nudging”). Seaman et al. (J. Appl. Meteor., 1995) extended the multiscale nudging technique to a series of MM5 runs at 36 km, 12 km, and 4 km horizontal grid spacing where analysis nudging is emphasized at the coarser horizontal resolution and there is a gradual transition to observation nudging at the finer resolution. That nudging strategy is commonly used for meteorological modeling simulations that support retrospective air quality modeling.

In the mid-90's, background (or “first guess”) analyses were only archived every 12 h and at a coarse horizontal grid spacing (e.g., global 2.5 degree or 1 degree). Those analyses, even when “reanalyzed” to the simulation domain using available land-based observations, often did not reflect mesoalpha-scale details (much less mesobeta-scale details) that define the meteorology at those scales. Thus, nudging toward those data at the finer scales may not have been beneficial. Today background analyses from operational models at the National Centers for Environmental Prediction (NCEP) are archived with a finer temporal (i.e., 3 h or 6 h) and spatial (i.e., 12 km horizontal grid spacing) granularity. In addition, the NCEP analyses include remotely sensed data that supplement the land-based observations to give a more complete 3D description of the state of the atmosphere. Given these advances in modeling that have occurred, as well as the upgrade from MM5 to the Weather Research and Forecasting (WRF) Model, it is timely to explore whether or not the strategies that are suggested by Stauffer and Seaman (1994) and Seaman et al. (1995) are still appropriate. This presentation will report on progress toward the development of an effective nudging strategy using WRF that is suitable for retrospective air quality modeling at the mesobeta scale.

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.