The Air Pollution Model (TAPM)

The meteorological results show that TAPM performs well in a variety of regions (e.g., coastal, inland and generally complex terrain for subtropical to mid-latitude conditions). The pollution results show that TAPM performs well for a range of important phenomena (e.g. nocturnal inversion break-up fumigation; stable, neutral, convective and building wake dispersion; shoreline fumigation; and general dispersion in complex rural and urban conditions). In particular, TAPM performs very well for the prediction of extreme pollution statistics, important for environmental impact assessments, for both non-reactive (tracer) and reactive (nitrogen dioxide, ozone and particulate) pollutants for a variety of sources (e.g. industrial stacks and/or general surface or urban emissions).

In this study, the photochemical smog formation over a heavily industrialized area with complex terrain was investigated. A mesoscale prognostic meteorological and air pollution model was used in combination with data, which were collected by in situ and remote monitoring stations. The area of interest is a mountainous basin in northwestern Greece. The intensive industrial activity in this area has resulted in effects on the air quality of the area, mainly caused by the lignite-fired power stations and the lignite mining operation. The study focused on the dispersion of ozone production due to primary pollutants' emissions from the power stations (PSs). Moreover, the investigation of this external sources' contribution to the ozone concentrations measured in Kozani, the most populated city of the area, is ventured. The results showed a considerable skill of the model in predicting the major mesoscale features affecting the pollutants' dispersion and concentrations in the area of interest. Numerical simulation data of photochemical pollutants were significantly correlated with meteorology during the simulation period. The correlation reveals the most important factors of ozone production, such as solar radiation, temperature, wind speed and topography. The obtained results have contributed to the verification of distant pollutants' transfer from industrial sources.

In this study we present an application of a sensitivity analysis to identify a set of important factors that are allowed to be calibrated in the grid setup of a prognostic meteorological model for laboratory-based simulations. The use of a calibrated grid is of paramount importance for repeated procedures by leaving unnecessary information unprocessed and inevitably reducing run times. Identification and evaluation of sensitivity, importance and uncertainty elements is attempted by the design of a 'good practice experiment' for site-specific calibration. The factors of varied grid size and resolution, based on a one-factor-at-a-time approach, are used for the determination of local sensitivity in the area of interest. A total of five simulations was performed for a grid configuration study that aimed to calibrate The Air Pollution Model.

Computationally efficient sensitivity analysis of a large-scale air pollution model is an important issue we focus on in this paper. Sensitivity studies play an important role for reliability analysis of the results of complex nonlinear models as those used in the air pollution modelling. There is a number of uncertainties in the input data sets, as well as in some internal coefficients, which determine the speed of the main chemical reactions in the chemical part of the model. These uncertainties are subject to our quantitative sensitivity study. Monte Carlo and quasi-Monte Carlo algorithms are used in this study. A large number of numerical experiments with some special modifications of the model must be carried out in order to collect the necessary input data for the particular sensitivity study. For this purpose we created an efficient high performance implementation SA-DEM, based on the MPI version of the package UNI-DEM. A large number of numerical experiments were carried out with SA-DEM on the IBM MareNostrum III at BSC-Barcelona, helped us to identify a severe performance problem with an earlier version of the code and to resolve it successfuly. The improved implementation appears to be quite efficient for that challenging computational problem, as our experiments show. Some numerical results with performance and scalability analysis of these results are presented in the paper.

Air quality assessment in a polluted area with specific and complex terrain features situated in the northwestern part of Romania was made using The Air Pollution Model (TAPM). This is a 3D prognostic model that solves the fundamental fluid dynamics and scalar transport equations to predict both meteorological data and air pollution concentrations. In order to properly assess the concentrations of air pollutants in the studied area, there were taken into account not only the emissions from the activities on the premises of the main industrial platform, but also the contribution from the other pollution sources from the area of interest, such as other industries, residential heating, traffic, dump heaps. The mathematical modeling results, displayed as air pollutant dispersion maps, showed the significant influence of the complex terrain features and of the other pollution sources on the concentration levels in the region, usually associated with the emissions of the main industrial platform.

