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Donald A. Burrows

Abstract

A two-dimensional kinematic cloud model was developed to include snowflake growth by aggregation as well as riming and vapor deposition. The model includes new parameterizations of ice crystals and aggregates of ice crystals. The model was run and evaluated using a dataset gathered by the Sierra Cooperative Pilot Project (SCPP).

The model was tested for its sensitivity to two model parameters, a constant relating the mean mass of collected ice crystals to the mean mass of all ice crystals in the cloud, and an ice nucleation rate constant. Varying the collection constant by a factor of 2.5 had only minor effects on the model behavior, but varying the ice nucleation rate by a factor of 50 had significant effects on the relative importance of riming and vapor deposition growth.

Two problems that did develop in the model were the result of the model's bulk microphysical assumptions relating graupel and raindrop size to the magnitude of the mixing ratio, so that small concentrations behaved as cloud particles and would not fall out as precipitation.

In general, the model worked well in representing the distribution of ice crystals and aggregates in the cloud as well as the relative importance of the riming, aggregation, and vapor deposition processes. The location of ice crystals and snow aggregates compared well with aircraft observations in the real cloud including the location of a convergence zone with higher ice-crystal concentrations noted by the aircraft near the crest.

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Donald A. Burrows and Charles E. Robertson

Abstract

No abstract available.

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Steve R. Diehl, Donald A. Burrows, Eric A. Hendricks, and Robert Keith

Abstract

Two models have been developed to predict airflow and dispersion in urban environments. The first model, the Realistic Urban Spread and Transport of Intrusive Contaminants (RUSTIC) model, is a fast-running urban airflow code that rapidly converges to a numerical solution of a modified set of the compressible Navier–Stokes equations. RUSTIC uses the kω turbulence model with a buoyancy production term to handle atmospheric stability effects. The second model, “MESO,” is a Lagrangian particle transport and dispersion code that predicts concentrations of a released chemical or biological agent in urban or rural areas. As a preliminary validation of the models, concentrations simulated by MESO are compared with experimental data from wind-tunnel testing of dispersion around both a multistory rectangular building and a single-story L-shaped building. For the rectangular building, trace gas is forced out at the base of the downwind side, whereas for the L-shaped building, trace gas is forced out of a side door in the inner corner of the “L.” The MESO–RUSTIC combination is set up with the initial conditions of the wind-tunnel experiment, and the steady-state concentrations simulated by the models are compared with the wind-tunnel data. For the multistory building, a dense set of detector locations was available downwind at ground level. For the L-shaped building, concentration data were available at three heights in a lateral plane at a distance of one building height downwind of the lee side. A favorable comparison between model simulations and test data is shown for both buildings.

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Eric A. Hendricks, Steve R. Diehl, Donald A. Burrows, and Robert Keith

Abstract

An urban dispersion modeling system was evaluated using the Joint Urban 2003 field data. The system consists of a fast-running urban airflow model (RUSTIC, for Realistic Urban Spread and Transport of Intrusive Contaminants) that is coupled with a Lagrangian particle transport and diffusion model (MESO) that uses random-walk tracer diffusion techniques. Surface measurements from fast-response and integrated bag samplers were used to evaluate model performance in predicting near-field (less than 1 km from the source) dispersion in the Oklahoma City, Oklahoma, central business district. Comparisons were made for six different intense operating periods (IOPs) composed of three different release locations and stable nighttime and unstable daytime meteorological conditions. Overall, the models were shown to have an underprediction bias of 47%. A possible influence to this underprediction is that the higher density of sulfur hexafluoride in comparison with air was not taken into account in the simulations. The models were capable of predicting 42% of the sampler data within a factor of 2 and 83% of the data within a factor of 10. When the effects of large-scale atmospheric turbulence were included, the models were shown to be capable of predicting 51% of the data within a factor of 2. The results were further broken down into performance for varying meteorological conditions. For daytime releases, the models performed reasonably well; for nighttime releases the models performed more poorly. Two possible causes of the poorer nighttime comparisons are (a) an inability to model the suppression of vertical turbulence because of the assumption of isotropy in RUSTIC’s k–ω turbulence model and (b) difficulty in modeling the light and variable inflow winds. The best comparisons were found for the three continuous daytime releases of IOP-4. It was hypothesized that these good comparisons were a result of steadier inflow conditions combined with the fact that the release site was more exposed and closer to the sodar used for the inflow meteorological conditions.

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Donald A. Burrows, Eric A. Hendricks, Steve R. Diehl, and Robert Keith

Abstract

The Realistic Urban Spread and Transport of Intrusive Contaminants (RUSTIC) model has been developed as a simplified computational fluid dynamics model with a kω turbulence model to be used to provide moderately fast simulations of turbulent airflow in an urban environment. RUSTIC simulations were compared with wind tunnel measurements to refine and “calibrate” the parameters for the kω model. RUSTIC simulations were then run and compared with data from five different periods during the Joint Urban 2003 experiment. Predictions from RUSTIC were compared with data from 33 near-surface sonic anemometers as well as 8 sonic anemometers on a 90-m tower and a sodar wind profiler located in the Oklahoma City, Oklahoma, central business district. The data were subdivided into daytime and nighttime datasets and then the daytime data were further subdivided into exposed and sheltered sonic anemometers. While there was little difference between day and night for wind speed and direction comparisons, there was considerable difference for the turbulence kinetic energy (TKE) comparisons. In the nighttime cases, RUSTIC overpredicted the TKE but without any correlation between model and observations. On the other hand, for the daytime cases, RUSTIC underpredicted the TKE values and correlated well with the observations. RUSTIC predicted both winds and TKE much better for the exposed sonic anemometers than for the sheltered ones. For the 90-m tower location downwind of the central business district, RUSTIC predicted the vertical profile of wind speed and direction very closely but underestimated the TKE.

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