3.3 Meteorology and atmospheric dispersion
A system integrated comprehensive atmospheric dispersion module has been built from models suitable for fast real-time atmospheric dispersion calculations as suggested by [1], cf. Table 1.
TABLE 1: THE MET-RODOS MODULE: Associated models and data
Near-range flow and dispersion models, including pre-processors:
· Meteorological pre-processor (PAD)
· Mass Consistent Flow model (MCF)
· Linearized flow model (LINCOM)
· Puff model with gamma dose (RIMPUFF)
· Near-range elongated puff model (ATSTEP)
Complex terrain models (stand alone system):
· Prognostic flow model (ADREA) and Lagrangian dispersion model (DIPCOT)
Mesoscale and Long-range Models:
· Hybrid Lagrangian-Eulerian model (MATCH)
On-line Weather Forecast data:
· Numerical Weather Prediction data (DMI-HIRLAM and SPA -TYPHOON)
The module is called MET-RODOS and it consists of models and pre-processors contributed to by Work Group 2 (Atmospheric Dispersion) partners.
3.3.1 The MET-RODOS module
A schematic overview of the system integrated MET-RODOS atmospheric dispersion module [2] is presented in Figure 1.
Details about the systems „functionality specification“ is described in [3] whereas the systems User’s manual [4] holds references to the systems User’s guides, input/output specifications and test runs.
The MET-RODOS dispersion module contains three distinguishable sub-systems:
• The Local-Scale Pre-processor LSP,
• The Local-Scale Model Chain LSMC, and
• The long-range Model Chain LRMC
57 On-site Off-site N So dar ’ s Met- +4 Hr Towers W Real -time P
ON-LINE MET-DATA INTERFACE & STORAGE Long- S u* x PAD SUB’ Range Local Scale z/L x Model x I O I Zi Chain Pre-processor A,B,C x I O M Models MCF & LINCOM provides A RO DO S Wind & Turbulence grid fields over: T
10Co 15Co C 20Co Topograph Roughness Thermal H
Shared RODOS INTEGRATED DISPERSION MODULES: ATSTEP RIM PUFF Memory
Figure 1.: (a) The MET-RODOS system integrated within RODOS
MET-RODOS: Atmospheric Dispersion Model Chains Input - Output and Model Chains
Meteorological on-line data: Release and site Input: On-line: On-line: Met-towers, SODAR, specific data Source Local (on-site) Met- Numerical Weather Prog’s from NWP centers Topography terms towers and Sodars Prediction data
Local Scale Preprocessor LSP Pre-processors for Met-towers, SODAR’s, NWP data Model Chains: Turbulence parameterisation schemes Local Scale Preprocessor
Atmospheric Dispersion Models Output:
Local scale models: Long-range model: Doses from Cloud and Trajectories and RIMPUFFand ATSTEP MATCH Deposition Weather Forecasts
(b) MET-RODOS Atmospheric Dispersion Model Chains. (c) MET-RODOS input/output and model chain structure.
MET-RODOS has its own build-in Local Scale Pre-processor software called LSP, cf. Figure 1a.
58 LSP [5] provides the local-scale Atmospheric Dispersion Models (ADM’s) with measured and forecasted local scale wind fields and dispersion parameters. It provides local scale diffusion and atmospheric deposition parameters as well as local scale wind fields for plume and puff transport. It integrates local scale wind models with micro-meteorological pre-processing algorithms.
LSMC, the local scale model chain [6], contains a suite of different local scale mean wind and dispersion models, Via LSMC, different wind and dispersion models can be activated depending on the character of the topography and atmospheric stability in question.
LSMC and provides via its build-in ADM‘s ground level air concentrations (in [Bq/m3]) and concentration of deposited isotopes (in [Bq/m2]), and ground level gamma dose rates (in Grays per second [Gy/s]) for subsequent use by the RODOS system. When clouds are leaving the outer bounds of the local scale domain (variable from 20 km to 160 km), diffusion specific parameters such as cloud size, content and position is passed on to the long-range model chain LRMC.
LRMC, the long-range model chain, manages the transport and fallout assessments on national and European scales in MET-RODOS. Near surface air concentrations (in [Bq/m3]) and integrated depositions of isotopes (in [Bq/m2]) are provided, Inputs are in the first place weather data from any numerical weather prediction system, like the DMI-HIRLAM, which gives a consistent description of the atmospheric state and motion on a synoptic scale. Secondly dispersion inputs are taken from the LRMC in terms of source information given as cloud puffs defined by location, size and mass content.
The MET-RODOS module is furthermore integrated with the RODOS systems real-data bases: an on-line met-tower database and a real-time updated Numerical Weather Prediction (NWP) database. The On- line Met-Tower Data Base maintains and updates meteorological met-tower measurements available to the system, and FCASTDB is a real-time numerical weather forecast data base that stores and time stamps the real-time numerical weather forecast data available to the system.
Integration within RODOS
MET-RODOS is an integral part of the RODOS system. Mode and time control, data management, user input and graphics control are all handled via the RODOS Operating Subsystem (OSY). As indicated in Figure 1a, MET-RODOS communicates with the operating system via shared memory and has access to the RoGIS integrated real-time databases. MET-RODOS’ input source terms are provided directly via RODOS real-time data bases while meteorological data are downloaded in background via on-line network connection. MET-RODOS generated output (i.e. dose rates from air and ground deposited material) is stored in the ROGIS database.
On-line meteorological input data
Real-time application of the system requires an on-line connection to quality real-time measurements of local meteorological quantities (wind, direction, stability etc). Such data must be available from at least one nearby and on-line connected met-tower in the vicinity of the release point (on-site). For application of the system on distances beyond the local 10 (20) km) scale, on-line meteorological measurements from within the regional (100-km scale) of wind and temperature conditions can also be used by the MET-RODOS module for „now-casting“ of the plume spread in real time. The system can monitor an on-going release in real time based on met-data from a single or a network of on-line automatic meteorological stations.
Real-time numerical weather prediction data
59 Forecasting of accidents in time is based on pre-calculated or downloaded numerical weather prediction data to the system. If such data are not available directly at the RODOS emergency centre, such weather forecast data can be downloaded to the system via the Internet from national or international meteorological forecasting services.
Numerical weather prediction (NWP) data for Europe are today available via computer networks at high spatial and temporal resolution (8–50 km horizontal grid resolution at three (and in some cases one) hour time intervals up to typically +48 hours). High-resolution NWP data are produced around the clock at a number of national and international operational meteorological centres. During the RODOS development and implementation phase 1998–1999, NWP data have been obtained on-line from the Danish Meteorological Institute (DMI) and previously also from the SPA-Typhoon partner in Obninsk, Russia. A data delivery agreement was negotiated with the Danish meteorological institute DMI for real-time on-line deliverance of real-time on-line NWP products for the developing phase of the RODOS project. A subsequent section describes the DMI-HIRLAM model and the gained experience with the on-line data transfer and integration of DMI-HIRLAM NWP products in the MET-RODOS module.
The local scale model chain LSMC
The running of ATSTEP or RIMPUFF requires, in addition to the standard meteorological parameters wind and temperature, also determination of the dispersion controlling scaling parameters, such as stability category or the Monin-Obukhov stability measure, and determination of the mixing height.