The main objective of this paper is to study photochemical air pollution in Portugal through the application of the "The Air Pollution Model" (TAPM) (version 2.0), a 3D prognostic model that solves the fundamental fluid dynamics and scalar transport equations to predict both meteorology and air pollution concentrations. The possibility of using a meso-meteorological and dispersion model on an hourly base during an entire year represents an important achievement for air quality studies. The model was applied to Portugal, for the year 2003, to predict ozone concentrations. The simulation domain was 350 x 700 km with nested horizontal resolutions of 30 and 10 km. This domain included the entire Portuguese mainland and some of the surrounding ocean in order to adequately simulate the complexity of the atmospheric flow. Model predictions of meteorological surface parameters, such as wind and temperature, were compared with measurements of six meteorological monitoring sites. The Index of Agreement (IOA) was applied between observations and model results predicted on one year-long meteorological parameters. IOA is greater than 0.50 for all parameters, indicating a good agreement with measurements. Predicted ozone concentrations were compared with measurements from twelve air monitoring sites using the Paired Peak Estimation and the Median Bias Error. This numerical tool demonstrates a satisfactory performance to calculate mesoscale circulation patterns and photochemical air pollution over Portugal, providing important information to be used by decision-makers for air quality assessment.

Many complex problems arising in different areas of science and engineering can be successfully studied by applying large scale mathematical models. Several important tasks have to be performed both during the development of a large scale mathematical model and during its application in the attempts to get answers to important questions related to the problems studied. The successful completion of the following tasks is crucial:

Large-scale mathematical models are very powerful tools in the efforts to provide more information and more detailed information about the pollution levels, especially about pollution levels which exceed certain critical values.. However, the model used must satisfy at least two conditions: (i) it must be verified that the model results are reliable and (ii) it should be possible to carry out different study by using the model. It is clear that comprehensive studies about relationships between different input parameters and the model results can only be carried out (a) if the numerical methods used in the model are sufficiently fast and (b) if the code runs efficiently on the available high-speed computers. Some results obtained recently by a new unified version of the Danish Eulerian Model will be presented in this paper.

The basic parallel versions of the Danish Eulerian Model (UNI-DEM) has been implemented on the new petascale supercomputer DISCOVERER, installed last year in Sofia, Bulgaria by the company Atos. DISCOVERER is part of the European High Performance Computing Joint Undertaking (EuroHPC), which is building a network of 8 powerful supercomputers across the European Union (3 pre-exascale and 5 petascale). The results of some scalability experiments with the basic MPI and a hybrid MPI-OpenMP parallel implementations of UNI-DEM on the new Bulgarian petascale supercomputer DIS-COVERER (in EuroHPC network) are presented here. They are compared with similar earlier experiments performed on the Mare Nostrum III supercomputer (petascale too) at Barcelona Supercomputing Centre-the most powerful supercomputer in Spain by that time, upgraded currently to the pre-exascale Mare Nostrum V, also part of the EuroHPC JU infrastructure.

Large-scale environmental models are powerful tools, designed to meet the increasing demand in various environmental studies. The atmosphere is the most dynamic component of the environment, where the pollutants and other chemical species actively interact with each other, and can quickly be moved in a very long distance. Therefore the advanced modeling is usually done in a large computational domain. Moreover, all relevant physical, chemical and photochemical processes should be taken into account, which heavily depend on the meteorological conditions. All this makes the air pollution modeling a huge and rather difficult computational task, requiring a large amount of computational power. The most powerful supercomputers have been used for the development and test runs of such a model, the Danish Eulerin Model (DEM). Distributed parallel computing via MPI is one of the most efficient techniques in achieving good performance and getting results in real time. The quickly advancing GRID computing technology is another powerful tool that can be used to reach higher level of performance of such a huge model. Both techniques and their inherent problems are discussed in this paper. Results of numerical experiments are presented and analysed and some conclusions are drown, based on the experiments.

