For air quality modeling, it is imperative that the meteorological fields that are derived from meteorological models reflect the best characterization of the atmosphere in a dynamically consistent manner. It is well known that the accuracy and overall representation of the modeled meteorological fields can be improved for retrospective simulations by creating dynamic analyses where Newtonian relaxation, or “nudging”, is used throughout the simulation period. Given the impact that meteorological conditions have on air quality simulations, it has been assumed that the resultant (one-way-coupled) air quality simulations would be more skillful by using dynamic analyses rather than meteorological forecasts to characterize the meteorology, and that the statistical trends in the meteorological model fields are also reflected in the air quality model. This paper provides insight into the value of using nudging-based data assimilation for dynamic analysis in the meteorological fields for air quality modeling. Meteorological simulations are generated by the fifth-generation Pennsylvania State University/National Center for Atmospheric Research Mesoscale Model (MM5) using both the traditional dynamic analysis approach and using forecasts for a summertime period. The resultant meteorological fields are then used for emissions processing and air quality simulations using the Community Multiscale Air Quality (CMAQ) Modeling System. The predictions of surface and near-surface meteorological fields and ozone are compared against large, independent networks of meteorological and air quality observations (National Weather Service (NWS) and Air Quality System (AQS), respectively) and against a small network of collocated meteorological and air quality observations (Clean Air Status and Trends Network (CASTNET)).

As expected, on average, the near-surface meteorological fields show a significant degradation over time in the forecasts (when nudging is not used), while the dynamic analyses maintain nearly constant statistical scores in time compared against NWS observations. The use of nudged MM5 fields in CMAQ generally results in better skill scores for daily maximum 1 h ozone mixing ratio simulations when compared against AQS observations. On average, the skill of the daily maximum 1 h ozone simulation deteriorates significantly over time when non-nudged MM5 fields are used in CMAQ. The daily maximum 1 h ozone mixing ratio also degrades over time in the CMAQ simulation that uses MM5 dynamic analyses, although to a much lesser degree, despite no aggregate loss of skill over time in the dynamic analyses themselves.

Using the collocated CASTNET sites, comparisons of 2 m temperature, 10 m wind speed, and surface shortwave radiation show a significant degradation over time when nudging is not used, while the dynamic analyses maintain consistent statistical scores over time for those fields. Using nudging in MM5 to generate dynamic analyses, on average, leads to a CMAQ simulation of hourly ozone with smaller error. Domain-wide error patterns in specific meteorological fields do not directly or systematically translate into error patterns in ozone prediction at the CASTNET sites, regardless of whether or not nudging is used in MM5, but large broad-scale errors in shortwave radiation prediction by MM5 directly affect ozone prediction by CMAQ at specific sites. These results affirm the advantage of using nudging in MM5 to create the meteorological characterization for CMAQ for retrospective simulations, and it is shown that MM5-based dynamic analyses are robust at the surface throughout 5.5 day simulations.

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.