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ECOSIM Telematics Applications Project:
Deliverable D04.01

Specification of Monitoring and Modeling Functions
and their Interfaces (updates)

Keywords:
Air quality modeling, Monitoring.
Release 0.2 10 November 1997
Author: Peter Mieth
Edited by: Kurt Fedra




Synopsis
Programme name Telematics Application Programme
Sector Environment
Project Acronym ECOSIM
Contract number EN 1006
Project title Ecological and environmental monitoring and simulation system for management decision support in urban areas
Deliverable number D04.01
Deliverable title Specification of Monitoring and Modeling Functions and their Interfaces (updates)
Deliverable version number 0.2
Work package contributing to deliverable 4
Nature of the deliverable Online Working Document
Dissemination level Limited to Project Participants
Contractual date of delivery PM20 (August 1997)
Actual date of delivery PM20 (online)
10 November 1997 (hardcopy)
Author Peter Mieth
GMD FIRST
Project technical co-ordinator Dr.Kurt Fedra, Environmental Software & Services GmbH
tel: +43 2252 633 050
fax: +43 2252 633 059
E-mail: kurt@ess.co.at





Executive Summary:

This document describes the main modeling and monitoring functions of the ECOSIM demonstrator. The system is able to compute and to analyze the following environmental processes and management scenarios:

  • mesoscale athmospheric processes, including:
    • wind flow over complex terrain including sea breeze effects

    • surface temperature and 3d potential temperature

    • atmospheric stability and diffusion coefficients

    • solar radiation

    • investigation of the influence of land use for the mesoscale meteorology

  • air related processes, including:

    • introducing anthropogenic emissions of sulfur dioxide, nitrogen monoxide, nitrogen dioxide, carbon monoxide and volatile organic compounds at different heights and locations

    • biogenic emissions of volatile organic compounds and nitrogen oxides

    • dispersion calculation of anthropogenic air pollutants and 3d concentration fields of inert or photochemical reactive substances

    • analyzing of related quantities, such as time series, critical load

    • 24 hour forecasting of chemical inert and photochemical reactive air pollutants, e.g. ozone

    • computation of dry deposition fields at the surface

    • emission reduction scenarios changing the strength of a single emission source,of emission classes or areas, changing the diurnal variation

    • emission contents scenarios

    • planning studies, investigating optimal locations for unavoidable emission sources

  • water quality problems, including:

    • groundwater flow and contamination from landfills

    • coastal water pollution due to sewer outfalls and terrestrial sources.

In addition, ECOSIM provides tools to display and analyze environmental monitoring data, acquired from on-line connections to monitoring networks, including

  • air pollution observations, concentration time series

  • water domain monitoring data: temperatures, pollution levels.

The ECOSIM system provide tools to store and manage this monitoring data, analyze them, and uses them for model validation.





Contents

1 INTRODUCTION

2 THE MESOSCALE METEOROLOGICAL MODEL MEMO

3 THE CHEMICAL TRANSPORT MODEL DYMOS

4 THE OZONE FORECAST MODELS REGOZON AND MUSE

4.1 REGOZON

4.2 MUSE

5 INPUT/OUTPUT OF THE ATMOSPHERIC AND DISPERSION MODELS

5.1 Model Input

5.2 Model Output

6 THE OCEAN MODEL POM

7 REFERENCES

1 INTRODUCTION

ECOSIM provides tools for the

  • computation and analyzing of mesoscale meteorological quantities and phenomena:

    • mesoscale wind flow over complex terrain including sea breeze effects

    • surface temperature and 3d potential temperature

    • atmospheric stability and diffusion coefficients

    • solar radiation

    • investigation of the influence of land use for the mesoscale meteorology

  • computation and analyzing of the air quality in the atmospheric boundary layer

    • introducing anthropogenic emissions of sulfur dioxide, nitrogen monoxide,

    • nitrogen dioxide, carbon monoxide and volatile organic compounds at different heights and locations

    • biogenic emissions of volatile organic compounds and nitrogen oxides

    • dispersion calculation of anthropogenic air pollutants and

    • 3d concentration fields of inert or photochemical reactive substances,

    • analyzing of related quantities, such as time series, critical load

    • 24 hour forecasting of chemical inert and photochemical induced air pollutants concentration, e.g. ozone