Air quality assessment in a polluted area with specific and complex terrain features situated in the northwestern part of Romania was made using The Air Pollution Model (TAPM). This is a 3D prognostic model that solves the fundamental fluid dynamics and scalar transport equations to predict both meteorological data and air pollution concentrations. In order to properly assess the concentrations of air pollutants in the studied area, there were taken into account not only the emissions from the activities on the premises of the main industrial platform, but also the contribution from the other pollution sources from the area of interest, such as other industries, residential heating, traffic, dump heaps. The mathematical modeling results, displayed as air pollutant dispersion maps, showed the significant influence of the complex terrain features and of the other pollution sources on the concentration levels in the region, usually associated with the emissions of the main industrial platform.

Large scale air pollution models are powerful tools, designed to meet the increasing demand in different environmental studies. The atmosphere is the most dynamic component of the environment, where the pollutants can be moved quickly on far distnce. Therefore the air pollution modeling must be done in a large computational domain. Moreover, all relevant physical, chemical and photochemical processes must be taken into account. In such complex models operator splitting is very often applied in order to achieve sufficient accuracy as well as efficiency of the numerical solution. The Danish Eulerian Model (DEM) is one of the most advanced such models. Its space domain (4800 × 4800 km) covers Europe, most of the Mediterian and neighboring parts of Asia and the Atlantic Ocean. Efficient parallelization is crucial for the performance and practical capabilities of this huge computational model. Different splitting schemes, based on the main processes mentioned above, have been implemented and tested with respect to accuracy and performance in the new version of DEM. Some numerical results of these experiments are presented in this paper.

The study presents the development of an integrated model that optimises the performances of Neural Nets (NN) and dispersion models developed by the authors. Supervised NN models and dispersion models are two important techniques for evaluating air pollution concentrations. In our work we filter the concentration levels produced by an air pollution model with an NN to account for disagreement between the measured and predicted values. The performance of the new methodology was tested using two different formulations (VHDM and SPM) applied to the data set of the Prairie Grass, of the Copenhagen and of the Kincaid. The VHDM (Virtual Height Dispersion Model), a Gaussian formulation that take account vertical non-homogeneity of wind field and turbulence conditions, is applied to the Kincaid data set. The puff model (Skewed Puff Model) is based on the Monin-Obukhov similarity theory and is applied to the Prairie Grass and the Copenhagen data sets. Results show a marked improvement for all simulations when the neural network is added downstream of the dispersion model. This study demonstrated that the use of NN in order to correct the air dispersion model could be the reasonable model combination when the air pollution model gives some systematic error with respect to experimental data.

for transport and vertical diffusion in the Atmospheric Boundary Layer (ABL). In this formulation, the source height is replaced by a virtual height expressed by simple functions of meteorological variables. The model accepts a general profile of wind u(z) and eddy diffusivity coefficient K z. The lateral dispersion coefficient is based on Taylor's theory (TAYLOR G. I., Proc. London Math. Soc., 20 (1921) 196-204). The turbulence in the ABL is subdivided into various regimes, each characterized by different parameters for length and velocity scales. The model performances under unstable conditions have been tested utilizing two different data sets.

Sensitivity Analysis of the Danish Eulerian Model requires an extensive amount of output data from computationally expensive numerical experiments with a specially adapted for the purpose version of the model, called SA-DEM. It has been successfully implemented and run on the most powerful parallel supercomputer in Bulgaria-IBM Blue-Gene/P. A new enhanced version, capable of using efficiently the full capacity of the mashine, has recently been developed. It will be described in this paper together with some performance analysis and numerical results. The output results are used to construct some mesh-functions of ozone concentrations ratios to be used further in sensitivity analysis of the model by using Monte Carlo algorithms.