To serve this purpose, extensive pre-processing software has been included in the local scale model chain, [6]. On-line incoming meteorology – from either automatic meteorology stations and/or from weather forecast model nodes near or inside the local-scale model domain – are pre-processed into gridded mean (wind) and turbulence quantities (including the above mentioned atmospheric stability measures) for all local scale grid points
LSP, which invokes a set of nine pre-processing routines (the so-called PAD sub-routines) is running in real-time in conjunction with the fast diagnostic local-scale and turbulence models (LINCOM).
The Local Scale Model Chain LSMC also handles the local scale dispersion, deposition and gamma radiation models, and it produces „source-terms“ for the long-range model chain LRMC.
Local scale pre-processor LSP
Figure 1(a-c) shows the Local Scale Pre-processing unit LSP interfacing on-line accessible meteorological information from met-towers and from NWP centres to the local scale dispersion models ATSTEP and RIMPUFF and to the long-range model chain with MATCH.
The LSP unit provides the necessary model input parameters for running both local and the long-range dispersion models. The starting point is parsing and binning of the (at random in time) on-line incoming meteorological data, which automatically are checked for consistency and stored in the RODOS systems real-time database. It holds separate partitions for both the on-line met-TOWER DataBase ”TOWERDB”, and the real-time numerical weather ForeCAST DataBase “FCASTDB”.
Continuously running in background LSP accesses the real-time database (every 10 min) and processes all new meteorology available, including new met-tower measurements and new forecast data, into gridded wind and scaling parameters fields on the local scale grid. They are continuously stored as time-stamped grid files in the RODOS system’s shared memory.
Pre-processor for atmospheric dispersion: PAD
A set of nine Pre-processing subroutines for atmospheric Dispersion, “PAD” as described in [1] were combined to convert the input primary meteorological observations (wind speeds, temperatures etc)
60 into atmospheric stability measures and scaling parameters as required by the similarity-based flow and dispersion models of MET-RODOS. These PAD subroutines are now integral part of LSP for real- time operational use.
Initially, a series off fixed input parameters must be available to the LSP: Gridded (typically 500 m × 500 m) values of land use or aerodynamic surface roughness and terrain height.
At the beginning of each local-scale meteorological updating period - which is typically between 10- min and 1-hr for most meteorological tower station-based systems, the PAD subroutines then automatically start processing of any new arrived primary meteorological data from tower measurements or from NWP. The list of primary input data typically include Cloud cover (in octaves), net radiation, horizontal wind direction and (if possibly also) wind vane fluctuations: [horizontal, vertical], vertical temperature profile (minimum 2 points), and wind speed measurements (minimum at one (10 metres) height.
Depending then on the set of actually available input data for a given 10-min period, the user can via LSP select the most suitable PAD –subroutines for his purpose, and then processes the required atmospheric stability measures such as: stability category, Monin-Obukhov stability length scale, mixing heights: (mechanical, convective). Also the turbulence scaling parameters such as: heat-flux, shear stress and variances: horizontal, vertical, and mean profiles of wind and temperature are calculated. Cf. [5,7] for details.
Linearized wind model: LINCOM
Detailed modelling of the wind and turbulence field on the local scale is important for prediction of the trajectory-directions - and the time of arrival - of radioactive clouds traversing across hilly terrain and over heterogeneous surfaces (e.g. over land-water-land interfaces).
To determine the advection, diffusion and deposition rate of the radioactive clouds in real-time, local scale winds and turbulence fields are also modelled within LSP. The integrated LINCOM model system provides the local model chain with a fast diagnostic flow model system, which is based on the solu- tion of a set of linearized momentum and continuity equations, with a first order spectral turbulent diffusion closure. The wind and turbulence fields are modelled under influence of: 1) the local topography (hills), 2) the vertical thermal stratification of the atmosphere, and the surface aerodynamic roughness’ (z0).
The linearized LINCOM-concept for neutral stratified, pressure-gradient driven winds over hilly terrain was first conceived in [8]. In the LINCOM-T version, LINCOM was extended in concept to include effects of thermally driven flows (such as valley breeze and nocturnal drainage flows, [9]. LINCOM has further been extended during the Met-RODOS project to model the effects of local changes in the surface aerodynamic roughness (LINCOM-z0) [10,11]. In addition to changes in the mean wind introduced by the dissimilarities in the surface roughness, the „z0" version also models the local turbulence levels (i.e. the surface sheer-stress field (u*)) over the local scale grid.
The above mentioned LINCOM-model is integral part of LSP. Together with PAD, LSP provides the local scale model system with „model-intelligent“ interpolated/extrapolated wind fields and turbulence fields at all grid points and heights, and these gridded fields are then subsequently available to the dispersion models ATSTEP and RIMPUFF to advect, deposit and diffuse the plumes and puffs.
Figure 2(a-c) shows LINCOM-z0 generated mean and turbulence winds over Northern Zealand (from [11]).
61
0.005 0.05 1.0 1 .5 4.0 m/s 11.2m/s 0.25 m/s 1 .0 m/s m/s Figure 2: Over Northern Zealand - Denmark: (a) Roughness distribution (z0) on a 1 km × 1 km grid; (b): LINCOM-Z0 mean wind field (U); (c) LINCOM-Z0 generated turbulence wind field (u*).
The mass consistent wind model MCF
Also a mass-consistent-flow model MCF has been integrated in the LSP module [12]. MCF has complementary properties to the LINCOM system: while the Navier-Stokes-equation based LINCOM system must be initialised with data from a single point (e.g. from a met-tower or from a single node point in the forecast model), the mass consistent MCF code complies better with simultaneous inputs from a network of met-towers.
MCF generates mass-consistent interpolated wind fields over the entire local scale domain under the constraints of minimum flux divergence [13].
As with the LINCOM models, applications with MCF should focus on gentle rolling but not steep terrain. The LSMC User’s guide assists the user in selecting the most suitable wind model (LINCOM or MCF) for a given application, and depending on the available meteorology.
Diffusion, deposition, and gamma dose models
The local scale model chain integrates the puff dispersion model RIMPUFF [14] and the elongated puff model ATSTEP[15]. Separate (stand-alone) dispersion systems based on a particle model have been interfaced separately for use in connection with severe complex terrain and local sea-breeze circulation’s (see section3.3.5). The long-range model chain is established by nesting the outputs from the local scale model chain to the Eulerian long-range model MATCH [16].
The puff dispersion model RIMPUFF
The local scale puff diffusion model RIMPUFF [17] provides the RODOS system with detailed real-time simulation of atmospheric dispersion phenomenon. It accounts for local changes in meteorological conditions (in both time and space) while the accident evolves. The dispersion model is provided with puff-splitting features such as “pentafurcation” and “trifurcation” for improved modelling of dispersion over moderately hilly terrain, which involves channelling, slope winds and inversion layer effects). Also a true Gaussian puff-based gamma dose module has been added [18].
62 All diffusion and deposition parameterisation in RIMPUFF is formula-based. The puffs local wind and turbulence levels as provided by LSP during each time stepping (typically 10 sec’s) determine the puff advection step and time and space-dependent diffusion growth rates.
RIMPUFF can accommodate user-specified formula-based parameterisation scheme for its horizontal and the vertical dispersion parameter σy and σz. RIMPUFF has 4 optional sigma parameterisation schemes included within the RODOS framework. They are the:
1. Karlsruhe-Jülich height dependent σy and σz (1-hr averaged plume sigma’s)
2. Risø instantaneous (no averaging) instant puff-diffusion sigma’s δy and δz.
3. Similarity-theory based plume-sigma’s (σy and σz) - averaging time 10-min to 1-hr.
4. German-French-Commission (GFC) proposed horizontal σy model for variable averaging-time, ranging between zero (instantaneous puff) and 1-hr (plume sigma’s). Special considerations have been given to low wind speed conditions. While the stability class dependent Karlsruhe-Jülich parameterisation requires a well-defined non-zero mean wind for proper calculation, the other schemes can work with low wind speeds, including zero. The similarity theory based method, for instance, assures proper W* scaling for puff’s growth-rates during unstable conditions with zero mean wind.