    • computation of dry deposition fields at the surface

    • emission reduction scenarios,

    • changing the strength of a single emission source, of an emission class or an area, changing the diurnal variation

    • emission contents scenarios

    • planning studies, investigating optimal locations for unavoidable emission sources

    • process and sensitivity studies, varying defined conditions

  • computation and analyzing coastal water

    • releasing of domestic waste into coastal areas and its dispersion

    • considering point source release by rivers or area or line source release (landfills)

    • computing of coastal water temperature changes as potential input for atmospheric models

  • monitoring data

    • analyzing meteorological and air pollution monitoring data: time series,

    • critical loads, diurnal variations, short term and long term trends

    • using of meteorological observations and air pollution monitoring data for

    • initializing the models and providing background information, using of surface

    • observations and upper air soundings

    • information about wind speed, wind direction, cloud cover, humidity,

    • air temperature and pressure, soil and water temperature, mean gradients

    • air pollution observations and

    • access to an air pollution data base

    • using monitoring data for evaluation purpose

In the model domain, the ECOSIM system must be able to realize two major tasks:

(a) scenario analysis and long-term planning (b) short-term forecasting.

Function (a) requires comprehensive models with state-of the art parametrization features. On the other hand, function (b) requires a fast execution of the program. Even with todays fastest supercomputers, model runs using advanced 3D atmospheric models including transport and chemistry need too much time. A compromise allows the use of a somewhat simplified model system for forecast purposes keeping in mind that with a further rapid development of the computer power the use of models of category (a) can be certainly used for forecast purposes.

The appropriate models for the task (a)

  • MEMO (atmospheric simulation) and

  • DYMOS (transport and tropospheric chemistry)

were chosen, for task (b)
  • REGOZON and

  • MUSE.

According to the users requirements, these models cover urban to regional scale phenomena:

  • model domain size: 2,000 km2 to 10,000 km2

  • resolution: 1-10 km2.

It is not intended to use microscale approaches for very local phenomena.

The atmospheric model MEMO computes 3D-fields of meteorological quantities like wind direction, wind speed, pressure, temperature, solar radiation and turbulence coefficients. After an initialisation, its prognostication bases mainly on the computation of the time variability of the surface energy budget. The model is able to compute wind fields in rather complex terrain. Interesting features that might be a matter of investigation with this model are land-sea breeze effects, urban heat islands, or mountain waves.

The model uses orography, land use and meteorological observations as input parameters (e.g. vertical wind and temperature profiles and surface observations). The time-dependent MEMO output serves as an input for the DYMOS model. It computes 3D concentration fields and deposition pattern. It includes a photochemical module for investigation tropospheric ozone production. An additional model input is formed by emission inventories. The emissions are grouped into different classes regarding to their characteristics. For scenario analysis purposes, a user is able to scale the emissions with a desired factor. Rerunning a real episode, the current results of an emission reduction measure can be examined; or computing a hypothetic situation with changed emissions can help to estimate future air quality trends.

The forecast model REGOZON has the capability to run in a fully automatic mode. It has defined interfaces to meteorological observations in the WMO (World Meteorological Organisation) format and can automatic incorporate even synoptic weather forecast. Also, it has a predefined interface to an air pollution monitoring net to use these data as initial values. A typical forecast period is 24 hours. Longer simulations might be possible using reliable synoptic weather forecast.

The coastal water model POM is able to compute ocean the dispersion of pollutants from different source types (rivers carrying domestic waste, landfills). Its prognostic formulation of the thermodynamic processes in coastal water areas allows the computation of the water temperature changes and can serve as an input for atmospheric models to test the coupling of ocean and atmospheric processes for a more reliable environmental simulation.

THE MESOSCALE METEOROLOGICAL MODEL MEMO

The prognostic mesoscale model MEMO describes the dynamics of the atmospheric boundary layer. In the present model version air is assumed to be unsaturated. The model solves the continuity equation, the momentum equations and several transport equations for scalars (including the thermal energy equation and, as options, transport equations for water vapour, the turbulent kinetic energy and pollutant concentrations). The equations are formulated in terrain-following coordinates and solved on a three-dimensional grid. Turbulence and radiative transfer are the most important physical processes that have to be parameterized in a prognostic mesoscale model.

