Ensemble Simulations with Coupled Atmospheric Dynamic and Dispersion Models: Illustrating Uncertainties in Dosage Simulations

Warner, Thomas T.; Sheu, Rong-Shyang; Bowers, James; Sykes, R. I.; Dodd, Gregory C.; Henn, D. S.

doi:10.1175/1520-0450(2002)041<0488:eswcad>2.0.co;2

Cited by 40 publications

(37 citation statements)

References 32 publications

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“…Recently, Scheele and Siegmund (2001) coupled members of a meteorological model prediction system with a trajectory model to estimate forecast error and found that higher-resolution meteorological ensemble outputs were needed to capture complex boundary layer processes. Warner et al (2002) developed a higherresolution short-range meteorological model ensemble system to drive a series of air quality simulations. Atmospheric dispersion probability density functions were computed at each model grid point.…”

Section: American Meteorological Societymentioning

confidence: 99%

Meteorological Research Needs for Improved Air Quality Forecasting: Report of the 11th Prospectus Development Team of the U.S. Weather Research Program*

Dabberdt¹,

Carroll²,

Baumgardner³

et al. 2004

Bulletin of the American Meteorological Society

110

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The U.S. Weather Research Program convenes expert working groups on a one-time basis to identify critical research needs in various problem areas. The most recent expert working group was charged to “identify and delineate critical meteorological research issues related to the prediction of air quality.” In this context, “prediction” is denoted as “forecasting” and includes the depiction and communication of the present chemical state of the atmosphere, extrapolation or nowcasting, and numerical prediction and chemical evolution on time scales up to several days. Emphasis is on the meteorological aspects of air quality.The problem of air quality forecasting is different in many ways from the problem of weather forecasting. The latter typically is focused on prediction of severe, adverse weather conditions, while the meteorology of adverse air quality conditions frequently is associated with benign weather. Boundary layer structure and wind direction are perhaps the two most poorly determined meteorological variables for regional air quality prediction. Meteorological observations are critical to effective air quality prediction, yet meteorological observing systems are designed to support prediction of severe weather, not the subtleties of adverse air quality. Three-dimensional meteorological and chemical observations and advanced data assimilation schemes are essential. In the same way, it is important to develop high-resolution and self-consistent databases for air quality modeling; these databases should include land use, vegetation, terrain elevation, and building morphology information, among others. New work in the area of chemically adaptive grids offers significant promise and should be pursued. The quantification and effective communication of forecast uncertainty are still in their early stages and are very important for decision makers; this also includes the visualization of air quality and meteorological observations and forecasts. Research is also needed to develop effective metrics for the evaluation and verification of air quality forecasts so that users can understand the strengths and weaknesses of various modeling schemes. Last, but not of least importance, is the need to consider the societal impacts of air quality forecasts and the needs that they impose on researchers to develop effective and useful products.

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Section: American Meteorological Societymentioning

confidence: 99%

Meteorological Research Needs for Improved Air Quality Forecasting: Report of the 11th Prospectus Development Team of the U.S. Weather Research Program*

Dabberdt¹,

Carroll²,

Baumgardner³

et al. 2004

Bulletin of the American Meteorological Society

110

View full text Add to dashboard Cite

show abstract

“…Numerous studies on this problem suggest an ensemble approach ( [3,4,11,38]; among others) where statistical analysis and treatment of several dispersion simulations of a common case study is made for estimating the dependency of the dispersion pattern on factors like atmospheric circulation, dispersion methods, source characteristics etc., to determine the most probable dispersion patterns that provide an envelope to the uncertainty. The ensemble contains more information than the results from a unique model configuration and hence can be used to analyse the sensitivity of the system to various input parameters.…”

mentioning

confidence: 99%

“…One of the methods of studying the sensitivity of dispersion models is to use a single atmospheric circulation model and produce different ensemble members using different physics variants for sub-grid scale physical processes [35]. Warner et al [38] used a high-resolution meteorological model with various physics options and initial data fields to provide input meteorological fields for a dispersion model and estimated the dispersion probability at each model grid point. The ensemble dispersion produced with this technique accounts for the dependency of the case study on the atmospheric circulation variability.…”

mentioning

confidence: 99%

Sensitivity of atmospheric dispersion simulations by HYSPLIT to the meteorological predictions from a meso-scale model

et al. 2008

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Mesoscale transport and dispersion of air pollutants from a few major point sources in the Mississippi Gulf coastal region is calculated using a coupled modeling system consisting of the atmospheric dynamical model WRF and the lagrangian particle model HYSPLIT. The sensitivity of the dispersion model results to the meteorological fields is studied by conducting an ensemble of simulations using the WRF model for the same dispersion case. Several parameterization schemes for the physical processes of boundary layer turbulence and land surface temperature/moisture prediction in WRF are used in various combinations to produce different meteorological members which are then used for dispersion simulation. The uncertainty in the simulated concentration probabilities to the meteorological model configurations and the ensemble mean are presented. The parameters used for determining the uncertainties include the wind fields, temperature, area of concentration and the levels of concentration. The results indicate that dispersion model results are influenced by the choices made in respect of the planetary boundary layer and land surface schemes in the mesoscale model to produce the meteorological forecast thereby leading to certain amount of uncertainty in the resultant concentrations. Results show that the specific choices made about the atmospheric model configuration can significantly after the simulated concentrations.

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“…Dabberdt and Miller (2000) considered an ensemble of slightly different meteorological fields and the related surface concentration fields of H 2 SO 4 . In other cases the effects of different physical parameterization schemes of the meteorological model on dispersion were studied in Lagrangian dispersion models, like SCIPUFF and HYSPLIT (Warner et al, 2002;Challa et al, 2008). Several papers revealed the relation between standard ensemble forecasts and uncertainties in dispersion patterns.…”

Section: T Haszpra Et Al: Particle Dispersion In Ensemble Forecastsmentioning

confidence: 99%

Dispersion of aerosol particles in the free atmosphere using ensemble forecasts

Haszpra

Lagzi

Tél

2013

Nonlin. Processes Geophys.

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Abstract. The dispersion of aerosol particle pollutants is studied using 50 members of an ensemble forecast in the example of a hypothetical free atmospheric emission above Fukushima over a period of 2.5 days. Considerable differences are found among the dispersion predictions of the different ensemble members, as well as between the ensemble mean and the deterministic result at the end of the observation period. The variance is found to decrease with the particle size. The geographical area where a threshold concentration is exceeded in at least one ensemble member expands to a 5-10 times larger region than the area from the deterministic forecast, both for air column "concentration" and in the "deposition" field. We demonstrate that the rootmean-square distance of any particle from its own clones in the ensemble members can reach values on the order of one thousand kilometers. Even the centers of mass of the particle cloud of the ensemble members deviate considerably from that obtained by the deterministic forecast. All these indicate that an investigation of the dispersion of aerosol particles in the spirit of ensemble forecast contains useful hints for the improvement of risk assessment.

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Ensemble Simulations with Coupled Atmospheric Dynamic and Dispersion Models: Illustrating Uncertainties in Dosage Simulations

Cited by 40 publications

References 32 publications

Meteorological Research Needs for Improved Air Quality Forecasting: Report of the 11th Prospectus Development Team of the U.S. Weather Research Program*

Meteorological Research Needs for Improved Air Quality Forecasting: Report of the 11th Prospectus Development Team of the U.S. Weather Research Program*

Sensitivity of atmospheric dispersion simulations by HYSPLIT to the meteorological predictions from a meso-scale model

Dispersion of aerosol particles in the free atmosphere using ensemble forecasts

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