Belgian Nuclear Research Centre (SCK CEN), Boeretang 200, B-2400 Mol, Belgium (2)

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1 Radiation Protection Dosimetry Vol. 14, No. 1, pp (23) Nuclear Technology Publishing DATA ASSIMILATION IN THE DECISION SUPPORT SYSTEM RODOS C. Rojas-Palma (1), H. Madsen (2), F. Gering (3), R. Puch (4), C. Turcanu (5), P. Astrup (6),H.Müller (7), K. Richter (7), M. Zheleznyak (8), D. Treebushny (8), M. Kolomeev (9), D. Kamaev (9) and H. Wynn (4) (1) Belgian Nuclear Research Centre (SCK CEN), Boeretang 2, B-24 Mol, Belgium (2) DHI Water & Environment, Agern Alle 11, DK-297 Horsholm, Denmark (3) Institute for Ion Physics, University of Innsbruck, Technikerstr. 25, 62 Innsbruck, Austria (4) Department of Statistics, University of Warwick, Coventry CV4 7AL, United Kingdom (5) IFIN-HH, National Institute of Physics and Nuclear Engineering Atomistilor 47 Street, 769 Bucharest, Romania (6) Risoe National Laboratory, Frederiksborgvej 399, 4 Roskilde, Denmark (7) GSF National Research Center for Environment and Health Ingolstaedter Landstrasse 1, Neuherberg, Germany (8) Institute of Mathematical Machines and System Problems, National Academy of Sciences Prospect Glushkova 42, Kiev, Ukraine (9) Scientific Production Association Typhoon, Lenin 82 Street, 2492 Obninsk, Russia Received November 7 22, amended February 12 23, accepted February Abstract Model predictions for a rapid assessment and prognosis of possible radiological consequences after an accidental release of radionuclides play an important role in nuclear emergency management. Radiological observations, e.g. dose rate measurements, can be used to improve such model predictions. The process of combining model predictions and observations, usually referred to as data assimilation, is described in this article within the framework of the real time on-line decision support system (RODOS) for off-site nuclear emergency management in Europe. Data assimilation capabilities, based on Kalman filters, are under development for several modules of the RODOS system, including the atmospheric dispersion, deposition, food chain and hydrological models. The use of such a generic data assimilation methodology enables the propagation of uncertainties throughout the various modules of the system. This would in turn provide decision makers with uncertainty estimates taking into account both model and observation errors. This paper describes the methodology employed as well as results of some preliminary studies based on simulated data. INTRODUCTION Model predictions for the rapid assessment and prognosis of the possible radiological consequences after an accidental release of radionuclides play an important role in nuclear emergency management. Radiological observations, e.g. dose rate measurements, can be used to improve these model predictions. The combination of model predictions and measurements, usually called data assimilation (DA), is described in this paper within the framework of the European decision support system RODOS (1). As opposed to earlier work related to improvement of model predictions based on ground level dose rate measurements (2), the approach described in this paper is designed to work in real time and on existent monitoring networks. DA capabilities are under development for several modules in RODOS (3), including the atmospheric dispersion, deposition, food chain and hydrological modules. The objective of introducing DA techniques in the atmospheric dispersion calculations is to improve the Contact author crojas@sckcen.be predictive capabilities of the RODOS system, by making use of observations near the release point to update and improve the predicted values at farther distances, e.g., the assessment of the accident consequences at or near densely populated areas. Measurements are performed by mobile units or fixed radiological monitoring networks, which exist in many European countries. A new module, the atmospheric dispersion updating module, runs in parallel with the atmospheric dispersion module to update the air concentrations of radionuclides using gamma dose rate measurements. This module also computes cumulated nuclide specific depositions on ground surface as an input to the deposition module. The late phase monitoring modules are the deposition monitoring module and the food chain monitoring module. The deposition monitoring module (DeMM) is designed to update the total deposition on ground surface and on all kinds of plant surfaces. The update is based on measurements of gamma dose rates after the deposition process has ended and on measurements of radionuclide concentrations on plants. The food chain monitoring module (FoMM) updates radionuclide concentrations in different feed and foodstuffs based on direct measurements of these quantities. 31