In the MEMO model radiative transfer is calculated with an efficient scheme based on the emissivity method for longwave radiation and an implicit multilayer method for shortwave radiation [Moussiopoulos 1987].

The diffusion terms may be represented as the divergence of the corresponding fluxes. For turbulence parameterizations K-theory is applied. In case of MEMO turbulence can be treated either with a zero-, a one- or a two-equation turbulence model. For most applications a one-equation model is used, where a conservation equation for the turbulent kinetic energy E is solved.

In the prognostic model MEMO initialization is performed with suitable diagnostic methods: A mass-consistent initial wind field is formulated using an objective analysis model; scalar fields are initialized using appropriate interpolating techniques [Kunz 1991]. Data needed to apply the diagnostic methods may be derived either from observations or from larger scale simulations.

Suitable boundary conditions have to be imposed for the wind velocity components, the potential temperature and the pressure at all boundaries. At open boundaries, wave reflection and deformation may be minimized by the use of so-called 'radiation conditions' [Orlanski 1976].

As the original formulation of the radiation conditions merely allows disturbances to propagate out through the boundary, but does not allow information from outside to be imposed at the boundary expanded radiation conditions at lateral boundaries are used [Carpenter 1982].

According to the experience gained so far with the model MEMO, neglecting large scale environmental information might result in instabilities in case of simulations over longer time periods.

For the nonhydrostatic part of the mesoscale pressure perturbation, homogeneous Neumann boundary conditions are used at lateral boundaries. With these conditions the wind velocity component perpendicular to the boundary remains unaffected by the pressure change. At the upper boundary Neumann boundary conditions are imposed for the horizontal velocity components and the potential temperature. To ensure non-reflectivity, a radiative condition is used for the hydrostatic part of the mesoscale pressure perturbation at that boundary. Hence, vertically propagating internal gravity waves are allowed to leave the computational domain [Klemp 1983]. For the nonhydrostatic part of the mesoscale pressure perturbation, homogeneous staggered Dirichlet conditions are imposed. Being justified by the fact that nonhydrostatic effects are negligible at large heights, this condition is necessary, if singularity of the elliptic pressure equation is to be avoided in view of the Neumann boundary conditions at all other boundaries.

The lower boundary coincides with the ground (or, more precisely, a height above ground corresponding to its aerodynamic roughness). For the nonhydrostatic part of the mesoscale pressure perturbation, inhomogeneous Neumann conditions are imposed at that boundary. All other conditions at the lower boundary follow from the assumption that the Monin-Obukhov similarity theory is valid. With the exception of water surfaces (where the temperature is specified) the surface temperature is calculated from the nonlinear heat balance equation.

MEMO has been designed to describe atmospheric features in the mesoscale like sea breeze effects or mountain waves.

3 THE CHEMICAL TRANSPORT MODEL DYMOS

The DYMOS submodel is a three-dimensional grid model designed to compute the concentration of air pollutants in the troposphere. It includes transport processes of advection and turbulent diffusion as well as chemical changes and surface uptake processes. The mathematical formulation is given by the advection-diffusion equation, which represents the mass balance for a species. The solution of this equation has to be computed for every substance. The model employs numerical differencing techniques on the discretized grid given by the MEMO output. An efficient solution technique is the method of fractional steps so that the terms representing different atmospheric processes are solved separately using the most suitable scheme.

The anthropogenic partition of emission rate of a substance can be obtained in different ways:

  • determined by the emission data base for the area, modified by a time dependent emission factor describing seasonal, weekly or diurnal variation of the emission strength,
  • model computation by an emission model (e.g. traffic flow model),
  • on-line emission measurements.

Only a fraction of all substances are primary species. They are emitted by anthropogenic activities or by the ecosystem (biogenic emissions). Others may appear as a result of the chemical reaction scheme.

The deposition rate of a substance is approached by a resistance model. In order to compute ozone a chemical submodul simulates the chemistry of the dry troposphere. This is important because high surface near ozone concentrations are among the harzardous environmental problems.