RIMPUFF is originally equipped with standard (Briggs) plume rise formulas and has usual inversion- height and ground-level based reflection options. The projects Russian Inco-Copernicus contractor NSI has developed a new plume rise module, which allows for inversion height penetration and large (Mega-Joule) energy releases (Cf. the Chernobyl accident). This new plume rise module has been developed to fit into the real-time mode in RIMPUFF. It applies to plumes of various intensities and for explosive heat sources under wide spectrum of atmospheric conditions, and handles also inversion layer penetration. Under conventional conditions, to which the conventional Briggs formulas apply, the result of the new NSI plume rise module is close to the old ones. To evaluate the new plume rise module, simulations of the convective cloud during the early phase of the Chernobyl accident were made (which was an explosive heat source), cf.[19]. This result includes the altitude of the release and dimensions of the cloud, and is calculated by treating the vertical-horizontal cloud asymmetry. The validation of the model and the tuning of the closure constants were carried out, with observational data for either small explosions (up to 30 kg of TNT equivalent) and high power nuclear explosions (0.5 to 50 kT TNT equivalent) used for this purpose.
A new gamma dose feature inserted in RIMPUFF plays an important role within RODOS for future data assimilation and back-fitting procedures in conjunction with real-time radiological (gamma) monitoring data.
Deposited activity is also modelled with RIMPUFF. Dry deposition rates are treated differently for e.g. iodine vapour (elementary iodide) and iodine contaminated aerosols, and different deposition velocities can be specified depending on land use. Figure 3 shows a RIMPUFF calculated footprint of deposited radioactivity from a 137Cs plume traversing Northern Zealand. During the plume passage, the deposition rate is varied depending on the local surface characteristics (land, water, forest, urban, etc.).
63 Cs137 deposition with roughness variation and localized deposition rates
6230
6220 Figure 3: 6210 Footprint of a depositing
] 6200 2
3 Cs137 plume calculated by 6190 RIMPUFF over Northern 6180
km UTM Zealand. The deposition rates 6170 Y [ vary locally according to land 6160 use. 6150
6140
680 690 700 710 720 730 740 750
X [km UTM 32] The atmospheric turbulent transport velocity can under certain conditions limit the effective dry deposition velocities for aerosol particulate [20]. RIMPUFF [17] takes this turbulence-limited deposition velocity into account. The 2 atmospheric deposition velocity of aerosols is estimated from the atmospheric resistance (U/u* ) as provided by the pre-processor PAD and the model chains LINCOM-z0 model. The new resistance-based deposition has been implemented into RODOS.
Another new feature in RIMPUFF is the so-called „Shear rise„ - it has been introduced based on our experience during the operational test phase of the system. As the vertical diffusion coefficient σz at some distance downwind has grown to a size larger than the initial release height, the new „Shear rise„ feature causes the mean puff height to increase as the puff size increases. As the wind speed and direction at each puffs centre point determines its path, the shear rise option provides a new method to take account of the vertical wind speed shear.
Also, for cases of a strong vertical directional wind shear, a more particle look-alike modelling of dispersion can be achieved in RIMPUFF by splitting the puffs in the vertical direction according to certain split-criteria. This new method for coping with strong vertical wind directional shearing is called “trifurcating”. Depending on the measured vertical directional shear, each puff splits vertically into tree new Gaussian puffs of the smaller size, each of which have the option to wondering off along separate directions.
The criteria for vertical puff splitting is: i) In a vertical plane and taken about the centroid of the trifurcated puff (i.e., the radial integrated inertia moment), and of the original puff, the second moments are equal. ii) The centre concentration at (x, y) = (0, 0) of the trifurcated and original puff is equal. iii) The sizes of the new trifurcated puffs all equal one-half times the original puff size.
64 In addition, we require mass conservation, i.e. the total amount of matter allocated to the tree new puffs must equal the amount of matter first allocated to the original one
The main idea behind introducing trifurcation is that once trifurcated the new two satellite puffs can set off in individual directions with the wind speed corresponding to their present centre height. Information about the vertical directional wind shear is explicitly contained in the local-scale wind flow-field, as it is determined at two heights.
An example of the effect of vertical directional shear on dispersion footprint is shown in Fig.4. The consequences for emergency response strategy is seen to be dramatic!
Integrated Cs137 concentrations. With/without Z-rise and Trifurcation 6192
6190
6188
6186 Trifurcation
6184 Base ] 2 3 M 6182 UT [km Y 6180
Pasquill C, 5 m/s 6178 Release height: 50 m
6176 Shear: 80 deg 6174 10 - 410 meters
6172 684 686 688 690 692 694 696 698 700 702 704 X [km UTM 32]
Figure 4: Plume dispersion during strong vertical wind direction shear, with (solid) and without (dotted) the combined shear rise and trifurcation feature.
The elongated puff dispersion model ATSTEP
ATSTEP [17, 21,22] is a Gaussian model with properties of a simplified puff model. It is capable of calculating the dispersion of elongated puffs during changing meteorological conditions and 2- dimensional wind fields with the advantage of low computing time. The length of the time step used limits the spatial and temporal resolution of the ATSTEP results. The time step length (and correspondingly the puff length) in ATSTEP is 30 minutes for prognosis calculations, and 10 minutes for diagnostic calculations.
It was the first atmospheric dispersion model fully integrated with the RODOS system. Because of its simplicity, it is extensively used in connection with demonstration and training of the RODOS system. It is also used as a tool for generating hypothetical data sets to be used with expert elicitation on local scale radiological accidents, and as a benchmark reference for RIMPUFF.
Due to its relatively long advection time steps (10 to 30 minutes, as opposed to 10-30 sec’s for RIMPUFF) - and to its correspondingly elongated puffs, ATSTEP requires much fewer computation loops 65 compared to RIMPUFF for simulation of a given accident, and therefore needs less computing time. Its shortcomings, however, compared to RIMPUFF, shows in connection with strongly inhomogeneous or non-stationary conditions, - including terrain, where detailed wind and turbulence structures are required from a wind and turbulence model.
Dispersion parameters used in ATSTEP
• Karlsruhe-Jülich height dependent σy and σz (1-hr averaged plume sigma’s) for high roughness length zo >1 m.
• Mol - σy and - σz for moderate roughness length zo =0.5 m.
• German-French-Commission (GFC) time dependent dispersion parameters derived from spectral model
ATSTEP is equipped with standard (Briggs) plume rise formulas and has usual inversion-height and ground-level based reflection options.
In ATSTEP dry and wet deposition of activity to surfaces is modelled taking into account the different deposition properties of nuclides by calculating dispersion, deposition, and depletion of puffs separately for four deposition groups: noble gases, elementary iodine, organically bound iodine, and aerosols. Correspondingly different deposition velocities and washout parameters are used. The deposition velocities depend on land use - it is varied depending on the local surface characteristics (land, water, forest, urban, etc.).
ATSTEP calculates the gamma-dose rate as proportional to the local air concentrations by assuming submersion and by use of cloud-correction factors, whereas RIMPUFF invokes full cloud integration for each time step.
