An intercomparison between three different models is presented. The simulated case is a tracer experiment performed in complex terrain. Two dispersion models are initialised with a meteorological model that can use as input the ECMWF analysis only or both these analysis and local measurements. The results demonstrate that the best performances are obtained by using the dispersion models

coupled with a meteorological model. Moreover the Lagrangian model seems to slightly better perform when the local measurements are accounted for

Alessandrini, S.

Ferrero, E.

Orlandi, E.

Pertot, C.

English

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DOI 10.1393/ncc/i2005-10187-0IL NUOVO CIMENTO Vol. 28 C, N. 2 Marzo-Aprile 2005Comparison of different dispersion models withtracer experiment(∗)S. Alessandrini(1), E. Ferrero(2), C. Pertot(1) and E. Orlandi(3)(1) CESI - Milano, Italy(2) Dipartimento di Scienze e Tecnologie Avanzate, Universita` del Piemonte OrientaleAlessandria, Italy(3) Dipartimento di Fisica, Universita` di Milano - Milano, Italy(ricevuto il 7 Febbraio 2005; approvato il 6 Giugno 2005)Summary. — An intercomparison between three different models is presented.The simulated case is a tracer experiment performed in complex terrain. Two dis-persion models are initialised with a meteorological model that can use as input theECMWF analysis only or both these analysis and local measurements. The resultsdemonstrate that the best performances are obtained by using the dispersion mod-els coupled with a meteorological model. Moreover the Lagrangian model seems toslightly better perform when the local measurements are accounted for.PACS 92.10.Lq – Turbulence and diffusion.PACS 01.30.Cc – Conference proceedings.1. – IntroductionThe aim of this work is to assess the ability of different models in simulating pol-lutant dispersion in real complex terrain. The comparison between Gaussian, Puff andLagrangian Particle models shows how they reproduce the measured ground level con-centrations with the aim to select the best model for such a situation.It is also a purpose of the paper to discuss the improvements due to the use of ameteorological circulation model to provide the flow and turbulence fields needed to thedispersion models. In fact the meteorological model should be able to compute bothmean wind and turbulent fields on a three-dimensional domain and the dispersion modelaccounting for such a detailed input should correctly predict the concentrations of theconsidered pollutant.(∗) Paper presented at CAPI 2004, 8◦ Workshop sul calcolo ad alte prestazioni in Italia, Milan,November 24-25, 2004.c© Societa` Italiana di Fisica 141142 S. ALESSANDRINI, E. FERRERO, C. PERTOT and E. ORLANDIFor this reason the comparison of the models performances with tracer experimentresults and the inter-comparison between different models is of great importance.Some simpler models can derive the input parameters simply from measurementsmade at or in the proximity of the pollutant source. Others, generally more sophisticated,need to be coupled with a circulation model reproducing the meteorological conditions ona large domain around the source point. The latter, obviously, require more computingresources but, in general, give better results. In this work we compare the results of threedifferent models tested against tracer data gathered during an experiment, which consti-tutes a severe test due to the complex topographical and meteorological conditions. Thecomparison is performed by means of a model evaluation procedure based on statisticalindexes calculated on the cross wind integrated concentrations.In sect. 2 the models are presented, in sect. 3 the tracer experiments is described, insect. 4 the simulations details are given and the results are shown, while sect. 5 containsthe conclusions.2. – Models descriptionThe first modelling system consists in a coupled model based on the regional at-mospheric model RAMS and the Lagrangian particle model SPRAY. RAMS (RegionalAtmospheric Modeling System) is a well-known prognostic model, developed at theColorado State University [1]. The coupling between the two models is made usingan interface code, MIRS 2.0 [2], which receives RAMS outputs and calculates bound-ary layer and turbulence parameters for the Lagrangian dispersion model SPRAY, inparticular the variables not directly provided by RAMS.We performed meteorological simulations using the RAMS version 4.4 initialised withthe ECMWF analysis and the measurements carried out during the tracer experiment.The turbulence model used is the Mellor and Yamada closure model [3], which providesthe turbulence kinetic energy (E) to MIRS that in turn calculates the wind velocitystandard deviations, and the Lagrangian Time Scales needed to the dispersion