2 The hydrological DA modules consist of DA tools for each of the main components of the hydrological model chain, including wash-off of radionuclides from watersheds and transport and sedimentation in rivers, reservoirs and lakes. The update is based on measurements of radionuclide concentrations in the water bodies. The module chain in RODOS with extensions of DA modules (3) is shown in Figure 1. DATA ASSIMILATION FRAMEWORK Data assimilation in RODOS is based on the use of the Kalman filter (KF) which is a recursive, linear, minimum mean-squared error estimator (4,5). The KF estimates a particular state of a system by linearly combining a prediction of the state with a set of measurements. Here, a state refers to a mean value and its uncertainty, which is expressed in terms of a covariance matrix whose value evolves in time and thus reflects the expected mean-squared error of the estimated state at any time. The basis for the KF methodology is a state-space formulation of the numerical model with a stochastic extension. The different models in the model chain can be formulated as follows: x k = (x k 1, u k, k ), (1) where is the model operator representing the numerical scheme of the model, x k is the state vector representing the state of the modelled system at time step k, u k is the forcing of the system, and k is a stochastic element representing the uncertainty of the system. Atmospheric Dispersion Updating Module Late Phase Monitoring Modules C. ROJAS-PALMA et al The modelling uncertainties are mainly attributed to: (1) uncertainties in the model forcing; (2) model structural uncertainties, i.e., neglected or poorly described physical processes in the system equations and mathematical approximations; (3) uncertainties related to the use of non-optimal model parameters. Uncertainties in the model are captured by assigning probability distributions to key parameters and variables. The uncertainty of the state of the system can then be assessed by propagating these probability distributions in the modelling system according to the model dynamics described by the model operator (.). Uncertainties are propagated in the model chain by forcing the downstream models by the uncertainties in the upstream models according to the model interfaces, as shown in Figure 1. In addition to Equation 1, the observations have to be given a stochastic interpretation. This is formulated in the measurement (observation) equation Terrestrial Food and Dose Modules z k = C k x k + k, (2) where C k is a matrix that describes the relation between measurements and state variables (i.e. a mapping of state space to measurement space), and k is a random noise, representing the observation errors with covariance matrix R k. Measurement uncertainties arise from a number of sources, including the measurement equipment, the sampling procedure and the interpretation of sensor measurements as state variables, e.g., the use of point Atmospheric Dispersion Module Deposition Module Hydro Modules Hydro Data Assimilation Modules Aquatic Food and Dose Modules 32 Countermeasure Modules Figure 1. RODOS module chain (white rounded rectangles), and its interaction with data assimilation modules (grey rounded rectangles).

3 measurements to represent grid averages in the numerical model. An updated state of the system x a k is obtained by a linear combination of the model forecast x f k and the measurements z k as x a k = x f k + K k (z k C k x f k). (3) The corresponding updated covariance matrix is given by P a k = P f k K k C k P f k, (4) DATA ASSIMILATION IN RODOS wherep f k is the forecasted covariance matrix. In Equations 3 and 4 K k is the Kalman gain, which is a weighting matrix that reflects the relative uncertainties of the model dynamics and the data, as well as the correlation between the different state variables: K k = P f kck T [C k P f kck T + R k ] 1. (5) The main challenge in the operational use of the Kalman filter is the description of the uncertainty propagation, i.e., determination of P f k. In the case of non-linear model dynamics, an approximate Kalman filter algorithm, the extended Kalman filter, in which the uncertainty propagation is based on a statistical linearisation of the model equation can be adopted (5). In highdimensional systems, the huge computational load and storage requirements associated with the propagation of the error covariance matrix is the main bottleneck for operational use. In recent years, several so-called suboptimal schemes have been formulated which use different approximations of the error covariance modelling to reduce the computational burden. For the data assimilation in RODOS, several procedures, which are especially tailored towards operational use, are being investigated and implemented in the modelling system. These include the ensemble Kalman filter (EnKF) (6 8), the reduced rank square root (RRSQRT) filter (9), and a Kalman filter based on a fixed error covariance (1,11). In the EnKF the statistical properties of the system state are represented by an ensemble of possible state vectors. In the forecast step of the EnKF, each of the initial state vectors is propagated through the model operator. Model uncertainties are considered by applying an ensemble of possible sets of model parameters according to their uncertainty distributions. In the analysis step each of these propagated state vectors is updated using a common Kalman gain. Measurement errors are considered by replacing the measured values through an ensemble of possible measurements generated from the measurement error covariance matrix (7). The resulting ensemble of updated state vectors provides an estimate of the updated system