Tests have shown that the two dispersion models produces comparable results if the meteorology is „well-behaved“ (i.e. slowly changing met-conditions and wind fields over smooth or hilly terrain without pronounced orographical structures). This is because they are both producing a meandering Gaussian plume in this limit.
CPU requirements on a HP 9000 C200 workstation is about 2.5 minutes for LSMC+ATSTEP and about 7.5 minutes for LSMC+RIMPUFF to produce a 24 hours dispersion prognosis with a 5 hour source term release with ½-hourly updates. Archiving and loading of results and graphics in RODOS extends the computing time to 8 minutes (with ATSTEP) and 20 minutes (with RIMPUFF).
ATSTEP and RIMPUFF interfaces identically to „shared memory“ in RODOS whereto they both provide ground-level air concentrations (in [Bq/m3]) and concentration of wet and dry deposited isotopes (in [Bq/m2]) and ground level gamma dose rates (in Grays [Gy/s]) for display and subsequent use by the other modules of the RODOS system.
The long-range model chain: LRMC
The meso/long range dispersion model integrated id the Hybrid Lagrangian-Eulerian model MATCH nested with the local scale radiation dose models RIMPUFF, ATSTEP and DIPCOT.
The European-scale long-range dispersion model we selected for system-integration with MET-RODOS is the operational MATCH code developed by the Swedish Meteorological and Hydrological Institute (SMHI). This particular code has previously demonstrated its potential with real-time back-fitting and data assimilation [23,24,25].
MATCH [16] is a 3-dimensional hybrid Lagrangian-Eulerian atmospheric transport model. The model grid in the horizontal and vertical is defined by the input numerical weather prediction data, and thus
66 automatically adapted to changes in the input weather data grid. In general most numerical weather prediction models use terrain following vertical co-ordinates for which MATCH is adapted. The model solves numerically the continuity equation for reactive or non-reactive species, including transport, turbulent mixing in the boundary layer and deposition by dry deposition processes and wet fallout. The transport is treated by a mass conservative, positive definite advection scheme, with small phase and amplitude errors, and boundary layer mixing is described by a mass consistent implicit vertical diffusion scheme based on K-theory. The model has modules for nesting local scale outputs where the local scale cloud puffs are in a seamless way transformed to a particle clouds initially treated by a Lagrangian particle model which initialise the Eulerian long range module of MATCH. The flexible design enables handling of an arbitrary number of radioactive nuclides.
Automatically given its source terms by RIMPUFF on the border between the local and long range scale (20 km from the source) MATCH has been integrated and runs now in RODOS on meteorological NWP data downloaded from DMI-HIRLAM and stored in the Real-Time Numerical Weather Forecast Data Base FCASTDB. That seamless interface of an Eulerian long-range model with the outputs from RIMPUFF has earlier demonstrated in [26].
MATCH is configured to work with a subset of the DMI-HIRLAM specific terrain following vertical layers. A typical grid selection is 100x90 grid-cells in the horizontal and 13 vertical layers. The extent of this grid does not permit storage of results in the RODOS database (besides that the projection is in spherical co-ordinates and not in UTM). Output from the MATCH model is therefor stored in a special packed file format (WMO GRIB standard) and visualised directly in RoGIS without intermediate storage in the RODOS database. Boundary layer parameters like sensible heat flux, friction velocity and boundary layer height, that are important for description of stability and turbulent mixing, are determined by a scheme based on near surface temperature, cloudiness, sea surface temperature etc.
An intensive integration of MATCH with DMI-HIRLAM model outputs has been achieved within MET- RODOS.
Vertical diffusion is for the convective case described from a determination of the turn-over-time in the boundary layer based on similarity theory, and for the neutral and stable case from ordinary eddy diffusivity K-theory. Dry deposition rates are dependent on the atmospheric stability as well as the property of the underlying surface. However, at present only physiographical information that is described by the DMI-HIRLAM is used which contains rather limited information about the surface characteristics.
Real-time interactive visualisation based on VIS5D
Outputs from the model chains (ground level concentrations and dose rates) are available to the shared memory data bases of the RODOS system and are as such displayable using the RODOS system’s build- in graphics system ROGIS.
However, in order to provide the MET-RODOS User’s with full three-dimensional graphical access to the vast amount of weather information and atmospheric dispersion products, MET-RODOS is also interfaced with the interactive visualisation program VIS5D. This program gives the MET-RODOS users access to a real-time display and animation feature based on available weather forecasts, winds and dispersion predictions.
VIS5D is a freeware system for interactive visualisation of large 5-D gridded data sets such as those produced by e.g. DMI-HIRLAM. VIS5D provides instant images of vector plots, ISO-surfaces, contour line slices, coloured slices, volume rendering etc. of data in a 3-D grid, then rotate and animate the image in real time. VIS5D is set up in MET-RODOS to visualise the DMI-HIRLAM provided medium and long-range meteorological forecast data downloaded in the real-time numerical weather prediction data base FCASTDB. It can be set up to run immediately following a new set of NWP forecast data have been downloaded and archived in the database. VIS5D features also real-time display and animation of long-range trajectories (forward and backward). Trajectories associated with source points within the
67 European Continent have in this way been made readily available in MET-RODOS. It should be noted, however, that the treatment of the vertical co-ordinate by VIS5D is only rudimentary, and the trajectories should accordingly be used as qualitative guidelines only. The VIS5D source code is public domain freeware, and it has been installed and set up to run in parallel with the MET-RODOS system for multi-dimensional data visualisation purposes.
3.3.2 The DMI-HIRLAM numerical weather prediction model and data transfer
The HIgh Resolution Limited Area weather forecasting system (HIRLAM) is a primitive-equation based Numerical Weather Prediction (NWP) model using a grid-point representation with second-order difference approximations for the spatial derivatives. The horizontal grid is a regular spatially staggered latitude/longitude grid (the Arakawa C grid) in a rotated spherical co-ordinate system. The vertical co-ordinate is a terrain-influenced hybrid co-ordinate, which near the surface is identical with the sigma co-ordinate (σ = p/ps), and approaches the pressure p with increasing height (the surface pressure is denoted ps). The HIRLAM project was initially started by the Nordic countries and the Netherlands (Machenhauer, 1988; Machenhauer et al., 1991; Källén et al., 1996). The project is pt joined by Ireland, and partly by France and Spain.
The DMI-HIRLAM model [30] is the operational version of HIRLAM at the Danish Meteorological Institute (DMI), - it runs around the clock on four different limited areas, cf. Figure 4. The boundary fields for the large-area version are obtained from the global model run by the European Centre for Medium-Range Weather Forecast (ECMWF). The smaller version covering Europe is nested in the large-area version, which provides the boundary values. The horizontal resolutions for the two versions are 0.45° (46 km) and 0.15° (16 km), respectively, and the forecast lengths are 60 and 48 hours, respectively. The models have the same vertical resolution (31 hybrid levels). For a standard atmosphere, the vertical levels are presently located at 33, 106, 188, 308, … meters height. There are nine levels available in order to resolve a typical daytime atmospheric boundary layer of 1.5-km height. Besides, data includes parameters at the surface (ground). The models output data at hourly intervals. The DMI-HIRLAM forecasting system consists of pre-processing, analysis, initialisation, forecast, post-processing and verification. The model versions are run with their own 6-hourly data-assimilation cycle.
Figure 5: Operational DMI-HIRLAM model domains. For the model version covering Europe, the horizontal resolution is 16 km, and 48 hour forecasts with hourly time resolution are produced four times a day.