model asinput by means of the following procedure.The variances of the velocity fluctuation σ2u,v,w are computed asσ2u = (1− 2γ)q2 , σ2v = γq2 , σ2w = γq2 ,where q2 = 2E, γ ≡ 1/3 − 2(A1/B1) and A1, B1 are the constants which define two ofthe characteristic length scales introduced in the Mellor and Yamada model [3].Then the Lagrangian time scales TL are obtained fromTLx,y,z =Kmx,y,zσ2u,v,w,where Kmx,y,z are the anisotropic diffusivities; the vertical component is calculated onthe basis of the E equation from the Mellor and Yamada closure while the horizontalcomponents are assigned from a deformational Smagorinsky-type scheme, based on thedeformation strain tensor and the horizontal grid spacing.Besides these quantities MIRS also provides to SPRAY the topography and the bound-ary layer height.SPRAY [4] is a Lagrangian stochastic particle model designed to study the pollutantsdispersion in complex terrain. It is based on the Langevin equation for the turbulentCOMPARISON OF DIFFERENT DISPERSION MODELS WITH TRACER EXPERIMENT 143velocities [5], whose coefficients depend on a solution of the Fokker-Planck equation fora given Eulerian probability density function (PDF) of the turbulent velocity and onthe inertial range turbulence theory, respectively. In the two horizontal directions thePDF is assumed to be Gaussian. In the vertical direction the PDF is assumed to benon-Gaussian, so to deal with convective conditions.The vertical dispersion calculation is based on the generalised Langevin equation [5].The equations prescribing the evolution of the vertical velocity fluctuation w and thedisplacement z are the following:dw = a(z, w)dt+√C0εdtdµ ,dz = wdt ,where dµ has zero mean and unit variance, C0 is a constant and ε is the dissipation rateof the turbulent kinetic energy. a(z, w) must be determined by solving the Fokker-Planckequation, obtaininga (z, w) =1P(B0∂P∂w+Φ),where B0 = (1/2)C0ε and, as suggested by Thomson [5],Φ = − ∂∂zw∫−∞wP (z, w) dw .P (z, w) is the PDF that must be prescribed from the available measurements orparameterisations. In the present work, we have used the Gram-Charlier PDF [6]. Themodel makes use of the Gaussian assumption in the horizontal directions. The assumedsteady conditions (as in the case of the other models considered) are not a limitation inthis kind of applications, which are performed at the small scales where the meteorologicalconditions can be considered the same during the dispersion process.The second modelling system used is the CALMET-CALPUFF chain. The wind fieldproduced by RAMS 4.4 is processed by CALMET that also computes the turbulencevariables necessary for CALPUFF. CALPUFF [7] is a multi-layer, multi-species non-steady-state puff dispersion model, which can simulate the effects of time- and space-varying meteorological conditions on pollutant transport, transformation, and removal,developed and distributed by Earth Tech, Inc.The model has been adopted by the U.S. Environmental Protection Agency (U.S.EPA) in its Guideline on Air Quality Models as the preferred model for assessing long-range transport of pollutants and their impacts on Federal Class-I areas and on a case-by-case basis for certain near-field applications involving complex meteorological conditions.The modelling system consists mainly of CALMET (a diagnostic three-dimensionalmeteorological model) and CALPUFF (an air quality dispersion model). Advection iscarried out by a three-dimensional wind field and the dispersion coefficients are computedfrom internally calculated wind standard deviations using CALMET micrometeorologicalvariables (u∗, w∗, L, etc.)144 S. ALESSANDRINI, E. FERRERO, C. PERTOT and E. ORLANDIThe third model used in this work is ISC3 (Industrial Source Complex Model) [8],a steady-state Gaussian plume model which can be used to assess pollutant concentra-tions from a wide variety of sources associated with an industrial complex. This modelaccounted in our simulations for plume rise as a function of downwind distance and wasdriven by the hourly meteorological data measured by the tower near the stack.3. – Bull Run data setThe models have been applied to the Bull Run experiment [9, 10] and the resultscompared. The Bull Run steam plant is located in the broad Tennessee River valley(about 60 km wide) surrounded by mountains rising over 1700 m of altitude. Close tothe plant, the ridges are approximately 100 m and the region is covered by forests. TheMelton Hill Lake cuts perpendicular across the ridges near the Bull Run steam plant.The meteorological measurements were collected from a 122 m TVA tower, locatednear the crest of a 70 m ridge about 2 km west of the stack, the central observing station,located at about the same elevation as the stack base in a field near the river about 5 kmnorthwest