state and the associated covariance matrix. Another approach is to approximate the covariance matrix by a matrix with reduced rank. In this case, only the leading eigenvectors of the covariance matrix are included in the covariance modelling. Verlaan and Heemink (9) used this approach together with a squareroot factorisation of the covariance matrix (reduced rank square root filter). The RRSQRT KF can be seen as an ensemble Kalman filter where the ensemble members are not chosen randomly, but in the directions of the leading eigenvectors of the covariance matrix. In the EnKF and RRSQRT filter the error covariance matrix has to be propagated as part of the model simulation, which typically requires a computational load of the order of 1 model simulations. The computational time can, however, be drastically reduced by replacing the dynamic evolution of the forecast errors with a fixed error covariance matrix, or equivalently, with a fixed Kalman gain matrix. In an operational setting this approach is very efficient since the computation is only slightly more expensive than a model run without data assimilation. However, a serious disadvantage is that uncertainty propagation is not part of the assimilation, and only average prediction uncertainties can be given. In the following a description is given of the implementation of the Kalman filter in the different modules of the RODOS system. The achievement of the data assimilation methodology is tested with twin experiments: data from a model scenario assumed as true are taken as simulated measurements, while a modified scenario is considered as false model prediction. Data assimilation corrects this false prediction by assimilating the simulated measurements. The resulting updated data can be checked against the true prediction. DATA ASSIMILATION IN THE ATMOSPHERIC DISPERSION MODEL Dispersion calculation and uncertainty description One of the local scale atmospheric dispersion models (ADMs) in RODOS is the Danish Mesoscale RIMPUFF model (12). It represents the radioactive cloud using a number of small clouds called puffs. The puffs are released at fixed time intervals, typically 1 min, with each puff containing the amount of radioactivity being released during the next time interval. The puffs follow the local wind and grow as a function of the local turbulence. The local wind and turbulence are estimated with a meteorological pre-processor working on numerical weather prediction data from the national weather service or on data coming on-line from meteorological measurement stations. Uncertainties embedded in the used background data add uncertainties to the puff location, size, and content. The uncertainties in wind direction and speed, for example, add uncertainties to the horizontal position of the puffs, turbulence uncertainties cause uncertainties in the puff size, and, as the puffs are forced to rise with increasing size, also in the puff centre height, while uncertainties in release rates lead to uncertainties in the puff content. The preprocessor models and RIMPUFF itself are all based on 33

4 simplifying assumptions, linearisations etc, and so contribute to the uncertainties in all calculated parameters. The RIMPUFF model has been previously evaluated using data from an experiment involving 41 Ar emissions from a research reactor and field measurements (13). Data assimilation methodology and results The data assimilation methodology for the atmospheric dispersion module (14,15) is intended to improve the overall behaviour of the RIMPUFF model, e.g., the calculated gamma dose rates (GDRs) for locations of interest, for example towns in the vicinity of the nuclear power plant. An extended Kalman filter (5) is specified, and the state vector is selected to include the radioactive contents and the centre positions of all puffs within the calculation area. At time k it can be written as x k = (x p,k : p = 1,, np(k)), where np(k) is the actual number of puffs and x p,k = (xp p,k, yp p,k, zp p,k, q p,k ) holds the centre coordinates and the radioactive content of puff p. The data being assimilated are gamma dose rate measurements at points within the calculation area, and first of all the measurements from the detectors placed around the nuclear power plant. These dose rate measurements result from both cloudshine and groundshine. The elements of the system equation matrix, which originates from the linearisation of Equation 1, are the derivatives of the time k state vector elements, with respect to the elements of the time k 1 state vector. As the puff advection is not directly dependent on the actual puff position, the derivatives of the wind velocity with position are neglected. The derivatives of the puff contents with respect to the former contents are calculated taking into account that both radioactive decay and deposition are directly proportional to the content. The elements of the observation equation matrix (see Equation 2) are the derivatives of the measurement prediction with respect to the state vector variables. The gamma dose from a given puff to a given point is directly proportional to the radioactive content of the puff, but a non-linear function of the distance between the point and the puff centre, and thereby