Plume rise in different atmospheres: a practical scheme and some comparisons with lidar measurements

Erbrink, H.

doi:10.1016/1352-2310(94)00197-s

Cited by 13 publications

(7 citation statements)

References 16 publications

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“…1). Fire smoke plume rise is calculated using formulas by Briggs'; the heat flux from BlueSky and NAM meteorological state variables are used as input (Erbrink 1994). The Brigg's algorithm calculates plume Geosci.…”

Section: Smokementioning

confidence: 99%

Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign

Pan¹,

Kim²,

Lee³

et al. 2017

Preprint

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Abstract. Multiple observation data sets, including Interagency Monitoring of Protected Visual Environments (IMPROVE) network data, Automated Smoke Detection and Tracking Algorithm (ASDTA), Hazard Mapping System (HMS) smoke plume shapefiles and aircraft acetonitrile (CH3CN) measurements from the NOAA Southeast Nexus (SENEX) field campaign are used to evaluate the HMS-BlueSky-SMOKE-CMAQ fire emissions and smoke plume prediction system. A similar configuration is used in the National Air Quality Forecasting Capability (NAQFC). The system was found to capture signatures of most of the observed fire signals. Use of HMS-detected fire hotspots and smoke plume information are valuable for both initiating fire emissions and evaluating model simulations. However, we also found that the current system does not include fire contributions through lateral boundary condition and missed fires that are not associated with visible smoke plumes resulting in significant simulation uncertainties. In this study we focused not only on model evaluation but also on evaluation methods. We discuss how to use observational data correctly to filter out fire signals and synergistic use of multiple data sets together. We also address the limitations of each of the observation data sets and of the evaluation methods.

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Section: Smokementioning

confidence: 99%

Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign

Pan¹,

Kim²,

Lee³

et al. 2017

Preprint

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“…When this ratio exceeds unity, the boundary layer is considered to be sufficiently unstable for premature termination of plume rise to occur. The procedure suggested by Erbink (1994) is then used to calculate the final plume rise:…”

Section: Plume Risementioning

confidence: 99%

An On Line Atmospheric Dispersion Model (OLADMO) for the World Wide Web

Clegg¹

2006

Surv Geophys

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This paper presents an overview of earlier and current methods of modelling atmospheric dispersion, and proposes and evaluates a screening model for operation over the World Wide Web. The On Line Atmospheric Dispersion Model (OLADMO) is a quasi boundary layer parameterised Gaussian plume model with additional algorithms to account for plume rise, building wake effects and deposition processes. The Monin-Obukhov length boundary layer parameter is utilised to define six stability classes in order to determine atmospheric turbulence and stability, whilst new equations, derived from an intercomparison study of old and next generation dispersion models, are used to calculate the horizontal and vertical dispersion coefficients r y and r z . Using data from two field experiments in Copenhagen, Denmark and Lillestrøm, Norway, the model results from OLADMO are found to compare favourably with the results from several old and next generation dispersion models. As a consequence of the unique nature of the meteorological and location factors of the Lillestrøm experiment, all models struggled to represent the concentrations observed during the field study adequately. However, OLADMO was the best performing model in this case, with a mean normalised crosswind integrated concentration 13% closer to the mean observed concentration than its nearest competitor. Because the evaluation of the model was conducted with a limited dataset, several limitations and improvements to both the model and experimental procedure are suggested.

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“…8.84 and 8.85). This new algorithm is similar to other layer-by-layer approaches available in CMAQ (Byun and Ching, 1999), based on the hesitantplume algorithm described in Turner et al (1991) and in dispersion modelling work by Erbrink (1994). In the new algorithm (hereafter referred to as the revised Briggs plume rise or simply plume rise) we utilized the model's calculated vertical profile of atmospheric temperature and wind speed to estimate the plume height as the height at which the emitted plume buoyancy flux dissipates totally.…”

Section: Plume-rise Algorithms: Two Alternative Approachesmentioning

confidence: 99%

A chemical transport model study of plume-rise and particle size distribution for the Athabasca oil sands

et al. 2018

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Abstract. We evaluate four high-resolution model simulations of pollutant emissions, chemical transformation, and downwind transport for the Athabasca oil sands using the Global Environmental Multiscale -Modelling Air-quality and Chemistry (GEM-MACH) model, and compare model results with surface monitoring network and aircraft observations of multiple pollutants, for simulations spanning a time period corresponding to an aircraft measurement campaign in the summer of 2013. We have focussed here on the impact of different representations of the model's aerosol size distribution and plume-rise parameterization on model results.The use of a more finely resolved representation of the aerosol size distribution was found to have a significant impact on model performance, reducing the magnitude of the original surface PM 2.5 negative biases 32 %, from −2.62 to −1.72 µg m −3 .We compared model predictions of SO 2 , NO 2 , and speciated particulate matter concentrations from simulations employing the commonly used Briggs (1984) plume-rise algorithms to redistribute emissions from large stacks, with stack plume observations. As in our companion paper (Gordon et al., 2017), we found that Briggs algorithms based on estimates of atmospheric stability at the stack height resulted in under-predictions of plume rise, with 116 out of 176 test cases falling below the model : observation 1 : 2 line, 59 cases falling within a factor of 2 of the observed plume heights, and an average model plume height of 289 m compared to an average observed plume height of 822 m. We used a highresolution meteorological model to confirm the presence of significant horizontal heterogeneity in the local meteorological conditions driving plume rise. Using these simulated meteorological conditions at the stack locations, we found that a layered buoyancy approach for estimating plume rise in stable to neutral atmospheres, coupled with the assumption of free rise in convectively unstable atmospheres, resulted in much better model performance relative to observations (124 out of 176 cases falling within a factor of 2 of the observed plume height, with 69 of these cases above and 55 of these cases below the 1 : 1 line and within a factor of 2 of observed values). This is in contrast to our companion paper, wherein this layered approach (driven by meteorological observations not co-located with the stacks) showed a relatively modest impact on predicted plume heights. Persistent issues with over-fumigation of plumes in the model were linked to a more rapid decrease in simulated temperature with increasing height than was observed. This in turn may have led to overestimates of near-surface diffusivity, resulting in excessive fumigation.

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Plume rise in different atmospheres: a practical scheme and some comparisons with lidar measurements

Cited by 13 publications

References 16 publications

Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign

Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign

An On Line Atmospheric Dispersion Model (OLADMO) for the World Wide Web

A chemical transport model study of plume-rise and particle size distribution for the Athabasca oil sands

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