Ozone is formed in the troposphere through chemical reactions between nitrogen oxides (NOx) and volatile organic compounds (VOC) under presence of solar radiation. Hundreds of substances and thousands of reactions participate in the ozone formation process. That is why the explicit treatment of the chemistry is impossible in a Eulerian dispersion model for the following two reasons:

  • limitation on computer resources,
  • lack of input data.

Thus the photochemical mechanism must be compressed significantly. The chosen chemical reaction scheme is the Carbon Bond Mechanism (CBM-IV).

Reactions with minor importance in the given time scale and typical concentration range were removed from the explicit scheme. The organic compounds are disaggregated based on the carbon bonds of the organic compounds. For example, butene would be split into one olefinic bond (OLE) and two paraffinic bonds (PAR). Some organic species remain explicit because of their special role in the ozone chemistry. CBM IV contains the following primary classes of nonmethane VOC`s:

PAR 1 C-atom carbon single bond
ETH 2 C-atoms ethene
LE 2 C-atoms carbon double bonds, olefine
TOL 7 C-atoms toluole, monoalkylbenzene
XYL 8 C-atoms xylole, C8 aromatic species
FORM 1 C-atom formaldehyde
ALD2 2 C-atoms acetaldehyde, higher aldehydes
ISOP 5 C-atoms isoprene
NR 1 C-atom nonreactive VOC`s

VOC`s are any compounds of carbon excluding carbon monoxide, carbon dioxide, carbon acid, metallic carbides or carbonates and ammonium carbonates. Methane is also excluded from this VOC definition because of its low importance on the ozone production in the urban scale.

The reaction rates could be constant, temperature dependent or radiation dependent (photolysis rates). These variable photolysis rates depend on light intensity and spectral distribution. The parameters are computed internally as a function of longitude and latitude, day of the year, time and fraction of cloud cover.

The mathematical formulation for the chemical reaction scheme leads to a stiff system of ordinary differential equations (ODES). The equations contain a wide variation in the reaction rate constants. The ODES must be solved for every grid box. No matter what numerical algorithm is selected, the solution of such systems remains computationally expensive. On the other hand, a parallel implementation of this code guarantees a relatively high acceleration and allows a much faster execution in comparison with most serial high performance computer systems.

For DYMOS, the user can select the

  • substances he wants to analyse

  • number of vertical output levels

  • the output period

  • the total model duration

  • switches to use biogenic emission modules and deposition calculation.

Additionally, he can scale the emission source levels by the use of factors.

4 THE OZONE FORECAST MODELS REGOZON AND MUSE

4.1 REGOZON

The principle design of the model REGOZON [Mieth 1993] is similar to the MEMO/DYMOS system. A wind field model is coupled with a dispersion model and a chemical module. For the computation of meteorological values and the dispersion, an enhanced version of the Eulerian grid model REWIMET [Heimann 1985] is used. It is based on the conservation laws for impulse, mass, energy and passive constituents comparable to MEMO/DYMOS but with a higher level of approximations. A hydrostatically stratified atmosphere is assumed, which is dry and incompressible.

The model equations are expressed in three vertical layers. The first (surface layer) follows the ground level and has a fixed vertical thickness of 50 m above ground. It is turbulently mixed and its physical behaviour is strongly coupled with the surface characteristics.

Emissions from traffic, from households and from industrial sources with low emission heights are introduced into the surface layer. The second layer (mixed layer) reaches from the upper level of the surface layer to the upper level of the atmospheric boundary layer, up to the mixing height. This layer is also turbulently mixed and shows the characteristic diurnal variation of the thickness of the atmospheric boundary layer. Emissions from higher emission sources, for example high stacks from power stations, enter the mixed layer. The third layer (temporary layer) is located above the mixed layer. It is assumed to be free of turbulence.

Since the atmospheric boundary layer can expand to the suprascale inversion, it is possible for the temporary layer to disappear. It will be recreated when the atmospheric boundary layer sinks. No substances are emitted in this layer but it transports the suprascale background concentrations of ozone and ozone precursor substances above the atmospheric boundary layer.

For the computation of the temperature regime a surface energy budget routine has been added. The computation of the dispersion is carried out immediately after the determination of the meteorological values. The transport model uses the same vertical structure. The dispersion equation is solved in two dimensions but with an allowed vertical exchange according to the determined stability. The chemical changes form a source or sink term in the dispersion equation. The photochemical scheme of CBM IV [Gery 1988] is applied; the chemical module is nearly identical with the DYMOS module.

