JUNE 2006 D RAXLER 383 The Use of Global and Mesoscale Meteorological Model Data to Predict the Transport and Dispersion of Tracer Plumes over Washington, D.C. ROLAND R. DRAXLER National Oceanic and Atmospheric Administration/Air Resources Laboratory, Silver Spring, Maryland (Manuscript received 11 August 2005, in final form 8 November 2005) ABSTRACT 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 calcula- tions 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 meteo- rological 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 per- formance 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°–20° 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 Hemi- sphere, 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. 1. Introduction duties of the local meteorological forecaster. Current National Weather Service instructions to their local There has been increasing interest in providing more forecast offices on providing non-weather-related accurate pollutant plume dispersion predictions at dis- emergency products (NWS 2004) outline in some detail tances of a few to tens of kilometers from the source. At the dispersion model products that are routinely avail- this range plumes interact with the local environment in able or could be requested and for which the local fore- more complex ways that limit the traditional approach cast office would be expected to provide interpretation. of using a single meteorological observation near the Online dispersion model training is now available for pollutant source location. At the same time the plumes NWS forecasters (information online at http:// are not yet large enough to span the domain of even a meted.ucar.edu/dispersion/cam_hys/noflash.htm). single grid cell from routine global or even regional The goal of this analysis is to use some historical forecast model outputs. The interpretation of disper- experimental dispersion data appropriate over an ur- sion model plumes, their interaction with the local en- ban area to evaluate how well routine meteorological vironment, and the appropriateness of regional model model products in conjunction with a dispersion model guidance are starting to become part of the routine could simulate pollutant releases. There are many dif- ferent dispersion models that can be used to evaluate Corresponding author address: Roland R. Draxler, National experimental tracer data. Many countries with a na- Oceanic and Atmospheric Administration/Air Resources Labo- ratory, R/ARL-SSMC3, Rm. 3350, 1315 East–West Highway, Sil- tional meteorological service have some dispersion ver Spring, MD 20910. modeling capability available as demonstrated by the E-mail: [email protected] 28 models that participated in the European Tracer Ex- Unauthenticated | Downloaded 09/26/21 07:32 PM UTC WAF926 384 WEATHER AND FORECASTING VOLUME 21 periment (Graziani et al. 1998). Chang et al. (2003) rerun with more modern mesoscale meteorological compared the performance of several of the models models. Initial and boundary conditions (ICs and BCs) supported by the U.S. Environmental Protection for a mesoscale model can easily be obtained from the Agency and U.S. Department of Defense to the data National Centers for Environmental Prediction– collected from a series of tracer experiments in Utah National Center for Atmospheric Research (NCEP– and Nevada and they found that the dispersion model’s NCAR; Kalnay et al. 1996) or the European Centre for performance was highly dependent upon the method Medium-Range Weather Forecasts (ECMWF; Kåll- used to interpolate the meteorological observations. berg et al. 2004) reanalysis projects. Although im- However, special meteorological observations may not proved urbanized mesoscale models are under devel- always be available in emergency response applica- opment (Otte et al. 2004), for this study the standard tions. The Hybrid Single-Particle Lagrangian Inte- version of the fifth-generation Pennsylvania State Uni- grated Trajectory (HYSPLIT; Draxler and Hess 1998) versity–NCAR Mesoscale Model (MM5; Grell et al. model is currently being used by the NWS to support 1994) was run to provide data fields at a horizontal dispersion predictions for large-scale nuclear incidents resolution down to 4 km. Although the global meteo- (Draxler et al. 1997), volcanic eruptions, wild fire rological data are based upon analyses, the resulting smoke transport, and for emergency response acci- high-resolution MM5 simulations are in effect very dents, using routine NWS forecast model products. The short forecasts and are very comparable to what would NWS guidance document suggests that HYSPLIT be available in a real emergency situation today. could be applied at distances of 10 km and greater from Evaluating the meteorological and dispersion com- a pollutant source. The model’s parameterizations are ponents of a calculation requires model evaluation not limited to just long-range simulations. Typically, the tools that work well with air concentration data and prediction of any dispersion model that uses an external their inherent uncertainties. Chang and Hanna (2004) source for its meteorological data is limited by the as- provide a detailed review of the advantages and disad- sumptions made in deriving those data, for example, vantages of various statistical approaches to dispersion how the meteorological model’s resolution and defini- model evaluation. Dispersion model simulations have tion of the local terrain features influence the calcula- been run for the entire year of 1984 using different tion of the low-level wind field. meteorological data. Statistical results have been used Issues surrounding urban-scale dispersion are again to draw some initial conclusions about the meteorologi- of great interest and experiments have recently been cal and dispersion models’ performance. These statisti- conducted in New York City, New York; Oklahoma cal analyses will be further refined to enhance the dif- City, Oklahoma; and Salt Lake City, Utah (SLC). The ferences between the simulations. Finally, a detailed SLC experiments have been the subject of several analysis has been conducted for one of the intensive model evaluation studies (Chang et al. 2005; Warner et experiments, in which there are sufficient measure- al. 2004). Although they were conducted in the central ments to show the plume in the sampling network, pro- urban area, the SLC experiments consisted of only a viding a visual representation of the conclusions de- few tracer releases with the most distant sampling arc 6 rived from the statistical analysis. km downwind. Generally, these recent experiments are limited to a few releases, during carefully selected sce- 2. Tracer experimental data: METREX narios, determined by criteria such as no rain, steady moderate winds, and certain wind directions. In terms The tracer experiment started in December of 1983 of experimental data, there is only one experiment that and ran through December 1984. Two inert perfluoro- contains a sufficient number of tracer releases to cover carbon [100 g hϪ1 perfluoromonomethylcyclohexane Ϫ1 a broad range of weather situations. The Metropolitan (C7F14), PMCH; 300 g h perfluordimethylcyclohex- Tracer Experiment (METREX; Draxler 1987) was con- ane (C8F16), PDCH] tracers (PFTs) were released for ducted for more than 1 yr in 1984 around Washington, 6-h durations from two locations simultaneously about D.C., and its suburbs and consisted of hundreds of 20 km outside of Washington, D.C., at regular 36-h tracer releases with air samples collected from a few intervals alternating between nighttime (starting at kilometers to about 75 km from the tracer source loca- 0300 UTC) and daytime releases (starting at 1500 tions. UTC). Continuous air samples were collected as 8-h During the time METREX was conducted, few me- averages at one urban and two suburban sites and as teorological models were available that could be ap- 30-day averages (samples were changed the first of each plied at a scale of less than 10 km. Computational