68 Different versions of HIRLAM run operationally around the clock at a number of European meteorological services. From these services, HIRLAM forecasts of e.g. wind, precipitation and stability can be made available through separate agreements for on-line transfer to RODOS users via e.g. dedicated (private) point-to-point digital telephone networks (ISDN), or via existing computer networks (Internet).
On-line transfer of DMI-HIRLAM data has been tested thoroughly through the project period. Since January 1999, data transmission has taken place on a daily basis (with updates four times a day) between DMI (Copenhagen) and Risø (Roskilde), and between DMI and FzK (Karlsruhe, Germany). Besides, transmission has been tested between Risø and Demokritos (Athens, Greece), and between Risø and the University of Leeds (Leeds, UK).
TABLE 2: DMI-HIRLAM DATA SETS FOR MET-RODOS LOCAL-SCALE AREAS. single-level fields: precipitation intensity, boundary-layer height, sensible heat flux, momentum flux, fraction of land, roughness length multi-level fields: geopotential height, wind speed, wind direction, virtual potential temperature, specific humidity
TABLE 3: DMI-HIRLAM DATA SETS FOR THE MET-RODOS EUROPEAN-SCALE AREA. single-level fields: surface geopotential, surface pressure, sea surface temperature, surface temperature, 2-meter temperature, 2-meter specific humidity, 10-meter u wind component, 10-meter v wind component, fraction of ice, fraction of land, albedo, dynamic (sea) surface roughness, climatological surface roughness, surface sensible heat flux, surface latent heat flux, surface momentum flux, accumulated stratiform precipitation, accumulated convective precipitation, boundary layer height multi-level fields: u wind component, v wind component, temperature, specific humidity
Figure 6a shows DMI-HIRLAM predicted boundary layer heights over Europe on August 20, 1996, at 1300 UTC as it appears downloaded in MET-RODOS using VIS5D graphics.
Figure 6b shows the corresponding predicted precipitation field over Europe at the same time.
Figure 6c shows a sub-set (40 × 40 grid points or ~ 1000 km × 1000 km) of the 10 metre surface winds over „greater“ Denmark at the same time. The inserted „black box“ over Northern Zealand and Copenhagen defines the other bounds of the local-scale-nested grid shown in Figures 2 a-c.
Figure 6 (a-d):
(a) DMI-HIRLAM predicted boundary layer heights over Europe on August 20, 1996, at 1300 UTC as it appears in MET-RODOS using VIS5D graphics. 69
(b) DMI-HIRLAM precipitation field over Europe on August 20, 1996, at 1300 UTC.
70 Figure 6, cont.:
(c)
Sub-set (40 × 40 grid points or ~ 1000 km × 1000 km) of surface (10 m) winds over Denmark on August 20, 1996, 1300 UTC. The inserted „box“ over Northern Zealand defines the bounds of the local scale model chain nested grid in (d).
(d)
DMI-HIRLAM wind vectors over the local scale grid (Northern Zealand- Denmark).
3.3.3 Modes of operation
This section discusses the WG2-envisioned strategy for daily operation of the MET-RODOS system. Assuming that a private (dedicated) standard 64 Kbit per second ISDN line is used for data transmission, the transfer of new +48 Hr forecasts at 16 km horizontal resolution and hourly time resolution for a RODOS local-scale area of 120 km × 120 km takes a few minutes. For the long range (European) scale, the transfer of new +48 Hr forecast data at 50-km horizontal resolution and three- hourly time resolution will take about an hour. In case the transmission line is shared with other users, the transmission time will of course depend on the current load. And for a transmission route through a number of nodes, the transmission time is set by the weakest link. Updated forecasts are available at every +6 hours.
It is envisioned that the local-scale atmospheric model chain will run „around the clock“ in the emergency centres and automatically be updated with new meteorology from both tower networks and forecasts in the „Alert State: Normal“ mode.
Display windows of dispersion from a potential local sources can in this way be visualised instantly (calculated on the basis of a „unit release“), so that the present „dispersion situation“ is always at hand for the RODOS operators and the decision-makers. This continuous „Normal“ mode of operation also
71 ensures continuous exercising of the data transfer systems and some quality assurance of the meteorological measurements involved.
During „elevated alert states“, or during exercises, special trained personnel will have to convene in the emergency room for manually taking control over the MET-RODOS system. Their tasks will be to start up the programs for the long-range dispersion and to assist with data- assimilation and back- fitting procedures on the local scale, and to provide realistic source terms, and to critically evaluate and update the MET-RODOS dispersion forecasts etc.
The dispersion models RIMPUFF and ATSTEP are both able to run according to the different modes of the RODOS system, cf. Table 4:
TABLE 4: LSMC MODES OF OPERATION:
Automatic mode: This mode is the real-time emergency mode. It comprises the diagnosis mode and the automatic prognosis mode of the LSMC. On-line measured (10 minutes cycle) and forecasted (1 hour resolution) meteorological data are transferred via the RODOS real-time database to the LSMC. Together with release information every 10 minutes an update of the actual radiological situation is calculated and dispersion and contamination prognoses for up to 24 hours are performed.
Interactive mode: This mode is off-line and not necessary real-time. For interactive simulations of accident scenarios the LSMC can be used in the prognosis mode. The release data, meteorological data, timing, and all other important data, are put into the system via user input windows and then a release and dispersion scenario covering 24 hours by 48 half hour steps is calculated.
72
3.3.4 Model evaluation history Codes and modules selected for the atmospheric model chain have all previously been evaluated experimentally during full-scale field tests, in addition they are now being quality assured, integrated and documented according to the specifications set out by the overall RODOS concept, cf. [31,32,33].
Near-range models - evaluation
Non-homogeneous terrain (Land-water-land):
RIMPUFF has previously been extensively evaluated with data from several non-homogeneous terrain experiment’s - e.g., the Øresund Experiment’s during 1982-1984 [14,34].
Hilly terrain:
A comprehensive field study "MADONA" (after: Meteorology And Diffusion Over Non-uniform Areas) were conducted over gently rolling hills near Porton Down in England in 1992 [35]. Several accident-simulations were recorded at high temporal resolution and with high spatial details using remote lidar sensing techniques for comparison of data with modelled diffusion patterns. A computerised near-range atmospheric dispersion model-training module has especially been made for RODOS [36, 37]. The MADONA data set is available on CD-ROM [35].
Mountainous terrain:
A series of 14 full-scale dispersion experiments was carried out during the 1990 Guardo trials in Northern Spain. They now form part of the experimental database for evaluation of the near-range model chain over complex terrain. Actual wind and turbulence and smoke plume measurements (using lidar remote sensing) were recorded in real-time and used as input data for a series of simulations made with the combined local scale model chain: PAD-LINCOM-RIMPUFF, cf. [38,39].
Long-range models - evaluation
Two long-range „European Tracer EXperiments“ by name ETEX were conducted in 1994 in continuation of the Chernobyl-triggered Atmospheric Transport Model Evaluation Study (ATMES). Sponsors were the EU, the World Meteorological Organisation (WMO) and the International Atomic Energy Agency (IAEA). ETEX was conducted to evaluate existing operational meteorological long- range models to forecast – in real time – air concentrations from a ground-based point source. 168 sampling stations as it dispersed over Europe in a four days course monitored the tracer gas cloud.