As characteristic time steps for the solution of the chemical system are much smaller, the chemical computations have been decoupled from the determination of transport. Usually, chemistry is not computed at every transport time step [Mieth 1996]. Dry deposition velocities were computed using a resistance approach as a function of land utilization as described in [Wesely 1988], the computation of biogenic emissions uses the methods from [Pierce 1990]. The forecast system REGOZON is applicable under the following restrictions:

  • relatively flat terrain without strong orographic features and land/see breeze effects

  • fair weather conditions with moderate wind speeds

  • stable, barotropic synoptic situations.

Because of the strong constraints and the rough vertical model resolution, the computational time for the REGOZON model is small. A comparison between this relatively simple model and a nonhydrostatic 3D model with 35 vertical layers [Kapitza 1992] show a similar ozone production for the selected episodes which conform with the above restrictions. The extent of computation time required by the advanced MEMO/DYMOS system is about an order of magnitude higher.

The REGOZON has defined interfaces to meteorological observations in the WMO (world meteorological organisation) format and can automatic incooperate even synoptic weather forecast. Also, it has a predefined interface to an air pollution monitoring net BLUME and the UBA monitoring net to use these data as initial values.

A typical forecast period is 24 hours. Longer simulations might be possible using reliable synoptic weather forecast. For REGOZON, the user can select the

  • substances he wants to analyse
  • the output period
  • the total forecast duration
  • the mode of input data selection
  • the running mode ("real" forecast using dat from today or forecast-like mode using data from a data base)
Additionally, he can scale the emission source levels by the use of factors.

4.2 MUSE

Whereas REGOZON is a combined mesoscale meteorological and dispersion model, MUSE [Sahm 1995] is a dispersion model only, requiring the meteorological quantities usually computed by MEMO. On one hand it enhances the computation time in comparison to REGOZON, on the other hand it could be applied to a lot more situations, because of the lower constrains of the meteorological parts. For example, whereas REGOZON seems to be unable to describe the transport processes related to sea breeze, MUSE is to some extent able to perform this task. The model equations solved by MUSE are those of the fully 3D version of the dispersion model DYMOS. However, the multilayer model MUSE differs from the ECOSIM main dispersion model in the following points:

  • Instead of discretizing the model domain into 10-30 non-equidistantly distributed vertical layers, three layers are being used in the vertical direction. This modeling concept is very similar to the REGOZON model structure. The depth of the lower layer (which practically corresponds to the surface layer) is prescribed and kept constant in time. The depth of the middle layer is permitted to vary following the diurnal variation of the mixing height. The latter is described by Deardorff's prognostic equation [Deardorff 1974] which was shown to lead to realistic results when the variation of the mixing height with time is strongly influenced by surface heating [Pielke 1984]. For periods of stability, an algebraic parameterization based on the friction velocity and the Monin-Obukhov length is applied [Moussiopoulos 1991]. The limit of the upper layer coincides with the fixed domain top. The assumption of a time dependent depth of the middle and upper layer leads to the need of an entrainment term [Ngyen 1989].

  • The description of vertical transport due to turbulent diffusion at the lower limit of the middle layer is based on the turbulent kinetic energy defined at the interface of the lower and middle layers. To avoid unrealistic vertical diffusion rates associated with the growth of the mixing height, one-sided concentration gradients are calculated at this interface. Turbulent diffusion in the upper limit of the middle layer is neglected.

  • Advective transport is described using the scheme designed by Smolarkiewicz [Smolarkiewicz 1984]. The average wind speed in the middle and upper layers is calculated by integrating the fluxes of the corresponding layers of the fully 3D wind field model.

  • The differential equation system in MUSE is solved with a backward difference (BDF) solution procedure (by applying the Gauss-Seidel iteration scheme) [Kessler 1995]. Because of the nature of this semi-implicit algorithm, vertical diffusive transport and chemical transformation of pollutants have to be treated separately.

  • Aspects related to the formation of photochemical oxidants could be analyzed with the multilayer model MUSE in conjunction with the chemical reaction mechanism KOREM (a modified version of the Bottenheim-Strausz mechanism [Bottenheim 1982]). This is a more compressed chemical reaction scheme.

