Advanced Atmospheric Modeling for Emergency Response

Fast, Jerome D.; O'Steen, B.L.; Addis, R.P.

doi:10.1175/1520-0450(1995)034<0626:aamfer>2.0.co;2

Cited by 31 publications

(11 citation statements)

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“…The initial state of the atmosphere for the current RAMS simulations could be improved by using the local SRS tower data (from eight separate locations), which are available in 15-minute averages. This has already been done on an experimental basis (Fast et al 1995) and would be particularly important for local transport applications.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Statistical comparison of Regional Atmospheric Modelling System forecasts with observations

Buckley

Weber

2004

Meteorological Applications

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Section: Conclusion and Discussionmentioning

confidence: 99%

Statistical comparison of Regional Atmospheric Modelling System forecasts with observations

Buckley

Weber

2004

Meteorological Applications

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“…These procedures, strongly determined by the number of available experimental data (data with a significant spatial-temporal resolution), generally focus on the intercomparison of deviations in maximum values, first-order statistical momentum, frequency distribution of concentration values, etc. (Carvalho et al, 2002;Fast et al, 1995;Uliasz, 1994;CityDelta web-page).…”

Section: Model Validationmentioning

confidence: 99%

“…At present, most simulated dispersion results are generally checked either against measurements of tracer-pollutant surface concentrations, with the dispersion analysis limited to the impact areas (Souto et al, 2001;Martín et al, 2001;Fast, 1995;Fast et al, 1995;Luhar, 2002); or occasionally, against instrumented airplane measurements taken during field campaigns lasting several days (Carroll and Baskett, 1979;Millán et al, 1992). In this latter case, the measurements recorded along the airplane pathway are difficult to compare with simulated concentrations due to the former's high temporal and spatial resolution 1 (Eastman et al, 1995;Carvalho et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

A study of dispersion in complex terrain under winter conditions using high-resolution mesoscale and Lagrangian particle models

Palau

Pérez-Landa

Meliá³

et al. 2006

Atmos. Chem. Phys.

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Abstract.A mesoscale model (MM5), a dispersive Langrangian particle model (FLEXPART), and intensive meteorological and COrrelation SPECtrometer (COSPEC) measurements from a field campaign are used to examine the advection and turbulent diffusion patterns associated with interactions and forcings between topography, synoptic atmospheric flows and thermally-driven circulations. This study describes the atmospheric dispersion of emissions from a power plant with a 343-m tall chimney, situated on very complex terrain in the North-East of Spain, under winter conditions. During the field campaign, the plume was transported with low transversal dispersion and deformed essentially due to the effect of mechanical turbulence. The main surface impacts appeared at long distances from the emission source (more than 30 km). The results show that the coupled models (MM5 and FLEXPART) are able to predict the plume integral advection from the power plant on very complex terrain. Integral advection and turbulent dispersion are derived from the dispersive Lagrangian model output for three consecutive days so that a direct quantitative comparison has been made between the temporal evolution of the predicted threedimensional dispersive conditions and the COSPEC measurements. Comparison between experimental and simulated transversal dispersion shows an index of agreement between 80% and 90%, within distance ranges from 6 to 33 km from the stack. Linked to the orographic features, the simulated plume impacts on the ground more than 30 km away from the stack, because of the lee waves simulated by MM5.

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“…The initial state of the atmosphere for the current RAMS simulations could be improved by using the local SRS tower data, which are available in 15-minute averages. This has already been done on an experimental basis (Fast et al 1995) and would be particularly important for local transport applications.…”

Section: Wsrc-tr-2001-00563 December 2001mentioning

confidence: 99%

Statistical Comparison of Regional Atmospheric Modeling System Forecast Meteorology with Observations

Buckley¹

2002

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Advanced Atmospheric Modeling for Emergency Response

Cited by 31 publications

References 0 publications

Statistical comparison of Regional Atmospheric Modelling System forecasts with observations

Statistical comparison of Regional Atmospheric Modelling System forecasts with observations

A study of dispersion in complex terrain under winter conditions using high-resolution mesoscale and Lagrangian particle models

Statistical Comparison of Regional Atmospheric Modeling System Forecast Meteorology with Observations

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