Media: improvement of the source term treatment F. Bompay, L. Borrel Mefeo France, 6'CEM/06V16', Tb^/o^^e, France

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1 Media: improvement of the source term treatment F. Bompay, L. Borrel Mefeo France, 6'CEM/06V16', Tb^/o^^e, France ABSTRACT MEDIA is an Eulerian three dimensional model of atmospheric dispersion, used at the present time by METEO-FRANCE to forecast the behaviour at middle and large scale, of an atmospheric release of pollutant. In this model, the source is described on the horizontal plan by a Gaussian distribution between the point of release and the 12 closest grid points.this scheme does not take into account the wind. In this paper we shall study six possible modifications of the source-mesh, in order to reduce the present drawbacks. the first four will consist in reducing the number of points that modelize the source so that computing by MEDIA (which takes account of the meteorological fields) will step in as soon as possible. In the last two ones we shall take into account the wind in order to reduce the concentration of pollutant upstream from the source, in a way which corresponds as much as possible.to the reality. 1-INTRODUCTION: MEDIA is an Eulerian three dimensional model of atmospheric dispersion, which was developped in METEO-FRANCE untill 1990 (JP Piedelievre and al,1990) to improve the forecasting of the behaviour of plums, particularly in case of atmospheric release of pollutant. Actually the Trajectory model used in the French Weather Service until 1990 was quite efficient to locate the source of the accident, but drawing up the map of the concentrations needed several trajectories and so a long time of computation even when a rapid decision was necessary. MEDIA uses fields (of wind, temperature...) computed by an operational model : it takes advantage of improvements in numerical forecasting. The source is described by a Gaussian distribution on the first 12 points around the release, and does not take the wind or any other meteorological field into account : points located upstream from the wind are contaminated the same way as if they were downstream. In these conditions, MEDIA was tested during the ATMES experiment, (W Klug et al, 1992) and gave good results. Nevertheless it was interesting to see if a change in the source mesh numerical treatment could lead to actual better results. We decided to value the modifications with the hypothesis of ATMES experiment for the source release. In this paper we shall first recall the basic equations and hypothesis of the MEDIA model. In a second part we shall present the different experiments : - in the four first ones, we shall reduce the number of points of the source so that the wind is taken into account by MEDIA as soon as possible.

2 114 Computer Simulation - in the two last experiments we shall use the wind field since the first points to reduce the concentration of pollutant upstream from the source. We shall then present the results from these experiments in the third part. 2 EQUATIONS REMINDER: The concentration of a passive chemical element in the atmosphere is described by the laws of a continuous medium, especially mass conservation : C is the pollutant concentration at a given node in time t ; V is the wind vector predicted by a numerical weather model ; K the REYNOLDS tensor ; S, the source terms ; Si the sink term. The pollutant concentration is advected by the wind field as predicted by the operational coupling model. In order to simplify the procedure and to be coherent with the coupling model, the diffusion is modeled using exchange coefficients. This choice means that the pollutant is assumed to be diffused in the same way as the water vapor. Source term description : In the initial code, the concentration of each of the 12 points located around the source as described on the following figure is given by a Gaussian distribution : r -</ Where: d is the distance from the source; Q the pollutant flux (UCs-0; H the vertical thickness of the pollutant cloud (m), and <jn the surface of the mesh which includes the source This method does not include the effect of the wind close to the source ; on the other hand, the loss of mass is quite small : the sum of the 12 values of concentration reaches 96 % of the emission. Coordinates system : We chose the a coordinate (a- P I P^) on a latitude longitude grid in accordance with the evolution of the data bases of the numerical weather forecasting models we are using (Model from the European Center for Medium Weather Forecasts -ECMWF -in that case).

3 The equation for the concentration evolution can then be written : Computer Simulation 115 i a Where: (/) is the latitude; A the longitde; R the earth's radius; TV the virtual temperature in Kelvin; a = da/dt and Ra = the dry air constant. 3 MODIFICATIONS OF THE SOURCE TREATMENT STUDIED : At the present time the model does not include the effect of the wind for the first 12 points around the source and nodes located upstream from the source are contaminated the same way as if they were downstream. The first 4 experiments will consist in reducing this number of points so that it will be taken account of the wind, by MEDIA as soon as possible. A - First experiment: The Gaussian distribution is only applied to the first four grid points around the source (c.f the following diagram); this method induces a 32 % loss of mass which is corrected by the addition of the following term : 0.32 x Q x CTn where : Q is the pollutant flux (UCs-i); H is the vertical thickness of the pollutant cloud (m), and On is the surface of the mesh which includes the source..ai is a value of surface as described by the following diagram. a-4 c/3 So the concentration on point i gets :

4 116 Computer Simulation where :d is the distance from the source; B - Second experiment : We consider that the whole mass of pollutant is concentrated on the grid point which is the closest from the source. C - Third experiment : We consider that every of the first four nodes around the source receives the concentration : c/c D - Fourth experiment : The first mesh around the source is devided in 4 parts as shown by the figure. The concentration on every point is then computed as following : dt exp... _ +0,32 x()x 2cTn} ~ otr where : a'l a'3 In the last two experiments the wind is taken into account in order to reduce the concentration of pollutant downstream of the source, in a way which corresponds, as much as possible to the reality.