The MET-RODOS long-range transport model MATCH is participating in the ETEX evaluation procedure [40,41], cf. Figure 7. Also the Danish Emergency Response Model of the Atmosphere (DERMA), [42,43,44] participates in these model evaluations based on DMI-HIRLAM data. In a preliminary model evaluation study based on measurements from 86 sampling stations during ETEX-1 these models obtained very high scores compared to most others. Comparison which observations from the Chernobyl case shows good ability to reproduce the measured concentrations, depositions and development of gamma dose rates [45]. This is indicative of high performance both of MATCH and DMI-HIRLAM long-range forecast system.
73 Figure 7: Match simulation of the etex-1 tracer cloud which shows both horizontal and vertical extend at 12 UTC on October 25 1994 [941025.12], corresponding to 44 hours after the start of release near Rennes in France.
The ETEX cloud’s vertical distribution is shown in a North-South vertical cross-section, which also reveals the vertical grid of the match model. Concentration are in [ng/m3], see legend. The four smaller figures inserted below show the position of the cloud at four consecutive 12-hour intervals: [941024.00; 941024.12; 941025.00 and at 941025.12]
3.3.5 The RODOS interfaced complex terrain module
The RODOS interfaced Complex Terrain Module is provided to handle cases of pollutant dispersion over terrain of severe complexity. “Interfaced” as opposed to “integrated” means that this module has been connected to the RODOS system with respect to its input and output products (such as source terms, dispersion products and graphics) - but the modules time (cycle) and mode control is external (stand alone). The following complex terrain codes have been interfaced with RODOS:
• DELTA modules
– GAIA: topography simulator
– HELIOS: shaded ground surfaces
74 – HYDRO: water runoff trajectories
• FILMAKER: meteorological pre-processor
• ADREA-diag: Mesoscale diagnostic meteorological model
• ADREA-prog: Mesoscale prognostic meteorological model
• DIPCOT Lagrangian dispersion and radiation doses
• ADREA-disp: Eulerian dispersion and radiation doses
The sequence of operation in RODOS of the modules of the complex terrain model chain is illustrated in Figure 8.
– Geographical data – Grid details
Topography DELTA simulation
Weather input files: – HIRLAM – ECMWF – ADREA-prognostic – Observation network
Weather FILMAKER pre-processing
Wind flow ADREA-I ADREA-diag
Dispersion, deposition and ADREA-disp DIPCOT radiation doses
Figure 8: Sequence of operation of Complex Terrain Model Chain
DELTA/GAIA
DELTA/GAIA is a topography simulator [46]. Its aim is to determine the characteristics of the air/soil and/or air/water interaction zone, such as ground inclination, area, ground roughness, albedo and land- use. These parameters are important for the air/ground mass and energy exchange and pollutant dispersion computations.
The user selects the location, dimensions and discretization of the computational domain. The DELTA/GAIA module uses terrain elevation and land-cover data to produce a simulation of the ground surface using adjacent triangular surfaces, each one having an appropriate roughness and albedo,
75 according to the land cover. It also calculates the volume and surface porosity’s of the adjacent to the ground computational cells defined by the user.
The input data from the RODOS data base are the 3-D computational grid (number of rectangular cells in each direction and the cells sizes), the computational domain location, the user’s options concerning methods of calculation and output, the terrain elevation data and the land cover. Optionally, the terrain elevation and the land cover can be read from external files.
The output archived in the RODOS database to be used by the subsequent meteorological and dispersion models includes:
• the co-ordinates of computational cells,
• the mean ground elevation value per cell,
• the ground elevation value at the cells interfaces,
• the ground roughness per cell,
• the eventual coastline co-ordinates and orientation,
• and the surface and volume porosity’s per ground cell and the inactive cells.
The output that is stored in external files is the boundary surfaces areas and orientation for each computational cell.
The calculated mean elevation value per cell is connected to the RODOS geographical data base and the Graphics System to be displayed on the map of the area under consideration.
DELTA/HELIOS
The DELTA/HELIOS [46] model identifies shaded ground surfaces by the topography, based on the sun’s position on the hour of the day. These results can be used by the prognostic meteorological model ADREA-prog to take into account differential ground heating.
The input data include boundary surfaces per computational cell (external file created by DELTA/GAIA) and the month, day, hour and time intervals for calculation.
The output data stored in a file to be used by the meteorological model are the characterisation of each triangular surface as illuminated or shaded by the topography as function of time.
DELTA/HYDRO
It calculates linear water runoff trajectories over the topography [46]. The input data include the triangles describing the topography (external file produced by DELTA/GAIA) and the location(s) of precipitation.
The output data are stored in an external file and consist of over-land flow line trajectories.
FILMAKER
FILMAKER is a meteorological pre-processor [47]. It takes as input weather observational and/or forecasting data from national and international weather services or individual meteorological stations, which cover the area of interest. The output of FILMAKER is complete meteorological information on a pre-requested three-dimensional grid and can be used by other codes of weather prognosis and pollution dispersion character.
76 FILMAKER has been especially designed for treating cases with highly irregular topography, as steep slopes and valleys, sparse meteorological stations, and thermally induced phenomena as sea or valley breezes.
The input data that are loaded from the RODOS data base are the computational grid, the domain location, the terrain elevation per cell, the roughness per cell, the eventual coastline co-ordinates and orientation (from DELTA/GAIA) and the user options about input data, methods of computation and output.
Input data read from files are the co-ordinates of the meteorological stations or the grid points of weather models and the meteorological data (wind velocity, temperature, humidity, precipitation).
The output archived in the RODOS data base are gridded meteorological parameters given as function of time: wind velocity in horizontal direction, temperature, diffusion coefficients in three directions, mixing layer height, Monin-Obukhov length, stability category, friction velocity, convective velocity, pressure, precipitation intensity, humidity, synoptic wind.
The calculated wind velocity field is connected to the RODOS geographical database and the Graphics System to be displayed as a vector plot on the map of the area under consideration.
ADREA-prog
ADREA-prog [48,49] is a mesoscale meteorological prognostic model, that solves the conservation equations of mass, momentum, energy and humidity for the air as well as the ground heat transfer. Turbulence is modelled through the eddy viscosity/diffusivity approach, solving an extra transport equation for the turbulent kinetic energy and using an anisotropic length scale. The code is particularly suitable for a terrain of high complexity. The volume and surface porosities concept that is used in the boundary cells allows a detailed description of the ground boundary surface without an increase in the problem complexity. The numerical scheme for the solution of the transport equations used in the ADREA codes is described in [50].
The ADREA-prog model offers the option of one-way nesting of a coarser grid domain towards a finer grid domain. A finer grid domain, called LOCAL, can be nested completely within the MACRO domain of a coarser resolution. The nesting procedure allows any effects that may occur in a larger scale than that of the LOCAL domain to be captured.
In the framework of RODOS, ADREA-prog have been used in the study of different meteorological scenarios in the meso-scale level.
The input data loaded from the RODOS data base includes:
• the “LOCAL” domain inactive cells,
• the “LOCAL” domain cells volume and surface porosities,
• the “MACRO” grid (number of cells and coordinates),
• the “MACRO” domain topography,
• the “MACRO” domain roughness (provided by DELTA/GAIA),
• and the “MACRO” domain meteorological data (wind velocity, temperature, humidity provided by FILMAKER).
77 The input data read from external files are the user options about e.g. modelling, boundary and initial conditions and output, the “LOCAL” domain computational grid and boundary surfaces area and orientation per cell (from DELTA/GAIA) and the eventual locations of sensors for a specified output.