Large-scale air pollution models can successfully be used in different environmental studies. These models are described mathematically by systems of partial differential equations. Splitting procedures followed by discretization of the spatial derivatives lead to several large systems of ordinary differential equations of order up to 80 millions. These systems have to be handled numerically at up to 250 000 timesteps. Furthermore, many scenarios are often to be run in order to study the dependence of the model results on the variation of some key parameters (as, for example, the emissions). Such huge computational tasks can successfully be treated only if (i) fast and sufficiently accurate numerical methods are used and (ii) the models can efficiently be run on parallel computers. Efficient Monte Carlo methods for some subproblems will be presented and applications of the model in the solution of some environmental tasks will also be made.

CSIRO's coupled meteorological and pollutant chemical dispersion model, the Air Pollution Model (TAPM), and the EPA Victoria multi-pollutant emission inventory for the Port Phillip region (including Melbourne-covering a region of approximately 24,000 km 2) were used to simulate 1 year of hourly averaged air pollution concentrations for smog and particles, both without and with meteorological data assimilation. Model results have been compared with data from the EPA Victoria air-monitoring network. Results show that TAPM predicts year-long extreme concentration statistics (i.e. the high end of the distribution of concentrations over 1 year) for hourly averaged smog (NO 2 and O 3) to within 9%, and for 24-hourly averaged particles (PM 10 and PM 2.5) to within 13%, averaged across all monitoring sites, even when no local meteorological data are assimilated into the model. Results for paired-in-time measures such as correlation coefficient, factor-of-two and gross error, also show that TAPM is performing well, with average values (averaged over all monitoring sites) of 0.51, 0.76 and 0.38 for these measures, respectively. Results for the simulation with meteorological data assimilation were very close to those from the simulation without meteorological data assimilation. The good results obtained with the model also indicate that the emission inventory is of a high quality.

In this work is presented the development of an integrated model composed by a Neural Net and a dispersion model. Using the concentrations predicted by an air dispersion model (ADMD) as input to a Neural net, we evaluated the performances of this new methodology in the cases of a releases from an elevated emission source using the urban data set of the Indianapolis field study as test case. INTRODUCTION The air pollution models constitute a sophisticated tools that reflects the knowledge on turbulent transport in the atmosphere. However, this models have not so far been able to reproduce satisfactorily ground level concentrations because the influence of important variables is not perfectly described. The results they provide are affected by a considerable margin of error, the most important of which is the uncertainty linked to the intrinsic variability of the atmosphere and to local topography. Most operative models for estimating the dispersion of gases and particles in the atmos...

This paper deals with convergence analysis and applications of a Zienkiewicz-type (Z-type) triangular element, applied to fourth-order partial differential equations. For the biharmonic problem we prove the order of convergence by comparison to a suitable ...

Large-scale mathematical models are very powerful tools in the efforts to provide more information and more detailed information about the pollution levels, especially about pollution levels which exceed certain critical values.. However, the model used must satisfy at least two conditions: (i) it must be verified that the model results are reliable and (ii) it should be possible to carry out different study by using the model. It is clear that comprehensive studies about relationships between different input parameters and the model results can only be carried out (a) if the numerical methods used in the model are sufficiently fast and (b) if the code runs efficiently on the available high-speed computers. Some results obtained recently by a new unified version of the Danish Eulerian Model will be presented in this paper.

Validation is the process through which reliability of remote sensing products and algorithms can be assessed. For that, it is necessary to intercompare the data provided by different sensors with independent measurements directly obtained from the ground. ... Since 2002, the Valencia Anchor Station and, more recently, since 2005, the Alacant Anchor Station, both are being used for validation activities in the context of remote sensing Earth Observation Missions such as CERES (Clouds and the Earth's Radiant Energy System) and GERB (Geostationary Earth ...

Large-scale air pollution models are represented by large systems of partial di erential equations. After some discretization and splitting procedures these are transformed into several huge systems of ODE's, which are to be handled numerically during many time-steps. The use of both fast numerical methods and high speed computers is crucial for such models. The chemical part of the model is normally the most time-consuming module. It consists of a huge number of sti ODE systems, which can be treated as parallel tasks by using di erent partitioning procedures. One can also try to exploit the sparsity of the Jacobian matrix in order to improve further the e ciency of the computations. A special sparsity algorithm for the chemical part of a large air pollution model is described and tested in this paper.