5 E - Fifth experiment : Computer Simulation 117 We consider the 12 points around the source, the concentration of which is first calculated by a Gaussian distribution as in the initial version of the model. We then apply a symmetry about the plan which is orthogonal to the wind and includes the source : the upstream from the source has its concentration transfered on the downstream part of the field. This quantity of pollutant is there distributed among the different points according to their distance from the source. F - Sixth experiment : We consider the grid point which is the closest from the source.its coordinates are (Is,Js). We then discretize each cell of the field (Is-2,Is+2) ; (Js-2,Js+2) in 8 elements on each axis, which leads to a (33,33,15) networking (15 is the number of levels on the vertical axis). The concentration of all the points of the domain is computed with MEDIA and the assumption that the whole mass is located on the grid point which is the nearest to the source, in the new network (this point can be different from the one we called (Is,Js) at the beginning ). The fields used at this level are the outputs of the meteorological model, interpolated on the (33,33,15) domain. The concentration of the 9 points belonging to the area (is-l,is+l) /(Js-l,Js+l) is then computed as the average value of the points located in the four adjacent meshes. MEDIA is at that time started from these 9 points of grid, with the usual mesh size.

6 118 Computer Simulation 4 RESULTS We compared daily air concentrations predicted by MEDIA with measurements collected from the REM data bank (F. Raes et al, 1990). Participants in the final ATMES workshop were provided with these data. For each of the six different simulations, we computed : Mean value and variance of the sample Bias defined as B = ]T (C(i)-O(i)) where C(i) is the predicted value and 0(i) the observed value * Normalized Mean Square Error, defined as NMSE = V #Y co where C is the predicted mean value and 0 the observed mean value. Factors FA2 ans FAS, which represent the percentage of predicted values which are within factor 2 or 5 of the observed value. The measurements sample can be described by its mean value 0 = 0,664 Bq/nf and its variance 0,733. Table 1 shows the values of the parameters for each simulation and for the ATMES sample (MEDIA results during the ATMES experiment). ATMES Expl Exp2 Exp3 Exp4 ExpS Exp6 Mean value 0,423 0,429 0,326 0,313 0,317 0,430 0,579 Variance 0,870 0,747 0,788 0,520 0,625 1,086 1,026 Bias -0,24-0,23-0,33-0,35-0,35-0,23-0,085 NMSE 2,99 2,55 4,33 3,92 4,02 3,57 2,43 FA2 26% 27% 19% 21% 22% 25% 36% FAS 57% 60% 48% 48% 48% 57% 65% Table 1 : statistical coefficients for each simulation (Total sample : 156 values) It appears that we can gather the results of the different simulations in 3 classes. Class A (experiment 2,3 and 4) represents the simplest simulations of the source term. Class B stands for the gaussian (function of the distance) representation of the source term (experiments 1, 5 and ATMES). Class C includes a complex simulation of the source term, taking into account the meteorological conditions within the source mesh, by coupling a subgrid eulerian model to MEDIA The mean values are about 0,3 Bq/nf for class A, 0,43 Bq/m^ for class B and nearly 0,6 Bq/nf for class C, while the bias are near -0,3, -0,2 and -0,1 for class A, B, C. Coefficient FA2 (and FAS) shows that 20% (48%) of the predicted sample is within factor 2 (5) of the observed sample for class A, 26% (57% to 60%) for class B and 36% (65%) for experiment 6.

7 Computer Simulation 119 As a conclusion, the overall behaviour of experiments 2,3 and 4 is not as good as the atmes simulation. Experient 5 gives results similar to those obtained with the ATMES simulation. Experiment 1 is slightly better than atmes simulation and experiment 6 seems to provide a significant improvement of the results. We then selected 5 locations : Praha, Attikis, Berlin, Innsbruck and Helsinki and compared the predited values to the observed ones. We define the total coverage TC as TC = rap(i) = O if not. with rap(i) = if C(i) is lower than 0(i) or Values of TC (in percentage) for the 5 selected locations and for the whole sample are presented in table 2. ATMES Expl Exp2 Exp3 Exp4 Exp5 Exp6 Attikis Berlin Helsinki Innsbruck Praha Total sample Attikis (see figure 1) : MEDIA overestimates air concentrations for all the simulations. Experiment 3 gives the best results with a total coverage of 63%. The differences between all the simulations are not very important, almost all the predicted values are within factor 5 of the observed data Experiments 3, 1, 4 and 6 improve the results of the ATMES simulation.

