TRANSALP 1989 experimental campaign-I. Simulation of 3D flow with diagnostic wind field models

Desiato, F.; Finardi, S.; Basso, Giuseppe; Morselli, M. G.

doi:10.1016/s1352-2310(97)00196-9

Cited by 31 publications

(14 citation statements)

References 15 publications

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“…Generally speaking, the more observations the better, since as noted earlier, any local phenomena not reflected in the interpolated field will likely not be present in the adjusted field. However, Desiato, et al (1998) caution that more observations do not necessarily improve the accuracy of the results. They argue that the input data should only reflect phenomena of a scale that is resolvable by the grid being used.…”

Section: Discussionmentioning

confidence: 94%

“…Its predictions were comparable to those obtained from application of the RAMS prognostic model. Desiato, et al (1998) applied both MINERVE and CONDOR to reconstruct the 3D wind fields during the TRANSALP-89 meteorological and tracer experiment conducted in the Swiss Alps. Though each used a different numerical solution technique, given the same input data and values for α, both models produced approximately the same solution, thus providing a cross-check on their validity.…”

Section: Discussionmentioning

confidence: 99%

“…This is viewed as a favorable indicator of both its accuracy, and its ease of use. The list of organizations includes: the Italian Electricity Board (ENEL) , Desiato, et al (1998), Morselli, et al (1997)); the Swiss Radio Protection Board; Science Applications International Corporation (SAIC) (Sontowski, et al (1995), Sontowski and Dougherty (1996), Cox, et al (1998)); the Defense Threat Reduction Agency (DTRA) and its predecessors DNA and DSWA (Bradley, et al (1997)); Software Solutions and Environmental Services Company (SSESCO) (cf. http:/ /www.ssesco.com/RITE.html); and ARIA Technologies (France) (cf.…”

Section: Discussionmentioning

confidence: 99%

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Three-Dimensional Wind Field Modeling: A Review

Homicz

2002

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Over the past several decades, the development of computer models to predict the atmospheric transport of hazardous material across a local (on the order of 10s of km) to mesoscale (on the order of 100s of km) region has received considerable attention, for both regulatory purposes, and to guide emergency response teams. Wind inputs to these models cover a spectrum of sophistication and required resources. At one end is the interpolation/ extrapolation of available observations, which can be done rapidly, but at the risk of missing important local phenomena. Such a model can also only describe the wind at the time the observations were made. At the other end are sophisticated numerical solutions based on socalled Primitive Equation models. These prognostic models, so-called because in principle they can forecast future conditions, contain the most physics, but can easily consume tens of hours, if not days, of computer time. They may also require orders of magnitude more effort to set up, as both boundary and initial conditions on all the relevant variables must be supplied. The subject of this report is two classes of models intermediate in sophistication between the interpolated and prognostic ends of the spectrum. The first, known as mass-consistent (sometimes referred to as diagnostic) models, attempt to strike a compromise between simple interpolation and the complexity of the Primitive Equation models by satisfying only the conservation of mass (continuity) equation. The second class considered here consists of the so-called linear models, which purport to satisfy both mass and momentum balances. A review of the published literature on these models over the past few decades was performed. Though diagnostic models use a variety of approaches, they tend to fall into a relatively few welldefined categories. Linear models, on the other hand, follow a more uniform methodology, though they differ in detail. The discussion considers the theoretical underpinnings of each category of the diagnostic models, and the linear models, in order to assess the advantages and disadvantages of each. It is concluded that diagnostic models are the better suited of the two for predicting the atmospheric dispersion of hazardous materials in emergency response scenarios, as the linear models are only able to accommodate gently-sloping terrain, and are predicated on several simplifying approximations which can be difficult to justify a priori. Of the various approaches used in diagnostic modeling, that based on the calculus of variations appears to be the most objective, in that it introduces the fewest number of arbitrary parameters. The strengths and weaknesses of models in this category, as they relate to the activities of Sandia's Nuclear Emergency Support Team (NEST), are further highlighted.

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Three-Dimensional Wind Field Modeling: A Review

Homicz

2002

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“…For example, evaluation of CALMET at the test-site in Nevada (1-km grid spacing) provided a mean velocity RMSE = 1.7 m/s (COX et al 2005); the application of the same model (and resolution) in long-term simulations in the south-central region of Chile provided RMSE-1.03 m/s (MORALES et al 2012). On the other hand, short-term simulations in the Alpine region, using CONDOR and MINERVE models (500-m grid spacing) provided a mean RMSE of 0.68 and 0.65 m/s, respectively (DESIATO et al 1998). Experimental evaluation of Meteodyn WT and WindMap models, conducted on four different test sites and using relatively high spatial resolution (50-m grid spacing) resulted in a RMSE ranging from 0.75 to 1.02 m/s (BEAUCAGE and BROWER 2012).…”

Section: Introductionmentioning

confidence: 89%

Remote Sensing Data in Wind Velocity Field Modelling: a Case Study from the Sudetes (SW Poland)

Jancewicz

2013

Pure Appl. Geophys.