With the above simplifications, the MUSE code becomes approximately 5 times faster than the full 3D version. At the same time, the memory requirements are reduced by 90 %. The model configuration MEMO/MUSE can be used for forecast purposes in more complex terrain than REGOZON under defined conditions.

5 INPUT/OUTPUT OF THE ATMOSPHERIC AND DISPERSION MODELS

5.1 Model Input

An digital elevation model is necessary to compute the wind flow. As the model cannot resolve the subgrid scale features, the orography heights above sea level must be provided in the chosen computation grid as grid averages.

The land use data influences the computation of the thermal properties of the surface and of ground resistance. Additionally, the surface uptake processes and the biogenic emissions are determined by land use parameters.

Because the land use or at least the agricultural use may varying weekly, seasonly or yearly, a regular update may be necessary. The most accurate means of data preparation is the use of satellite images. All land use types significant for the model domain should be included in the inventory.

The following types are distinguished and must be provided in percent per grid:

  • sea water
  • fresh water
  • arid land
  • low vegetation
  • grassland
  • farmland
  • coniferous forest
  • mixed forest
  • deciduous forest
  • sealed area
  • suburban area
  • urban area

The analysis of the air quality of an urban region by means of simulation requires a detailed emission inventory. Especially in order to calculate ozone, which is really a secondary air pollutant, as much as possible information about precursor emissions should be collected. For an ozone calculation, the inventory should contain at least emission rates for nitrogen oxides and volatile organic compounds. For investigating nonreactive transport processes, the user can select his substance of interest.

The anthropogenic emissions can be given as point sources with a concrete coordinate or they can be aggregated on any grid size smaller than the selected computational grid. Usually, the environmental agencies collect the data as yearly emission rates. So the typical unit for an emission source in the ECOSIM emission data base is kg per year. Every emitter can have its own emission height or the emission height of its emission class which can be a concrete height or might be distributed over a couple of levels. The emissions sources are grouped together in order to simplify the accessibility for scenario analysis. At the recent stage the ECOSIM emission inventory distinguishes 7 different emission classes:

  • power stations and large emitters (emissions from high stacks)
  • small industries and crafts
  • households
  • moving traffic (except of city centre traffic)
  • city centre traffic
  • traffic evaporation
  • air port and air craft emissions

The ECOSIM emission inventory must include at least one of these emission classes and one substance. The inventory groups NOx (sum of all nitrogen oxide emissions) and VOC (volatile organic compounds without methane) into one database, respectively. The user can select the appropriate relation between the species (e.g. the NO2/NO split) for every emission class or can simply use the default values reflecting typical conditions.

For episode simulations, and especially for forecast purposes, the dynamic behaviour of emissions must be considered. Every emission class for every substance can have its own diurnal variation pattern for

  • normal working days
  • saturday
  • sunday and holidays.

The system automatically searches for the day of the week and uses its dynamics.

The prognostic model MEMO is a set of partial differential equations in 3 spatial directions and in time. To solve these equations, information on the initial state in the whole domain and on the development of all relevant quantities at the lateral boundaries is required. To generate an initial state for the prognostic model, a diagnostic model [Kunz 1991] is applied using measured temperature and wind data. Both temperature and wind data can be provided as:

  • surface measurings i.e. single measuring directly above the surface (not necessary)

  • upper air soundings i.e. soundings that consist of several (at least two) measurings at different height levels at a constant geographical location (at least one sounding for temperature and wind velocity is necessary)

Information on quantities at the lateral boundaries can be taken into account as surface measurings and upper air soundings.

Air quality models in the urban scale should include mean concentration values from outside the model domain as background data. For example, ozone could exhibit a relatively high large scale background level with a strong diurnal variation near the ground. If these background levels would be neglected, even the computation of additionally ozone production would fail due to the nonlinear character of the ozone chemistry.

For DYMOS, the background values of all relevant substances can be defined separately for every layer. Also, these background values can vary in any desired time interval.

For a number of simulation purposes, there is no knowledge of the time dependencies of the background concentrations. But especially surface ozone, and also its background, shows a rather strong diurnal variation. To allow a more realistic simulation, the model can run on request in a box model background mode. In this mode, the model uses the background values only for initialising the concentrations.