8 120 Computer Simulation ATTIKIS 06/5 07/5 08/5 09/5 Figure 1 : Time evolution of daily average air concentration. Comparison between measurements and the 6 simulations from to Berlin (see figure 2) : MEDIA underestimates air concentrations for all the simulations. Experiment 5 gives the best results with a coverage of 46%. Except for experiment 2, there is no great difference between the simulations. Experiment 1 improves slightly the result of the ATMES simulation. BERLIN fli D ATMES expl exp2 D exp3 D exp4 B exps (II expg 29/4 30/4 01/5 02/5 03/5 04/5 05/5 06/5 07/5 08/5 Figure 2 : Time evolution of daily average air concentration. Comparison between measurements and the 6 simulations from to

9 Helsinki (see figure 3) : Computer Simulation 121 MEDIA slightly overestimates air concentrations for experiments L, 3,4 and 5. Except for experiment 6, the simulations are very close to each other. No experiment improves the results of the ATMES simulation. HELSINKI E ff 29/4 30/4 01/5 02/5 03/5 04/5 05/5 06/5 07/5 08/5 09/5 Figure 3 : Time evolution of daily average air concentration. Comparison between measurements and the 6 simulations from to Innsbruck (see figure 4) : MEDIA generally overestimates air concentrations by a slight amount for all the simulations. Experiment 6 gives the best results. Differences between experiments are significant over this location. Experiment 1 gives quite the same results as the ATMES simulation.

10 122 Computer Simulation INNSBRUCK i -' DATMES I _ expl i exp2! Oexp3, I ; U exp4 S exp5 ; dexp6 I 02/5 03/5 04/5 0*5/5 06/5 07/5 LJL Figure 4 : Time evolution of daily average air concentration. Comparison between measurements and the 6 simulations from to Praha (see figure 5) : Except for experiment 6, MEDIA underestimates air concentrations. The difference between experiments are not vey important. Experiment 1, 5 and 6 gives similar results to those derived from the ATMES simulation. PRAHA LJ ATNtS exp! exp2 D exp3 Dexp4 Sexp5! Ml exp6! 30/4 01/5 02/5 03/5 04/5 05/5 06/5 07/5 08/5 09/5 Figure 5 : Time evolution of daily average air concentration. Comparison between measurements and the 6 simulations from to

11 Computer Simulation 123 Figure 6 shows a chart of air concentration contour level above 0.1 Bequerel per cubic meter for the different simulations. The contours are very similar. The shapes of the polluted cloud look almost identical at long range. The differences appear near the source and on the eastern part of the clouds. Figure 6 : Air concentration on at 00 UTC (Significant value : 0.1 Bq/m3) 5 CONCLUSION AND FUTURE The comparison presented in the paper stresses that the simplest representations of the source term (putting the whole mass at the nearest grid point, contamining the 4 grid points surrounding the location of the source with quarter of the mass or proportionnaly with the distance) do not improve the results of the ATMES simulation. The Gaussian distribution, as a function of the distance from the source, used during the atmes experiment gave almost the same results when reduccing the number of polluted points (experiment 1) or when trying to only contamine points located downstream from the source (experiment 5). Nevertheless experiment 1 slightly improves the overall estimation of air concentrations. The spread of the predicted sample is similar to the spread of the observed one, the mean value is a little closer to the observed one, the bias is smaller and FA2, FA5 are higher.

12 124 Computer Simulation The complex simulation of the source term (MEDIA within MEDIA) gives a significant improvement of the results for all the criteria studied except for the spread, which is higher than the observed one. We are now trying to get more measured data in order to increase the sample with locations near the source (especially RIGA, VILNIUS, BARYSHEVKA, RAKHOV, MINSK and ST PETERSBURG). It should be emphasized that all the measurements that we get, are located far from Chernobyl; the nearest one is about 1200 km. At this distance, the differences between all simulations are not very important. But discrepancies could be worse at shorter range. On another hand, a comparison over one case does not lead to a definite conclusion, it only helps us in our way to improve the operational code MEDIA by giving some informations over different simulations of the source term. On major point is that we succeed in coupling MEDIA (large scale) with a subgrid model. In the future we will develop a lagrangian model for the source mesh and compare MEDIA(Langrangian source mesh) with the observed sample. The computing time was quite the same for experiments 1, 2, 3, 4 and for ATMES simulation. Experiment 5 required 5% more time and experiment 6, 35% more time. REFERENCES : Klug, W,G.Graziani, G.Grippa, D.Pierce & C.Tassone. Evaluation Of Long Range Atmospheric, transport Models Using Environmental Radioactivity Data From The Chernobyl Accident - The ATMES Report, Elsevier Applied Science, London and New York. PiedelievreJ.P, L Musson-Genon & F.Bompay. MEDIA An Euleran Model of Atmospheric dispersin:first Validation on the Chernobyl Relcasejournal of Applied Meteorology,Vol 29, N 12, December 1990,American Meteorological Society. Raes F,G Graziani, L Grossi, L.Marciano,D. Peirs, B.Pedersen,D. Stanners & N.Zarimpas. Radioactivity Measurements in Europe after the Chernobyl Accident,Commission of the European Communities, Nuclear Science and Technology, Directorate-General for Science, research and Development-joint research centre-ispra Site