The output archived in the RODOS database to be incorporated by the subsequent dispersion models includes the wind velocity, stability category, mixing layer height, Monin-Obukhov length, pressure, humidity, temperature, friction velocity, convective velocity, and the three-dimensional diffusion coefficients.
The calculated wind velocity field is connected to the RODOS geographical database and the Graphics System to be displayed as a vector plot on the map of the area under consideration.
ADREA-diag
ADREA-diag [51,52] is a mesoscale diagnostic meteorological model. Its purpose is to produce mass- consistent wind fields over complex topography in the “LOCAL” domain. The input wind fields are provided either by FILMAKER or by ADREA-prog run. The ADREA-prog output from a larger spatially domain or a domain of coarser discretisation needs to be processed by ADREA-diag. The FILMAKER output is processed by ADREA-diag for mass-consistency.
The input data loaded from the RODOS database is:
• the “LOCAL” domain inactive cells,
• the “LOCAL” domain cells volume and surface porosity’s,
• the “MACRO” grid (number of cells, co-ordinates),
• the “MACRO” domain topography,
• the “MACRO” domain roughness (provided by DELTA/GAIA),
• and the “MACRO” domain meteorological data (wind velocity, temperature, humidity, stability, mixing height, Monin-Obukhov length, friction velocity, convective velocity, diffusion coefficients and pressure provided by FILMAKER or ADREA-prog).
The input data read from external files include the user options about e.g. modelling, boundary and initial conditions and output, the “LOCAL” computational grid, the boundary surfaces area and orientation per cell (provided by DELTA/GAIA).
The output archived in the RODOS database is the mass-consistent wind velocity field.
The calculated wind velocity field is connected to the RODOS geographical database and the Graphics System to be displayed as a vector plot on the map of the area under consideration.
DIPCOT
DIPCOT is a Lagrangian dispersion and radiation doses model for complex terrain applications [53, 54]. It includes one puff and two types of particle dispersion models. DIPCOT uses topographical and meteorological data given at a 3-D grid and is capable of simulating dispersion of a number of pollutants from multiple point sources. In the case of buoyant point sources, the model performs plume rise calculations. The code includes models for dry and wet deposition of pollutants. Three types of input data, regarding the source characteristics, topography and meteorology are necessary for the simulations.
78 The input data loaded from the RODOS data base are the terrain elevation, the roughness, the grid of the meteorological parameters (provided by DELTA/GAIA), the meteorological parameters (provided by FILMAKER, or ADREA-diag, or ADREA-prog), the dose factors for radioactive pollutants and the user options about the selected modelling approach.
The input data read from external files involve the pollutant sources (co-ordinates, inventories, number of released puffs), the output grid, observation points and output times, the radioactive decay half- lives, the radioactive decay energies, the dry deposition velocities and the wet deposition factors.
The output archived in the RODOS data base, to be used by the subsequent countermeasures modules are: near-ground instantaneous and time-integrated air concentrations, dry and wet deposition, radiation dose rates and doses from cloud and ground, half-hour dose sections and potential human organ doses.
The output stored in external files consists of near-ground instantaneous and time-integrated air concentrations, dry and wet deposition, doses and dose rates from cloud and ground.
DIPCOT is also connected to the RODOS geographical database and Graphics System to display the results of the calculations on the map of the area of interest.
ADREA-disp
ADREA-disp is a mesoscale Eulerian dispersion and radiation doses model suitable for complex terrain applications [55, 56]. It solves the mass conservation equation for the pollutants under consideration. Diagnostic or prognostic models as filmaker, or adrea-prog provide the required meteorological data. ADREA-disp includes a diagnostic wind model thus, the input wind field does not have to be mass consistent for the computational domain and discretization.
The model has “one-way nesting” capabilities both for the concentration and meteorological variables.
ADREA-disp can incorporate a number of pollutants from multiple sources and source configurations (point and area sources). Dry and wet deposition of the pollutants is also taken into consideration.
The input data loaded from the RODOS database consist of:
• the “LOCAL” domain inactive cells,
• the “LOCAL” domain cells volume and surface porosities, the “MACRO” grid (number of cells, coordinates),
• the “MACRO” domain topography, the “MACRO” domain roughness (provided by DELTA/GAIA),
• and the “MACRO” domain meteorological data (wind velocity, temperature, humidity, stability, mixing height, Monin-Obukhov length, friction velocity, convective velocity, diffusion coefficients and pressure provided by FILMAKER, or ADREA-prog).
The input data read from external files include: the user options about e.g. modelling, boundary and initial conditions and output, the computational grid (number of cells, co-ordinates provided by DELTA/GAIA), the boundary surfaces area and orientation per cell (DELTA/GAIA), the pollutant sources (co-ordinates, inventories),
79 and the radioactive half lives, the energies per disintegration and the dose factors.
The output data archived in the RODOS data base to be used by subsequent countermeasures modules are: near-ground instantaneous and time-integrated air concentrations, dry and wet deposition, radiation dose rates and doses from cloud and ground, half-hour dose sections and potential human organ doses.
ADREA-disp is also connected to the RODOS geographical data base and Graphics System to display the results of the calculations on the map of the area of interest.
3.3.6 Complex terrain model chain evaluation The complex terrain model chain has been evaluated using experimental data of atmospheric dispersion over complex topography [57].
Examples of some geographical areas where DELTA/GAIA has been applied to provide topography data for wind flow and dispersion calculations are: the Attiki peninsula in Greece [58], central Peloponese in Greece [59], a region in central Alps[60], a region in the Rocky mountains in USA [61], the island of Sicily in Italy and the Mediterranean coast of eastern Spain [62].
The application of the module DELTA/GAIA and DELTA/HYDRO to the Attiki peninsula topography with some hypothetical precipitation is illustrated in Figure 9.
Applications of FILMAKER for processing weather data for subsequent dispersion calculations include conventional power plants in mountainous regions as in central Peloponese, Greece [59], several islands in the Aegean Sea, a region in Western Spain [63].
ADREA-prog has been successfully applied to a number of cases of thermally induced wind systems over regions of complex topography. These include the Attiki peninsula Greece [58], the Mediterranean coast of Eastern Spain [62], the Rocky Mountains, USA [61]. In all these applications ADREA-prog has been validated by comparing the time histories of the predicted wind velocities and near ground air temperatures with available observations. Statistical indices, such as mean bias and root mean square error, have also been calculated to evaluate the performance of the model.
80
Figure 9: Topography simulation with triangles by DELTA/GAIA and some water flow trajectories by DELTA/HYDRO for the Attiki peninsula (Greece).
The predicted near-ground wind velocity field over the eastern coast of Spain is shown in Figure 10. Some comparisons of model predictions with observations for the same application are depicted in Figure 11.
Figure 10: ADREA-prog predicted sea breeze formation.
81
9 360 )
-1 8
n 300 7 o 6 i 240
5 rect 180 4 3 120 nd di 2 nd speed (m s Wi 60 1 Wi 0 0 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 Time (LST) Time (LST) Measured ADREA-I Measured ADREA-I
Figure 11: Comparison of ADREA-prog predicted wind speed and direction with measurements for a near ground station.
DIPCOT has been extensively validated against experimental data.
Some of the applications include:
• the TRANSALP dispersion experiment in central Alps [60],
• a power plant in central Peloponese [59],
• a model inter-comparison exercise for a power plant in Spain [63],
• several power plants in islands of the Aegean sea,
• the Kincaid power plant in the USA[64].
