Evaluation of a Data Assimilation Technique for a Mesoscale Meteorological Model Used for Air Quality Modeling

Abstract

An observational data assimilation (ODA) technique was evaluated based on both its direct effect on meteorological model fields and its indirect effect on the results of two air quality models that input these meteorological fields: a Lagrangian particle model (LPM) and a photochemical model, the variable-grid version of the Urban Airshed Model (UAM-V). The purpose was to investigate the model performance improvements that are derived from using field-study observations with an ODA technique. The ODA technique, based upon Newtonian relaxation, was incorporated into the Colorado State University Regional Atmospheric Modeling System (RAMS). The technique was applied with rawinsonde, profiler, and sodar observations of winds, temperature, and moisture from an intensive field campaign during 3?7 August 1990 over the San Joaquin Valley in California. The RAMS meteorological fields, produced with and without the use of the ODA technique, and the results from the two air quality models using these two fields were evaluated and compared. The use of the ODA technique substantially reduced the gross errors of RAMS upper-air parameters but only produced minor reductions in the gross errors of RAMS surface-level parameters. The respective gross errors of RAMS upper-air results with and without ODA were 0.23 and 1.1 m s?1 for wind speeds and 1.1 and 1.9 K for temperatures. For RAMS surface-level results, the respective gross errors with and without ODA were 0.76 and 0.78 m s?1 for wind speeds and 3.4 and 3.8 K for temperatures. In both cases, the RAMS vector wind biases near the surface and aloft were less than 0.9 m s?1. Both LPM simulations of the field-study tracer experiments produced particle distributions that were consistent with observations and that were similar to each other. However, the more accurate vertical temperature structure due to the use of ODA produced shallower planetary boundary layers and resulted in larger surface tracer concentrations. Both UAM-V simulations of ozone produced similar ozone results, with less than 22% normalized gross mean errors for observations greater than 40 ppb on the last two days of the simulations. The strong influence of the UAM-V boundary-condition values on the sensitivity of UAM-V to the two meteorological inputs is demonstrated. This influence suggests that errors in other UAM-V inputs may obscure improvements in ozone modeling results from the use of ODA in creating meteorological inputs.