Large-scale air pollution models are represented by large systems of partial di erential equations. After some discretization and splitting procedures these are transformed into several huge systems of ODE's, which are to be handled numerically during many time-steps. The use of both fast numerical methods and high speed computers is crucial for such models. The chemical part of the model is normally the most time-consuming module. It consists of a huge number of sti ODE systems, which can be treated as parallel tasks by using di erent partitioning procedures. One can also try to exploit the sparsity of the Jacobian matrix in order to improve further the e ciency of the computations. A special sparsity algorithm for the chemical part of a large air pollution model is described and tested in this paper.

The main objective of this paper is to study photochemical air pollution in Portugal through the application of the "The Air Pollution Model" (TAPM) (version 2.0), a 3D prognostic model that solves the fundamental fluid dynamics and scalar transport equations to predict both meteorology and air pollution concentrations. The possibility of using a meso-meteorological and dispersion model on an hourly base during an entire year represents an important achievement for air quality studies. The model was applied to Portugal, for the year 2003, to predict ozone concentrations. The simulation domain was 350 x 700 km with nested horizontal resolutions of 30 and 10 km. This domain included the entire Portuguese mainland and some of the surrounding ocean in order to adequately simulate the complexity of the atmospheric flow. Model predictions of meteorological surface parameters, such as wind and temperature, were compared with measurements of six meteorological monitoring sites. The Index of Agreement (IOA) was applied between observations and model results predicted on one year-long meteorological parameters. IOA is greater than 0.50 for all parameters, indicating a good agreement with measurements. Predicted ozone concentrations were compared with measurements from twelve air monitoring sites using the Paired Peak Estimation and the Median Bias Error. This numerical tool demonstrates a satisfactory performance to calculate mesoscale circulation patterns and photochemical air pollution over Portugal, providing important information to be used by decision-makers for air quality assessment.

The main objective of this paper is to study photochemical air pollution in Portugal through the application of the "The Air Pollution Model" (TAPM) (version 2.0), a 3D prognostic model that solves the fundamental fluid dynamics and scalar transport equations to predict both meteorology and air pollution concentrations. The possibility of using a meso-meteorological and dispersion model on an hourly base during an entire year represents an important achievement for air quality studies. The model was applied to Portugal, for the year 2003, to predict ozone concentrations. The simulation domain was 350 x 700 km with nested horizontal resolutions of 30 and 10 km. This domain included the entire Portuguese mainland and some of the surrounding ocean in order to adequately simulate the complexity of the atmospheric flow. Model predictions of meteorological surface parameters, such as wind and temperature, were compared with measurements of six meteorological monitoring sites. The Index of Agreement (IOA) was applied between observations and model results predicted on one year-long meteorological parameters. IOA is greater than 0.50 for all parameters, indicating a good agreement with measurements. Predicted ozone concentrations were compared with measurements from twelve air monitoring sites using the Paired Peak Estimation and the Median Bias Error. This numerical tool demonstrates a satisfactory performance to calculate mesoscale circulation patterns and photochemical air pollution over Portugal, providing important information to be used by decision-makers for air quality assessment.

An attempt has been made in order to find a link between the Gaussian plume model and the K model under conditions of a height structured atmospheric boundary layer. The resulting set of equations connecting the Gaussian parameters to the K and u fields can be used to define a "modified" Gaussian plume model that is capable of modelling phenomena like trapping and fumigation of pollutants typical of situations with marked variations of the advection and dispersion fields with height.

Validation is the process through which reliability of remote sensing products and algorithms can be assessed. For that, it is necessary to intercompare the data provided by different sensors with independent measurements directly obtained from the ground. ... Since 2002, the Valencia Anchor Station and, more recently, since 2005, the Alacant Anchor Station, both are being used for validation activities in the context of remote sensing Earth Observation Missions such as CERES (Clouds and the Earth's Radiant Energy System) and GERB (Geostationary Earth ...