of puff position. A further complication is caused by the fact that gamma dose rate measurements are available only as averages over time, e.g. 1 min, and by the other fact that a puff model does not give reasonable instant gamma dose rate values, but also has to be averaged over a period of time. Within the time period of 1 min, a puff can travel a long distance, for which reason its trajectory is broken up into 1 one minute steps. Therefore, it is necessary to compute the derivatives of the calculated averaged GDRs with respect to the variation in the final positions/inventories. Twin experiments were used to evaluate the DA methodology, and preliminary results show that it can significantly improve the prediction of GDRs. In one of C. ROJAS-PALMA et al these experiments, environmental monitoring data are simulated for the Belgian Telerad detector network. Next the same scenario was run with a perturbed source term. Finally, a small set of detectors within a radius of 1 km from the site is used for updating the predicted GDRs at distances ranging from 3 to 4 km. Thus, the updating run is also performed with the perturbed source term, but now assimilating the data given by the first run. Figure 2 shows the calculated 1 min average GDRs Mean GDR (Gy s -1 ) Mean GDR (Gy s -1 ) Mean GDR (Gy s -1 ) True False Updated 26 Time since start of release (min) True False Updated Time since start of release (min) Time since start of release (min) 28 True False Updated 42 Figure 2. Twin experiment GDRs calculations for three prediction points (a, b, c). The ADM run with data assimilation, updated, gives 1 min mean GDRs closer to the simulated true values than the run without data assimilation, false. 34

5 for three locations of interest, corresponding to highly populated areas. The described implementation of Kalman filtration performs reasonably well, and the GDRs of the DA-based updated run are closer to those of the true run, than are those given by the system without DA. In particular, it performs very well for the first two prediction points. DATA ASSIMILATION IN THE DEPOSITION MODEL The Deposition Monitoring Module DeMM is designed to update the predicted total (dry + wet) deposition on the ground and on all kinds of plant surfaces. The predicted deposition data are calculated with a deposition model using results from atmospheric dispersion modelling. The update is based on measurements of net gamma dose rates after the deposition process has ended. For the future it is planned to incorporate also measurements of nuclide-specific dose rates (i.e., from in situ gamma spectrometry) and measurements of radionuclide concentrations on plants. Deposition calculation and uncertainty description The deposition model in DeMM is based on the radioecological model ECOSYS-87 (16), extended by an atmospheric resistance model for dry deposition. Input data are mainly results of the atmospheric dispersion models like concentration of radionuclides in air and rain water. From these data the deposition model calculates the activity deposited on pasture, lawn and up to 22 plant types. In the model the total deposition to pasture and lawn is composed of three contributions: dry deposition to bare soil, dry deposition to grass and total wet deposition to the ground surface. Deposition to a plant consists of dry deposition to the plant surface and the fraction of wet deposition which is held back on the plant. Dry deposition to a plant is determined by the atmospheric resistance and the resistance of the plant canopy, which itself depends on the stage of the plant s development. Results of the deposition model are always connected with some uncertainty, which can be described by assigning probability distributions to key parameters and variables. Probability distributions for model parameters have been derived based on earlier studies (17 19). Uncertainties of the input data are given by an error covariance matrix, which describes the statistical properties of the input data. This covariance matrix is the outcome of propagating uncertainties through the atmospheric dispersion modelling (see above). Due to the high-dimensional state of the system and the nonlinearities in the modelling, mathematical approximations are needed for the propagation and exchange of the covariance matrix. Here an ensemble representation of the covariance matrix is used as an efficient approximation. DATA ASSIMILATION IN RODOS Data assimilation method and results Data assimilation in DeMM is based on an ensemble Kalman filter (6 8). The state vector of the EnKF in DeMM consists of total deposition to lawn and wet deposition, for each radionuclide and each point of the calculation grid in RODOS. In addition to that, deposition to soil and to all considered plants is included for those points, for which measurements are available that can be assimilated in the food chain monitoring module (these data are input to FoMM). One example result from a twin experiment with an EnKF applied in the deposition model is shown in Figure 3. All data are displayed for a grid of cells with a grid spacing of 1 km, a simulated release of radionuclides occurred in the centre of the grid, while the wind was constantly blowing from east to west (right to left). The data displayed are ground depositions with a logarithmic colour coding, only the measurements represent net dose rates (in ngy h 1 ). In this experiment, measurements (b) at 24 detector points (circularly arranged with 1 km radius around the release point) are generated from a true model prediction (a) disturbed with measurement errors. A false model prediction (c) is based on disturbed weather conditions (a deviation of 3 in wind direction) and source term data (a factor of.1 in the total released activity). This false prediction is then updated by assimilating the measurements (d). It can be seen that the Ensemble Kalman filter can quite successfully correct the deviations in wind direction and source term. DATA ASSIMILATION IN THE FOOD CHAIN MODEL Calculation of plant contamination and uncertainty description Part of the RODOS system is the Food Monitoring Module (FoMM) which updates the predicted radioactive contamination of feed and foodstuffs by measured data. The module at this stage calculates the activity concentration of plant products. For this, FoMM computes activity concentrations normalised to unit deposition (1 Bq m 2 ) to soil and unit deposition (1 Bq m 2 ) to foliage of the plants (16). The normalised activities depend on the type of plant, on the type of soil, on the radionuclide, on the deposition date and on regional conditions with respect to climate, agriculture and vegetation. The normalised activities are calculated for a variable time grid, beginning at time t = days until t = 1 years after deposition. In order to calculate the plant contamination at a certain point of the RODOS grid, the appropriate normalised activities are multiplied with the activities deposited at this grid point. The uncertainty of the model predictions is caused by the uncertainty of the deposition data, which are given by the DeMM. Furthermore, the values of the model 35

6 parameters cannot be determined precisely and are additionally subject to natural variability. This leads to an uncertainty of the values of the normalised activities. Therefore, in order to estimate the uncertainty of the normalised activities as well as possible, some knowledge about the uncertainty of the model parameters is important. Using the results of earlier studies (17 19) the uncertainty characteristics of the food chain model parameters are derived and the most important parameters have been identified by sensitivity analysis (for examples see Table 1). C. ROJAS-PALMA et al Data assimilation method and results As in DeMM, data assimilation is carried out with the EnKF (6 8). The EnKF updates the state vector for those points in time at which the samples for the measurements have been taken, and for future points in time. However, for dose calculations activity concentrations integrated over the whole time range beginning with the deposition date are needed. Therefore, an Ensemble Kalman smoother (2) is used to update the state vectors at points in time before the samples have (a) (c) (b) (d) Figure 3. Twin experiment: (a) True prediction; (b) Measurements; (c) False prediction; (d) EnKF updated prediction. The figure shows deposited activity of 137 Cs after a release of radionuclides into the atmosphere, only the measurements in (b) are net dose rates (in ngy h 1 ). Table 1. Probability distributions of some selected food chain model parameters with most probable value (mpv), minimum value (min), maximum value (max) and type of probability density function (pdf). <1-1 <1 <1 <1 2 <1 3 <1 4 >1 4 Bq m -2 Parameter mpv min max pdf Soil mass of arable land/pasture (kg m 2 ) uniform uniform Weathering half-life (d) triangular Beginning/end of harvest, leafy vegetables (Julian day) triangular triangular Yield at time of harvest, leafy vegetables (kg m 2 ) uniform Beginning of growth of leafy vegetables (Julian day) triangular 36

7 been taken. The Ensemble Kalman smoother ensures that the state vector of each point in time is updated with all available measurements. An initial ensemble of state vectors is calculated by using the uncertainties of the model parameters. In order to take into account correlations between different plants and radionuclides without the need of unfeasible storage resources, the state vectors contain normalised activities. This has the further advantage that activity concentrations are updated not only in the surroundings of measurement sites but at all grid sites of a radio-ecological region. However, because of this definition of the state vector, normalised activities are the more updated the smaller the uncertainty of the deposition data. Since the activity concentrations are calculated by multiplying normalised activities with deposition data, a large uncertainty of the deposition data leads to a small Kalman gain and thus to small changes of the state vector. This means, improvements of food model predictions are achieved only if the uncertainty of the deposition data is not too high. In order to test data assimilation in FoMM, a twin experiment was carried out. In the example shown in Figure 4 the uncertainty is caused only by the uncertainty of the model parameters, i.e., the uncertainty of deposition data is neglected. Since the measurement error has been