The use of global and mesoscale meteorological model data to predict the transport and dispersion of tracer plumes over Washington, D. C.

The data from a yearlong tracer dispersion experiment over Washington, D. C., in 1984 were used to evaluate Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) dispersion model calculations using coarse global meteorological reanalysis data [NCEP-NCAR and 40-Yr ECMWF Re-Analysis (ERA-40)] and calculations using meteorological data fields created by running a high-resolution meteorological model [fifth-generation Pennsylvania State University-NCAR Mesoscale Model (MM5)]. None of the meteorological models were optimized for urban environments. The dispersion calculation using the ERA-40 data showed better performance than those using the NCEP-NCAR data and comparable performance to those using MM5 data fields. Calculations with MM5 data that used shorter-period forecasts were superior to calculations that used forecast data that extended beyond 24 h. Daytime dispersion model calculations using the MM5 data showed an underprediction bias not evident in calculations using the ERA-40 data or for nighttime calculations using either meteorological dataset. It was found that small changes in the wind direction for all meteorological model data resulted in dramatic improvements in dispersion model performance. All meteorological data modeled plume directions were biased 10 degrees-20 degrees clockwise to the measured plume direction. This bias was greatest when using the global meteorological data. A detailed analysis of the wind observations during the November intensive, which had the greatest difference between the model and measured plume directions, showed that only the very lowest level of observed winds could account for the transport direction of the measured plume. In the Northern Hemisphere, winds tend to turn clockwise with height resulting in positive directional transport bias if the lowest-level winds are not represented in sufficient detail by the meteorological model.