This special issue of Future Generation Computer Systems is devoted to modern applications of distributed and grid computing. A relatively small number of papers were selected in order to cover some important areas of distributed and grid computing. At the same time we do not pretend that all important areas of this fast developing scientific field are covered.

This paper presents the results from an air quality modelling study in the Sydney region using a meteorological model, the Conformal Cubic Atmospheric Model (CCAM) and a dispersion model called the Chemical Transport Model (CTM). Both CCAM and CTM were developed by CSIRO and have been used to conduct pilot modelling studies, such as the Sydney particle Study (SPS) previously. In this study, the emission input data, which is based on the latest NSW 2008 emission inventory and consists of 15 different source type categories, is used to evaluate the CCAM-CTM model by comparing with observed meteorological and air quality data. The study period is February 2011, which was the focus of the SPS. The air quality outputs of interest were nitrogen oxides (NO, NO 2), ozone (O 3) and particles (PM10 and PM2.5). The results of this study show that the current version of CCAM-CTM used with the above emission data provides good prediction of NO, NO 2 , Ozone and PM2.5 but not PM10. Based on the results, future improvements of the CCAM-CTM air quality model are discussed and suggested. (NSW) from time to time. As part of the OEH-commissioned Sydney Particle Study CSIRO demonstrated the use of CTM, using meteorological forecast data from CCAM (Cubic Conformal Atmospheric Model) and referencing the 2003 base year Greater Metropolitan Region (GMR) Emissions Inventory (Cope et al. 2013). As particles such as dust, sea salt, and wildfire aerosols can be transported across a wide region, there is a need for a flexible meteorological model that can be scaled to different resolutions to be integrated into the CTM framework. CCAM allows dynamical downscaling of global meteorological forecast or analysis data and is therefore suitable for this purpose. The Sydney Particle Study included summer and autumn measurements of precursor gases and particles (number, size, mass, composition), with observations used for model validation. Modelling approaches and observation data from the Sydney Particle Study are being used by OEH and CSIRO to further develop and calibrate the CCAM-CTM particle modelling system for use in NSW. An overview of advancements being made in the CCAM-CTM modelling system and the nature of the results are presented in this paper. CCAM-CTM MODELLING SYSTEM The CCAM (release 2057) using MODIS land-use data on the Linux platform is used to produce downscaled meteorology data for four Australian grid domains at 80 km × 80 km, 27 km × 27 km, 9 km × 9 km and 3 km × 3 km resolution for use in CTM simulations. As CCAM is a global model and only assimilates reanalyses data at large length scales with a scale-selective filter (Thatcher and McGregor 2009), it can equally be driven by ERA-Interim or NCEP reanalyses. ERA-Interim reanalyses was selected for use due to it having higher resolution and being more widely applied by the Bureau of Meteorology and CSIRO for regional modelling applications. The Conformal Cubic Atmospheric Model (CCAM) has been developed at CSIRO for 20+ years. CCAM was the first 3-D cubic atmospheric model in the world. The grid is derived by projecting a cube onto a sphere. Hence CCAM geometry is based on conformal cubic grid system. A C48 cubic grid indicates 48 × 48 horizontal grid points per face or 48 × 48 × 6 grid points per level. Schmidt transform is used for variable resolution regional simulations. CCAM uses host modelled data such as global meteorological modelling output (e.g.

Regional air pollution models are usually off-line coupled with numerical weather prediction models. The present study, however, focuses on on-line coupled modelling, for which the air pollution model is an integral part of the meteorological model. To this purpose, simulations of the first European Tracer Experiment (ETEX), which supplies the best suited verification data for dispersion modelling of a passive tracer, have been performed using versions of the Danish Meteorological Institute High Resolution Limited-Area Model (DMI-HIRLAM). Simulation results have been compared with observed values as well as with results from other models.

