The Impact of Nudging in the Meteorological Model for Retrospective Air Quality Simulations. Part II: Evaluating Collocated Meteorological and Air Quality Observations

For air quality modeling, it is important that the meteorological fields that are derived from meteorological models reflect the best characterization of the atmosphere. 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 in which Newtonian relaxation, or “nudging,” is used throughout the simulation period. This article, the second of two parts, provides additional 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 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 Modeling System (CMAQ). The predictions of surface and near-surface meteorological fields and ozone are compared with a small network of collocated meteorological and air quality observations. Comparisons of 2-m temperature, 10-m wind speed, and surface shortwave radiation show a significant degradation over time when nudging is not used, whereas 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. Domainwide error patterns in specific meteorological fields do not directly or systematically translate into error patterns in ozone prediction at these sites, regardless of whether 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.