A box model with a assumed infinite horizontal extension performs the determination of the time dependent background values. The vertical structure and the formulation of the vertical processes in the box is similar to the conditions in the model domain; the box model uses averaged meteorological values from the model domain as time depending input parameters.

5.2 Model Output

The MEMO output contains the fields of all relevant meteorological variables such as wind, pressure, diffusion coefficients and temperature. These data serve as input for the DYMOS model which calculates the transport, chemical changes, deposition and biogenic emissions on the basis of the meteorological data. The final output from DYMOS and REGOZON with relevant information for the user consists of the desired concentration field. The user can specify the substance(s) of interest, the output interval and the number of levels from the ground he wants to see. The graphic representation of the results uses all common features like isoline plots, contour plots or grid representation in selected sensitivity band. All simulation results can be stored together with the case descriptor and the input data set.

6 THE OCEAN MODEL POM

The coastal water model is based on a well known academic community model, the Princeton Ocean Model (POM). POM is a sigma-coordinate, 3-D thermodynamically active model that has been primary designed for coastal water studies. The UA group has developed a new module of POM for the dispersion of passive tracers. The scheme can simulate several surface and subsurface sources with different pollutant load.

This module has been tested in several areas and has been proved to be an effective tool for monitoring the fate of tracers in the coastal environment. The sources can be either point-sources (eg. rivers, sewage...) or linear-sources (landfills).

The model grid covers an area 100X100 Km with horizontal resolution of 2Km (50x50 points). The extension and the resolution of the horizontal grid were decided in collaboration with AUT according to the requirements of MEMO. The SST values computed by POM were used by MEMO in a number of sensitivity tests.

The model was initialized using climatological temperature and salinity data provided by MODB (Mediterranean Oceanic Data Base). The data were applied to the model grid using objective analysis techniques. The atmospheric forcing data that drives the model are calculated from climatological monthly means based on the 1980-1988 re-analysis of the NMC (national Meteorological Centre-USA) weather forecast. This data base has been extensively used in modeling efforts in the Mediterranean and has been proved to be the most appropriate for climatological studies. Finally, the bathometric data used in the model runs were provided by the Hellenic Hydrographic Services.

After a close collaboration with ESS, the model has been integrated inside the ECOSIM demonstrator and can be used either locally or through Internet. In order to build the communication interface with the model, the UA group used the input supplied by the user support partners during the Athens meeting (April 2-4, 1997). Using the ECOSIM demonstrator, the end-user can specify:

  • The number of sources that will be added to the model (maximum 100), their location, the flow rate for each one as well as the concentration value for specific pollutants.

  • The surface or subsurface release of tracer

  • The starting month of the integration. With this option, the user automatically specifies the corresponding hydrological and meteorological conditions for the simulation.

  • The output time step

  • The total duration of run in days (maximum 360 days)

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Kessler, Chr. Entwicklung eines effizienten Lösungsverfahrens zur modellmäßigen Beschreibung der Ausbreitung und Umwandlung reaktiver Luftschadstoffe, PhD Dissertation, Universität Karlsruhe, 1995.

Klemp, J.B. and Durran, D.R. (1983) An upper boundary condition permitting internal gravity wave radiation in numerical mesoscale models, Mon. Weather Rev.111, 430-444.

Kunz, R. (1991) Entwicklung eines diagnostischen Windmodells zur Berechnung des Anfangszustandes für das dynamische Grenzschichtmodell MEMO, Diplomarbeit Universität Karlsruhe.

Mieth P. and Unger S. (1993) Szenarienrechnungen und Fallstudien zur Ozonanalyse im Berliner Raum mittels eines mesoskaligen Ausbreitungsmodells. GMD FIRST im Auftrag der Senatsverwaltung für Stadtentwicklung und Umweltschutz Berlin und des Ministeriums für Umwelt, Naturschutz und Raumordnung des Landes Brandenburg, Berlin.

Mieth P. and Unger S. (1996) Comparison of identical emission reduction measures under different meteorological conditions, in Air Pollution IV (ed B. Caussade, H. Power, C.A. Brebbia), Proceedings of the Air Pollution `96 Conference, Toulouse, France, 1996, Computational Mechanics Publications, Southampton, 1996

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