of the stack, one acoustic sounder about 1 km east of the stack, the NationalWeather Service (NWS) stations at Knoxville and Nashville, Tennessee.The tracer (FS6) was released from the 243 m high plant stack and measured on 200monitors deployed on arcs from 0.5 to 50 km, during two 5-week periods. The locationsof the monitors were changed following the forecasted meteorological conditions in orderto catch the wind direction and depending on the forecasted maximum ground levelconcentration. In particular during the day chosen for the simulations (5 October 1982)187 receptors were located on 4 circles of radius 0.5, 2, 5, 10 km around the sourceposition. The tracer was released for 12 hours starting at 9 LST (Local Standard Time)and the hourly measurements have been performed for 9 hours since 11 LST. In fig. 1the topography, the receptors and the source position are shown. October 5th, 1982 wasa sunny day with low winds with very variable directions as shown in fig. 2.4. – Simulations and resultsRAMS was run using three horizontal nested grids corresponding to domains of ex-tension 1248 × 1248 km2, 328 × 328 km2 and 82 × 82 km2, with horizontal resolutionequal to 32000 m, 8000 m, 2000 m, respectively. In the vertical direction a stretched gridof 32 levels up to 18000 m, with a minimum grid size equal to 50 m and a maximum of1200 m, was considered. The integration time steps for the three grids were 50 s, 16 s,and 8 s, respectively.Two RAMS simulations were carried out for a period of 24 hours from 5 October00 UTC until 6 October 00 UTC. During the first simulation the initial and boundaryconditions were provided only by the ECMWF analysis data, while, during the secondsimulation, the necessary meteorological fields were obtained by the assimilation of theECMWF data with the measurements performed near the source during the experiment.A nudging procedure was applied in both cases. In figs. 3 a and b are shown the horizontalcross-sections at 250 m of wind speed computed by the two RAMS simulations at 15LST. It is possible to see that the up-slope thermal winds in the NW and SE parts ofthe domain are well reproduced even if the assimilation of the measured data reducesthe intensity of this phenomenon slowing down the wind speed.The results of the model evaluation are presented in table I, for the crosswind-integrated concentrations. It may be useful to stress that the simulations were performedCOMPARISON OF DIFFERENT DISPERSION MODELS WITH TRACER EXPERIMENT 145Fig. 1. – Topography, receptors positions on 4 arcs and source position (centre of the figure).in low-wind conditions and, in this case, it is very difficult for the usual dispersion modelsto reproduce all the characteristics of the concentrations distribution. Moreover the sin-gle observation cannot be significant for the risk assessment. For this reason we assumethat a suitable model should catch at least the cross-wind-integrated concentrations, al-though the single observations could not be perfectly predicted. The comparison betweenthe three models, RAMS SPRAY, RAMS CALPUFF and ISC3, is performed by usingFig. 2. – Wind direction (crosses) and wind speed (line) measured at 100 m height by themeteorological tower during 5 October 1982.146 S. ALESSANDRINI, E. FERRERO, C. PERTOT and E. ORLANDIFig. 3. – Horizontal cross-section at 250 m of the wind speed computed by RAMS at 15 LSTwithout (a) and with (b) the assimilation of the measured data.COMPARISON OF DIFFERENT DISPERSION MODELS WITH TRACER EXPERIMENT 147Table I. – Crosswind-integrated concentrations.MODEL MEAN SIGMA BIAS NMSE COR FA2 FB4575. 4436. 0.00 0.00 1.0 1.00 0.00RAMS SPRAY 5347. 3247. −772. 0.54 0.6 0.56 −0.16RAMS CALPUFF 5057. 3139. −482. 0.93 0.3 0.36 −0.10ISC 406. 688. 4168. 18.16 0.6 0.00 1.67RAMS SPRAY2 6371. 4487. −1797. 0.44 0.8 0.53 −0.33RAMS CALPUFF2 3543. 3010. 1031. 1.32 0.3 0.28 0.25the statistical indexes [11]: mean, standard deviation (SIGMA), bias, normalised meansquare error (NMSE), correlation coefficient (COR), factor of two (FA2) and fractionalbias (FB). The first line indicates the expected value for a “perfect model” (mean andstandard deviation are calculated from the measured data). The last two simulationsrefer to the second runs performed using RAMS SPRAY and RAMS CALPUFF by ini-tialising RAMS both with the ECMWF analysis fields and the measurements providedin the Bull Run data set.The results displayed in table I show that the best performances are obtained by usingthe RAMS SPRAY model. The NMSE, correlation and factor two are better than thoseobtained with the other models while FB indicates an overestimation.The second simulation performed with RAMS SPRAY improves the NMSE and thecorrelation, while FB is worse than in the previous simulation. In the case ofRAMS CALPUFF