Andrew J. Annunzio

Parameterizing Mesoscale Wind Uncertainty for Dispersion Modeling

Abstract A parameterization of numerical weather prediction uncertainty is presented for use by atmospheric transport and dispersion models. The theoretical development applies Taylor dispersion concepts to diagnose dispersion metrics from numerical wind field ensembles, where the ensemble variability approximates the wind field uncertainty. This analysis identifies persistent wind direction differences in the wind field ensemble as a leading source of enhanced “virtual” dispersion, and thus enhanced uncertainty for the ensemble-mean contaminant plume. This dispersion is characterized by the Lagrangian integral time scale for the grid-resolved, large-scale, “outer” flow that is imposed through the initial and boundary conditions and by the ensemble deviation-velocity variance. Excellent agreement is demonstrated between an explicit ensemble-mean contaminant plume generated from a Gaussian plume model applied to the individual wind field ensemble members and the modeled ensemble-mean plume formed from the ...

A Case Study of the Weather Research and Forecasting Model Applied to the Joint Urban 2003 Tracer Field Experiment. Part 2: Gas Tracer Dispersion

The Quick Urban & Industrial Complex (QUIC) atmospheric transport, and dispersion modelling, system was evaluated against the Joint Urban 2003 tracer-gas measurements. This was done using the wind and turbulence fields computed by the Weather Research and Forecasting (WRF) model. We compare the simulated and observed plume transport when using WRF-model-simulated wind fields, and local on-site wind measurements. Degradation of the WRF-model-based plume simulations was cased by errors in the simulated wind direction, and limitations in reproducing the small-scale wind-field variability. We explore two methods for importing turbulence from the WRF model simulations into the QUIC system. The first method uses parametrized turbulence profiles computed from WRF-model-computed boundary-layer similarity parameters; and the second method directly imports turbulent kinetic energy from the WRF model. Using the WRF model’s Mellor-Yamada-Janjic boundary-layer scheme, the parametrized turbulence profiles and the direct import of turbulent kinetic energy were found to overpredict and underpredict the observed turbulence quantities, respectively. Near-source building effects were found to propagate several km downwind. These building effects and the temporal/spatial variations in the observed wind field were often found to have a stronger influence over the lateral and vertical plume spread than the intensity of turbulence. Correcting the WRF model wind directions using a single observational location improved the performance of the WRF-model-based simulations, but using the spatially-varying flow fields generated from multiple observation profiles generally provided the best performance.

Contrasting the Use of Single-Realization versus Ensemble-Average Atmospheric Dispersion Solutions for Chemical and Biological Defense Analyses

AbstractChemical and biological (CB) defense systems require significant testing and evaluation before they are deployed for real-time use. Because it is not feasible to evaluate these systems with open-air testing alone, researchers rely on numerical models to supplement the defense-system analysis process. These numerical models traditionally describe the statistical properties of CB-agent atmospheric transport and dispersion (AT&D). While the statistical representation of AT&D is appropriate to use in some CB defense analyses, it is not appropriate to use this class of dispersion model for all such analyses. Many of these defense-system analyses require AT&D models that are capable of simulating dispersion properties with very short time-averaging periods that more closely emulate a “single realization” of a contaminant or CB agent dispersing in a turbulent atmosphere. The latter class of AT&D models is superior to the former for performing CB-system analyses when one or more of the following factors a...

The Effect of Topographic Variability on Initial Condition Sensitivity of Low-Level Wind Forecasts. Part II: Experiments Using Real Terrain and Observations

AbstractA study by Bieringer et al., which is Part I of this two-part study, demonstrated analytically using the shallow-water equations and numerically in controlled experiments that the presence of terrain can result in an enhancement of sensitivities to initial condition adjustments. The increased impact of adjustments to initial conditions corresponds with gradients in the flow field induced by the presence of the terrain obstacle. In cross-barrier flow situations the impact of the initial condition adjustments on the final forecast increases linearly as the height of the terrain obstacle increases. While this property associated with initial condition perturbations may be present in an analytic and controlled numerical environment, it is often difficult to realize these benefits in a more operationally realistic setting. This study extends the prior work to a situation with actual terrain, Doppler radar wind observations over the terrain, and observations from a surface mesonet for model verification...

Data Fusion and Prediction for CBRN Transport and Dispersion for Security

If a toxic contaminant is released in the atmosphere, either by accident or by terrorist activity, the responsible agency must rapidly identify the source, forecast the path and fate of the contaminant, warn the public or military command, and take action to protect the public, military personnel and equipment, and infrastructure. This process could be difficult if the location and type of source are not known and if there is not a dense network of meteorological stations. If, however, there are contaminant sensors, then the source and meteorological conditions can be back-calculated using a genetic algorithm-based software package and the transport and dispersion of the contaminant better predicted by applying data assimilation methods. This paper describes a technique for developing a sensor data fusion/meteorological data assimilation hybrid system. This work also analyzes the impact of noise in the data and assesses how much data are needed to perform the desired calculations.

The Effect of Topographic Variability on Initial Condition Sensitivity of Low-Level Wind Forecasts. Part I: Experiments Using Idealized Terrain

AbstractThe concept of improving the accuracy of numerical weather forecasts by targeting additional meteorological observations in areas where the initial condition error is suspected to grow rapidly has been the topic of numerous studies and field programs. The challenge faced by this approach is that it typically requires a costly observation system that can be quickly adapted to place instrumentation where needed. The present study examines whether the underlying terrain in a mesoscale model influences model initial condition sensitivity and if knowledge of the terrain and corresponding predominant flow patterns for a region can be used to direct the placement of instrumentation. This follows the same concept on which earlier targeted observation approaches were based, but eliminates the need for an observation system that needs to be continually reconfigured. Simulations from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) a...

Evolving Turbulence Realizations of Atmospheric Flow

A significant difference exists between estimates of contaminant atmospheric transport and dispersion calculated by an ensemble-averaged model and the turbulent details of any particular atmospheric transport and dispersion realization. In some cases, however, it is important to be able to make inferences of these realizations using ensemble-averaged models. It is possible to make such inferences if there are sensors in the field to report contaminant concentration observations. Any information determined about the atmospheric transport and dispersion realization can then be assimilated into a forecast model. This approach can enhance the accuracy of the atmospheric transport and dispersion forecast of a particular event. This work adopts that approach and reports on a genetic algorithm used to optimize the variational problem. Given contaminant sensor measurements and a transport and dispersion model, one can back-calculate unknown source and meteorological parameters. In this case, we demonstrate the dynamic recovery of unknown meteorological variables, including the transport variables that comprise the “outer variability” (wind speed and wind direction) and the dispersion variables that comprise the “inner variability” (contaminant spread). The optimization problem is set up in an Eulerian grid space, where the comparison of the concentration field variable between the predictions and the observations forms the cost function. The transport and dispersion parameters, which are determined from the optimization, are in Lagrangian space. This calculation is applied to continuous and instantaneous releases in a horizontally homogeneous wind field such as that observed during traditional transport and dispersion field experiments. The method proves to be successful at recovering the unknown transport and dispersion parameters for a numerical experiment.