assumed to be small (1% of the prediction error), the Kalman filter and Kalman smoother update the false prediction very well. Furthermore, the uncertainty of the update is smallest at the time of measurement, as is expected. Bq kg measurement Days after deposition Figure 4. Twin experiment for 137 Cs activity concentration in leafy vegetables after a deposition on a summer day. For the false prediction and the update, the curves represent median, as well as 5% and 95% percentile values. True prediction, False prediction, update. DATA ASSIMILATION IN RODOS DATA ASSIMILATION IN THE HYDROLOGICAL MODEL CHAIN Modelling system and uncertainty description The hydrological model chain consists of several individual modules. The basic hydrological model chain includes modelling of wash-off of radionuclides from watersheds following deposition from the atmosphere (RETRACE) (21), and modelling of the transport and sedimentation of radionuclides in river systems (RIVTOX) (22). Extended modelling components include 2D (COASTOX) and 3D (THREETOX) models for radionuclide transport (23,24). RIVTOX, COASTOX and THREETOX use as input the run-off predictions from RETRACE and can also handle direct releases into the water bodies. If deposition occurs directly on water bodies, surface concentrations from the atmospheric dispersion module are used as input in COASTOX and THREETOX. The output from the hydrological model chain is used by the aquatic food and dose module in RODOS for calculating the transfer of radionuclides to man and the resulting radiation exposure. One of the main uncertainties in the modelling of radionuclide wash-off from watersheds is related to the concentration and spatial distribution of the radionuclide deposition. In addition, for the wash-off coefficients that determine the rate of wash-off only rough and highly uncertain estimates can be given which adds further to the uncertainty. The deposition and the corresponding uncertainty (in terms of an ensemble approximation of the covariance matrix) are obtained from DeMM. Thus, when gamma dose rate measurements become available for updating DeMM more precise estimates of the deposition are obtained, which will reduce the uncertainty in the wash-off modelling. The uncertainty will be further reduced when radionuclide concentration measurements in downstream water bodies become available for updating the wash-off coefficients. The uncertainties related to the radionuclide wash-off modelling in RETRACE are propagated downstream in the model chain to the RIVTOX, COASTOX and THREETOX models. In addition to the wash-off uncertainty these models are also highly affected by uncertainties in key model parameters such as the partition coefficients and the exchange rates for the water suspended sediment and suspended sediment bottom layer systems. Uncertainties related to direct releases into the water bodies may further add to the model uncertainty. Data assimilation method and results For data assimilation in the hydrological model chain measurements of concentrations of different radionuclides in solute and on suspended sediments are available. Based on these measurements the RIVTOX, COASTOX and THREETOX models can be updated. 37

8 This includes updating of the three different phases of radionuclides: (i) in solute, (ii) on suspended sediments, and (iii) in bottom depositions in all computational grid points of the modelled system. Since the three radionuclide phases are linked together via the sorption/ desorption process descriptions in the model, the Kalman filter is able to update all three phases when only one of the phases is being measured. Direct observations for the watershed wash-off modelling in RETRACE will usually not be available, and updating of this model is therefore based on a feedback from the downstream models. In this case the wash-off coefficients in RETRACE are updated based on updated lateral inflow concentrations in the downstream models. For the data assimilation, different cost-effective Kalman filter procedures are being investigated and implemented in the hydrological modelling system. These include the reduced rank square root (RRSQRT) filter, the ensemble Kalman filter (EnKF), and Kalman filter based on a fixed error covariance matrix. Preliminary results from a twin test experiment with C (Bq m -3 ) (a) C. ROJAS-PALMA et al t (days) t (days) C b (Bq kg -1 ) (c) true updated false the RIVTOX model based on the RRSQRT filter are presented in Figure 5. In this test, measurements of radionuclide concentrations at one point in the river system are generated from a true model simulation disturbed with measurement errors. A false model simulation using a disturbed boundary condition is then corrected by assimilating the measurements. In general, the Kalman filter is able effectively to correct the model when forced with wrong boundary conditions. Note that all three radionuclide phases are effectively updated when only one of the phases is being measured. FUTURE WORK The methodology for data assimilation in the atmospheric dispersion model and the deposition model will be further evaluated and improved by making use of simulated environmental measurements generated with the Atmospheric Release Advisory Capability (ARAC) of the Lawrence Livermore National Laboratory, CA, USA. Last but not least, the final test will be carried C 5 (Bq kg -1 ) (b) true updated false true updated false t (days) Figure 5. Results of Kalman filter update using measurements of dissolved concentration in a single point in the river system. The figure shows (a) dissolved concentration: (b) concentration on sediments, and (c) concentration in bottom sediments at a downstream point. 38