Regional air pollution models are usually off-line coupled with numerical weather prediction models. The present study, however, focuses on on-line coupled modelling, for which the air pollution model is an integral part of the meteorological model. To this purpose, simulations of the first European Tracer Experiment (ETEX), which supplies the best suited verification data for dispersion modelling of a passive tracer, have been performed using versions of the Danish Meteorological Institute High Resolution Limited-Area Model (DMI-HIRLAM). Simulation results have been compared with observed values as well as with results from other models.

Large-scale environmental models are powerful tools, designed to meet the increasing demand in various environmental studies. The atmosphere is the most dynamic component of the environment, where the pollutants and other chemical species actively interact with each other, and can quickly be moved in a very long distance. Therefore the advanced modeling is usually done in a large computational domain. Moreover, all relevant physical, chemical and photochemical processes should be taken into account, which heavily depend on the meteorological conditions. All this makes the air pollution modeling a huge and rather difficult computational task, requiring a large amount of computational power. The most powerful supercomputers have been used for the development and test runs of such a model, the Danish Eulerin Model (DEM). Distributed parallel computing via MPI is one of the most efficient techniques in achieving good performance and getting results in real time. The quickly advancing GRID computing technology is another powerful tool that can be used to reach higher level of performance of such a huge model. Both techniques and their inherent problems are discussed in this paper. Results of numerical experiments are presented and analysed and some conclusions are drown, based on the experiments.

Large scale air pollution models are powerful tools, designed to meet the increasing demand in different environmental studies. The atmosphere is the most dynamic component of the environment, where the pollutants can quickly be moved in a very long distance. Therefore the advanced modeling must be done in a large computational domain. Moreover, all relevant physical, chemical and photochemical processes must be taken into account. The speed of these processes vary in a wide range. This fact implies that a small time step must be used in order to achieve both numerical stability and sufficient accuracy of the results. Thus the numerical treatment of such an air pollution model becomes in many cases a huge computational problem, a challenging task for the most powerful up-to-date supercomputers. The Danish Eulerian Model (DEM) is used in this work. The paper fo-cuses on the efficient parallel implementation of DEM on powerful parallel supercomputers. We present a variety of performance and scalability results, obtained on different parallel machines by using standard paral-lelization tools (MPI for distributed-memory parallelism and OpenMP for shared-memory parallelism). It is shown by experiments that MPI and OpenMP can both be used on separate levels of parallelism to get the best use of the clustered parallel machines. Application of the results in the environmental studies, related to high ozone concentrations in the air, is illustrated by some plots in the last section.

Large scale air pollution models are powerful tools, designed to meet the increasing demand in different environmental studies. The atmosphere is the most dynamic component of the environment, where the pollutants can be moved quickly on far distnce. Therefore the air pollution modeling must be done in a large computational domain. Moreover , all relevant physical, chemical and photochemical processes must be taken into account. In such complex models operator splitting is very often applied in order to achieve sufficient accuracy as well as efficiency of the numerical solution. The Danish Eulerian Model (DEM) is one of the most advanced such models. Its space domain (4800 × 4800 km) covers Europe, most of the Mediterian and neighboring parts of Asia and the Atlantic Ocean. Efficient parallelization is crucial for the performance and practical capabilities of this huge computational model. Different splitting schemes, based on the main processes mentioned above, have been implemented and tested with respect to accuracy and performance in the new version of DEM. Some numerical results of these experiments are presented in this paper.

Validation is the process through which reliability of remote sensing products and algorithms can be assessed. For that, it is necessary to intercompare the data provided by different sensors with independent measurements directly obtained from the ground. ... Since 2002, the Valencia Anchor Station and, more recently, since 2005, the Alacant Anchor Station, both are being used for validation activities in the context of remote sensing Earth Observation Missions such as CERES (Clouds and the Earth's Radiant Energy System) and GERB (Geostationary Earth ...