model, the new RAMS simulation does not improve the results. Thedifference in the performances of the two models is due to the fact that SPRAY uses boththe wind and turbulence fields produced by RAMS, while CALPUFF takes into accountthe wind field only.Figures 4 a,b,c,d,e show the scatter plot for the crosswind-integrated concentrations ofthe five simulations. It can be observed (fig. 4a) that the RAMS SPRAY first simulationgives rather good results except for some cases, which show either overestimation for smallconcentrations or underestimation of the higher values. Less satisfactory are the resultsof CALPUFF (fig. 4b), which exhibit a large spread of the scatter plot data. Totallyunsatisfactory are the results of ISC3 (fig. 4c), which show a large number of cases withzero concentration. This fact confirms, as expected, the inability of the Gaussian modelin reproducing the dispersion in low-wind conditions.The better results obtained in the second simulation of RAMS SPRAY model are pre-sented in fig. 4d. It can be observed that the cases of large over and underestimation arereduced. The performance of the second simulation carried out with RAMS CALPUFFmodel (fig. 4e) seems to be similar to the first one or slightly worse.5. – ConclusionsThe comparison between the performances of three different models simulating atracer experiment in complex terrain is presented. Two models, a Lagrangian particlemodel (SPRAY) and a puff model (CALPUFF), use the output of the meteorologicalmodel RAMS, while the third one is a Gaussian model using directly the measurementscarried out in the experimental site. Second simulations of SPRAY and CALPUFF areperformed using the RAMS outputs obtained initialising the meteorological model beside148 S. ALESSANDRINI, E. FERRERO, C. PERTOT and E. ORLANDIFig. 4. – Scatter plot of crosswind-integrated concentrations (computed y-axis, measured x-axis)for the five simulations RAMS-SPRAY (a), RAMS-CALPUFF (b), ISC (c), RAMS-SPRAY2 (d),RAMS-CALPUFF2 (e).COMPARISON OF DIFFERENT DISPERSION MODELS WITH TRACER EXPERIMENT 149the ECMWF data, also taking into account the experimental measurements. The bestresults are obtained by SPRAY and CALPUFF and the inclusion of the meteorologicalmeasurements in RAMS slightly improves the SPRAY performance.These results demonstrate that a great improvement in the model performances isobtained by driving the dispersion model with a meteorological model providing thethree-dimensional fields of flow and turbulence, even if there is a price to pay in termsof time and computer resources. However, considering the computing facilities nowadaysavailable and their future developments, these results suggest to follow this approach.Also using a stochastic model instead of a simpler puff model gives better results, whilethe assimilation of local measurements to initialise the meteorological model does notproduce sensible changes.∗ ∗ ∗CESI contribution to this paper has been supported by the MICA (Italian Ministryof Industry, Trade and Handicraft) in the frame of Energy Research Program for theItalian Electric System (MICA Decree of February 28, 2003), Project on SCENARI. Theauthors are grateful to S. R. Hanna and H. R. Olesen for providing the Bull Rundata-set.REFERENCES[1] Pielke R. A., Cotton W. R., Walko R. L., Tremback C. J., Lyons W. A., GrassoL. D., Nicholls M. E., Moran M. D., Wesley D. A., Lee T. J. and Copeland J.H., Meteor. Atmos. Phys., 49 (1992) 69.[2] Trini Castelli S., Rap. Int. Rep. ICGF/CNR No 412/2000 (2000).[3] Mellor G. L. and Yamada T., Rev. Geophys. Space Phys., 20 (1982) 851.[4] Tinarelli G., Anfossi D., Bider M., Ferrero E. andTrini Castelli S., Air PollutionModelling and its Applications XIII, edited by Gryning S. E. and Batchvarova E.(Plenum Press, New York) 2000, p. 23.[5] Thomson D. J., J. Fluid Mech., 180 (1987) 529.[6] Ferrero E. and Anfossi D., Int. J. Environ. Pollut., 9 (1998) 384.[7] Scire J. S. et al., Earth Tech Inc., Concorde, Massachusetts, USA (2000).[8] Londergan R. J., Minott D. H., Wackter D. J. and Bonitta D., EPA-450/4-83-003,U.S. Environmental Protection Agency, Research Triangle Park, NC, October 1982.[9] Hanna S. R. and Paine R. J., J. Appl. Meteorol., 28 (1989) 206.[10] Brown N. E., Londergan R. L. and Murray D. R., EA-3755, EPRI, Palo Alto, CA94304 (1985).[11] Hanna S. R., Strimaitis D. G. and Chang J. C., Hazard Response Modeling Uncertainty(a quantitative method), Vol. I: Users Guide for Software for Evaluating Hazardous GasDispersion Models. Sigma Research Corporation, Westford, Ma (1991).

Comparison of different dispersion models with tracer experiment

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Comparison of different dispersion models with tracer experiment

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