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Abstract-The phenomena of wind-field deformation above complex (mountainous) terrain is a popular subject of research related to numerical modelling using GIS techniques. This type of modelling requires, as input data, information on terrain roughness and a digital terrain/elevation model. This information may be provided by remote sensing data. Consequently, its accuracy and spatial resolution may affect the results of modelling. This paper represents an attempt to conduct wind-field modelling in the area of the Ś nie_ znik Massif (Eastern Sudetes). The modelling process was conducted in WindStation 2.0.10 software (using the computable fluid dynamics solver Canyon). Two different elevation models were used: the Global Land Survey Digital Elevation Model (GLS DEM) and Digital Terrain Elevation Data (DTED) Level 2. The terrain roughness raster was generated on the basis of Corine Land Cover 2006 (CLC 2006) data. The output data were post-processed in ArcInfo 9.3.1 software to achieve a high-quality cartographic presentation. Experimental modelling was conducted for situations from 26 November 2011, 25 May 2012, and 26 May 2012, based on a limited number of field measurements and using parameters of the atmosphere boundary layer derived from the aerological surveys provided by the closest meteorological stations. The model was run in a 100-m and 250-m spatial resolution. In order to verify the model's performance, leave-one-out cross-validation was used. The calculated indices allowed for a comparison with results of former studies pertaining to WindStation's performance. The experiment demonstrated very subtle differences between results in using DTED or GLS DEM elevation data. Additionally, CLC 2006 roughness data provided more noticeable improvements in the model's performance, but only in the resolution corresponding to the original roughness data. The best input data configuration resulted in the following mean values of error measure: root mean squared error of velocity = 1.0 m/s and mean absolute error of direction = 30°. The author concludes that, within specific meteorological conditions (relatively strong and constant synoptic forcing) and using the aforementioned input data, the Canyon model provides fairly acceptable results. Similarly, the quality of the presented remote sensing data is suitable for wind velocity modelling in the proposed resolution. However, CLC 2006 land use data should be first verified with a higher-resolution satellite or aerial imagery.

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“…The simulation is performed by the software package ARIA INDUSTRY that includes: the dispersion model SPRAY, the diagnostic meteorological model MI-NERVE (Geai 1987;Desiato et al 1998;Finardi et al 1998;Cox et al 1998;Cox et al 2005) and the turbulence model SURFPRO. SPRAY Tinarelli et al 2000) is a Lagrangian stochastic model for the simulation of the dispersion of passive pollutants in complex terrain under non-homogenous conditions.…”

Section: Modelling Of the Plume Dispersion In Atmospherementioning

confidence: 99%

Tri-generation power plant and conventional boilers: pollutant flow rate and atmospheric impact of stack emissions

Ghermandi

Teggi

Fabbi

et al. 2014

Int. J. Environ. Sci. Technol.

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The atmospheric impact of stack emissions from a power plant (tri-generator and boilers) that will be installed in an urban area in the central Po valley (Northern Italy), characterized by calm wind events, is studied and compared with the impact of the existing plant (conventional boilers). Both the plants are supplied by methane gas. The atmospheric dispersion of NO x emitted is simulated, both in the current and future scenario, by the software package ARIA INDUSTRY. The NO x emission rates are set equal to the regulatory emission limits for existing and future boilers, while the tri-generation system emission rates are set equal to the emission limits certified by the system manufacturer. The simulation periods focus over the 2010 winter season. The simulation estimates the impact of NO x emissions on air quality (vertical concentration profiles and concentration maps at the ground) in the urban area close to the plant. The future power plant impact on air quality results lower than the impact of the existing plant, even if the yearly total mass of pollutants emitted in atmosphere from the new power plant is higher than from the existing plant. The emissions of conventional boilers result the main responsible of the air pollution at the ground in the future scenario.

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TRANSALP 1989 experimental campaign-I. Simulation of 3D flow with diagnostic wind field models

Cited by 31 publications

References 15 publications

Three-Dimensional Wind Field Modeling: A Review

Three-Dimensional Wind Field Modeling: A Review

Remote Sensing Data in Wind Velocity Field Modelling: a Case Study from the Sudetes (SW Poland)

Tri-generation power plant and conventional boilers: pollutant flow rate and atmospheric impact of stack emissions

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