9 out using data from the Tokaimura criticality accident in collaboration with the Japanese Atomic Energy Research Institute. The results will be reported in a separate publication. The Food Monitoring Module will be extended to animal products. The present approach and in particular the present state vector definition will be carefully evaluated and optimised if necessary. Furthermore, the Food Monitoring Module will be tested with real measurements of milk and grass contaminations which have been taken during several years after the Chernobyl accident in For the data assimilation module in the hydrological model chain the Kalman filter solutions will be further advanced to reduce computational overhead. This is particularly important for operational use of data assimilation in the 2D and 3D models (COASTOX and THREETOX). Furthermore, the data assimilation procedures will be extended to facilitate source term updating in the case of direct release into the water bodies, which is extremely important in the early phase of an accident. The aim of the data assimilation methods described in this paper is to improve the model predictions with radiological measurements. Moreover, these methods provide a particularly suitable framework for evaluating and optimising monitoring strategies. Such optimisation techniques are typically summarised as adaptive sampling or targeted observations methods. Kalman filter methods naturally allow us to quantify the effect of possible monitoring strategies on the quality of the results. Optimised monitoring strategies would allow the best use to be made of all observational resources in the case of a nuclear accident. Therefore, optimisation techniques based on the applied data assimilation DATA ASSIMILATION IN RODOS methods will be one of the main directions of future work. CONCLUSIONS The results of the twin experiments show that the Kalman filter methodology can be applied successfully to improve the model predictions of environmental radioactive contamination by means of measured data. The use of a generic data assimilation methodology enables the propagation of uncertainties throughout the various modules of the RODOS system. This proved to be essential for the ability of data assimilation tools to correct false predictions. For example, only appropriate uncertainty propagation in the atmospheric dispersion modelling allows the Deposition Monitoring Module to make best use of the measurement data. Furthermore, since the deposition data and their uncertainty provided by the Deposition Monitoring Module are important input for the Food Monitoring Module, as well as for the hydrological DA modules, a successful update of activity concentrations in feed and foodstuffs and in the aquatic environment depends strongly on the preceding DA modules. The integrated approach for all data assimilation tools in the RODOS system provides decision makers with more realistic estimates of the radiological situation and consistent uncertainty estimates taking into account both model and observation errors. ACKNOWLEDGEMENT The work described in this article has been performed with support of the European Commission under the contract Data Assimilation for Off-site Nuclear Emergency Management (DAONEM), contract no. FIKR-CT REFERENCES 1. Ehrhardt, J. RODOS Decision support system for off-site emergency management in Europe. European Commission EUR- Report EN (2). 2. Macdonald, H. F., Thompson, I. M. G., Foster, P. M. and Robins, A. G. Improved estimates of Ar-41gamma dose rates around Hinkley Point Power Station. In: Proc. 4 th Symp. on Radiation Protection Theory and Practice, Malvern, UK, pp (1989). 3. Rojas-Palma, C., Gering, F., Madsen, H., Puch-Solis, R., Richter, K. and Müller, H. Theoretical framework and practical considerations for data assimilation in off-site nuclear emergency management. Internal Report RODOS(RA5)-TN(1)1 (21). 4. Kalman, R. E. A new approach to linear filtering and prediction problems. J. Basic Eng. 82, (196). 5. Gelb, A. (Ed.) Applied optimal estimation. (Cambridge: MIT Press) (1974). 6. Evensen, G. Sequential data assimilation with a non-linear quasi-geotropic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99, (1994). 7. Burgers, G., van Leeuwen, P. J. and Evensen, G. Analysis scheme in the ensemble Kalman filter. Mon. Weather Rev. 126(6), (1998). 8. Houtekamer, P. L. and Mitchell, H. L. Data assimilation using an ensemble Kalman filter technique. Mon. Weather Rev. 126(3), (1998). 9. Verlaan, M. and Heemink, A. W. Reduced rank square root filters for large scale data assimilation problems. In: Proc. Second Int. Symp. on Assimilation of Observations in Meteorology and Oceanography (World Meteorological Organization) pp (1995). 39

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