... G.Bonchev str., bl. 25-A, 1113 Sofia, Bulgaria. ... Stud-ies in Mathematics and Applications, 16, North-Holland, Amsterdam (1985) 6. McRae, GJ, Goodin, WR, and Seinfeld, JH: Numerical solution of the at-mospheric diffusion equations for chemically reacting flows. J. Comp. ...

Large scale air pollution models are powerful tools, designed to meet the increasing demand in different environmental studies. The atmosphere is the most dynamic component of the environment, where the pollutants can be moved quickly on far distnce. Therefore the air pollution modeling must be done in a large computational domain. Moreover, all relevant physical, chemical and photochemical processes must be taken into account. In such complex models operator splitting is very often applied in order to achieve sufficient accuracy as well as efficiency of the numerical solution. The Danish Eulerian Model (DEM) is one of the most advanced such models. Its space domain (4800 × 4800 km) covers Europe, most of the Mediterian and neighboring parts of Asia and the Atlantic Ocean. Efficient parallelization is crucial for the performance and practical capabilities of this huge computational model. Different splitting schemes, based on the main processes mentioned above, have been implemented and tested with respect to accuracy and performance in the new version of DEM. Some numerical results of these experiments are presented in this paper.

Large-scale environmental models are powerful tools, designed to meet the increasing demand in various environmental studies. The atmosphere is the most dynamic component of the environment, where the pollutants and other chemical species actively interact with each other, and can quickly be moved in a very long distance. Therefore the advanced modeling is usually done in a large computational domain. Moreover, all relevant physical, chemical and photochemical processes should be taken into account, which heavily depend on the meteorological conditions. All this makes the air pollution modeling a huge and rather difficult computational task, requiring a large amount of computational power. The most powerful supercomputers have been used for the development and test runs of such a model, the Danish Eulerin Model (DEM). Distributed parallel computing via MPI is one of the most efficient techniques in achieving good performance and getting results in real time. The quickly advancing GRID computing technology is another powerful tool that can be used to reach higher level of performance of such a huge model. Both techniques and their inherent problems are discussed in this paper. Results of numerical experiments are presented and analysed and some conclusions are drown, based on the experiments.

The numerical treatment of a regional air pollution model (such models are, as a rule, described mathematically by systems of partial differential equations) leads to the solution of very large computational problems. The chemical sub-model of an air pollution model is normally the most time-consuming part of the computational work. The application of appropriate discretization and splitting procedures reduces the

The air pollution, and especially the reduction of the air pollution to some acceptable levels, is an important environmental problem, which will become even more important in the next two-three decades. This problem can successfully be studied only when high-resolution comprehensive models are developed and used on a routinely basis. However, such models are very time-consuming, also when modern high-speed computers are available. Indeed, if an air pollution model is to be applied on a large space domain by using fine grids, then its discretization will always lead to huge computational problems. Assume, for example, that the space domain is discretized by using a (288x288) grid and that the number of chemical species studied by the model is 35. Then ODE systems containing 2903040 equations have to be treated at every time-step (the number of time-steps being typically several thousand). If a three-dimensional version of the air pollution model is to be used, then the number of equations must be multiplied by the number of layers. The treatment of large air pollution models on modern parallel computers by using efficient numerical algorithms will be discussed in this paper.

Air quality problems produced by high levels of ozone (O3) has been of concern for their effects on human health. The city of Rome is a typical Mediterranean metropolitan area experiencing frequently pollution episodes, characterized by high concentrations of ozone, associated with hot sunny days and stagnant conditions. The spatial extension of these phenomena was only detected by monitoring networks data, which are often duty influenced by local emissions. In a recent modelling study conducted by Gariazzo et al the chemical transport model (FARM) has been applied to study primary and secondary gas/aerosol pollutants concentrations in the urban area of Rome. The comparison of FARM model results against observations has shown that, although the FARM model was able to predict the observed ozone diurnal concentrations at both urban and rural stations, the nighttime predictions were sometimes overestimated due to both an i ncorrect evaluation of the nocturnal vertical exchange coefficients k z and to missed local NO x emissions in rural areas.