The performance evaluation of DIPCOT has followed well-established and documented methodologies (Model Validation Kit [65, 66, and 67]. These methods include comparisons of maximum concentrations, fractional bias, normalised mean square error, fraction within a factor of 2, scatter plots of statistical performance measures, box plots of residuals versus meteorological parameters. On Table 5, some statistical measures indicating the DIPCOT performance can be seen calculated for the Kincaid experiment.
Validation of the ADREA-disp model has been based on comparisons of calculated near ground pollutant concentrations with measurements. An example of such an application is the investigation of air pollution in Athens, Greece, caused by car traffic and industrial activities [68]. Some results are presented in Figure 12.
Arc-wise maximum method Near Centreline Method, QI=2 or 3, n=586 n=1896
FACT2 0.454 0.325
FB -0.203 0.335
-0.623 0.210 -0.0067 0.717
NMSE 2.14 4.24
0.945 3.34 1.72 6.8
Table 5 Statistical measures indicating DIPCOT performance for Kincaid experiment.
82 Maximum NOx concentrations
1200 )
3 1000
g/m 800 µ 600
400
Calculated ( 200
0 0 200 400 600 800 1000 1200 Observed (µg/m 3)
Figure 12: Calculated near-ground NOx concentrations and scatter plot of calculated versus observed concentration for various stations.
3.3.7 References 1. Mikkelsen, T. and F. Desiato (1993). Atmospheric dispersion models and pre-processing of meteorological data for real-time application. Radiation Protection Dosimetry Vol. 50, Nos. 2-4, pp 205-218.
2. Mikkelsen, T., S. Thykier-Nielsen, P. Astrup, J. M. Santabárbara, J.H. Sørensen, A. Rasmussen, L. Robertson, A. Ullerstig, S. Deme, R. Martens, J. G. Bartzis and J. Päsler-Sauer: ‘MET-RODOS: A Comprehensive Atmospheric Dispersion Module’, Radiat. Prot. Dosim. 73 (1997) 45–56
3. Mikkelsen, T., S. Thykier-Nielsen, P. Astrup, S. Deme, J. Havskov Sørensen, A. Rasmussen, J. Päsler-Sauer, T. Schichtel, W. Raskob, R. Martens and L. Robertsson (1998). Functionality Specification for the Local Scale Model Chain LSMC in RODOS; RODOS(WG2)-TN(98)03.
4. Mikkelsen, T., S. Thykier-Nielsen , P. Astrup, S. Deme, J. Havskov Sørensen , A. Rasmussen, J. Päsler-Sauer , T. Schichtel , W. Raskob and R. Martens (1999): User’s manual for the Atmospheric Dispersion Module:MET-RODOS; RODOS(WG2)-TN(99)10.
5. Astrup, P., T. Mikkelsen and S. Deme (1999) MET-RODOS: Meteorological Pre-processor Chain RODOS(WG2)-RP(99), [Submitted to: Journal of Physics and Chemistry of the Earth, May 1999].
6. Mikkelsen, T.; Thykier-Nielsen, S.; Astrup, P.; Santabárbara, J.M.; Havskov Sørensen, J.; Rasmussen, A.; Deme, S.; Martens, R., An operational real-time model chain for now- and forecasting of radioactive atmospheric releases on the local scale. In: Air pollution modeling and its application 12. 22. NATO/CCMS international technical meeting, Clermont-Ferrand (FR), 2-6 Jun 1997. Gryning, S. -E.; Chaumerliac, N. (eds.), (Plenum Press, New York, 1998) (NATO Challenges of Modern Society, 22) p. 501-508.
7. Astrup, P. (1998) Local Scale Model Chain - Input Description. RODOS (WG2)-TN(98)06
8. Troen, I. and de Baas, A.F. (1986): A spectral diagnostic model for wind flow simulation in complex terrain. In: Proceedings of the European Wind Energy Association Conference & Exhibition, pp.37-41, Rome, 1986.
9. Moreno, J., A.M. Sempreviva, T. Mikkelsen, G. Lai and R Kamada (1994). A spectral diagnostic model for wind flow simulation: extension to thermal forcing. In proceedings of the: Second International Conference on Air Pollution, 27-29 September 1994, Barcelona, Spain. Eds. J.M. 83 Baldasano, C.A. Brebbia, H. Power and P. Zanetti, Computational Mechanics Publications, Southhamton, U.K., Vol II, pp 51-58.
10. Astrup P., N.O. Jensen and T. Mikkelsen (1996): Surface Roughness Model for LINCOM. Risø report Risø-R-900(EN), ISBN 87-550-2187-5, ISSN 0106-2840; 30 pp. Available on request from: Information Service Department, Risø National Laboratory, e-mail: [email protected]
11. Astrup P., N.O. Jensen and T. Mikkelsen (1997): A fast model for mean and turbulent winds characteristics over terrain with mixed surface roughness. Radiat. Prot. Dosim. (1997) Vol. 73 p. 257-260
12. Martens, R., K. Massmeyer, T. Sperling and F. Steffany (1999): Description of the Atmospheric flow model MCF (Mass Consistent Flow) Final Version: 5. February 1998; RODOS(WG2)- RP(98)1.
13. Massmeyer, K., and Martens, R. (1991): Regional Flow Fields in Northrhine Westfalia - A Case Study Comparing Flow Models of Different Complexity -.in: Air Pollution Modelling and ist Application VIII, ed. by H. van Dop and D.G. Steyn, Plenum Press, New York, pp. 301 - 309, 1991.
14. Mikkelsen, T., S.E. Larsen and S. Thykier-Nielsen (1984). Description of the Risø Puff Diffusion Model “RIMPUFF”. Nuclear Technology, Vol. 67, pp. 56-65.
15. Päsler-Sauer, J. (1997): Description of the atmospheric dispersion model ATSTEP RODOS(WG2)- TN(97)-01
16. Robertson, L., Langner, J. and M. Engardt (1999): An Eulerian limited-area Atmospheric Model “MATCH”. J. Appl. Meteorol., Vol. 38, 190-210.
17. Thykier-Nielsen S., S. Deme and T. Mikkelsen (1998): Description of Atmospheric Dispersion Model RIMPUFF; RODOS(WG2)-TN(98)2.
18. Thykier-Nielsen, S., S. Deme, and E. Láng (1995). Calculation method for gamma-dose rates from Gaussian puffs. Risø-R-775-(EN).
19. Sorokovikova Olga (1998) New Code for Simulation of Radioactive Release Local Atmospheric Dispersion Process, RODOS (WG2)-TN(98)09.
20. Jensen, N.O. and P. Hummelshøj (1995): Derivation of canopy resistance for water vapour fluxes over a spruce forest, using a new technique for the viscous sublayer resistance. Agricultural and Forest Meteorology, Vol 73, pp 339-352.
21. G Päsler-Sauer, J., T. Schichtel, T. Mikkelsen, S. Thykier-Nielsen (1995): Meteorology and atmospheric dispersion, simulation of emergency actions and consequence assessment in RODOS . IN: Proceedings for Oslo Conference on International Aspects of Emergency Management and Environmental Technology. June 18-21 1995. Edited by: K. Harald Drager, A/S Quasar Consultants, P.O. Box 388 Skøyen N-0212, Oslo.
22. Päsler-Sauer, J., O. Schüle, C. Steinhauer (1996): RODOS Prototype Version 2.0 User Guide. RODOS(WG1)-TN(96)09.
23. Robertson, L. and J. Langner (1998): Source function estimates by means of variational data assimilation applied to the ETEX-I experiment. Atmos. Environ. Vol 32, No 24, 4219-4225.
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