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Three different modelling techniques to simulate the pollutant dispersion in the atmosphere at the microscale and in presence of obstacles are evaluated and compared. The Eulerian and Lagrangian approaches are discussed, using RAMS6.0 and MicroSpray models respectively. Both prognostic and diagnostic modelling systems are considered for the meteorology as input to the Lagrangian model, their differences and performances are investigated. An experiment from the Mock Urban Setting Test field campaign observed dataset, measured within an idealized urban roughness, is used as reference for the comparison. A case in neutral conditions was chosen among the available ones. The predicted mean flow, turbulence and concentration fields are analysed on the basis of the observed data. The performances of the different modelling approaches are compared and their specific characteristics are addressed. Given the same flow and turbulence input fields, the quality of the Lagrangian particle model is found to be overall comparable to the full-Eulerian approach. The diagnostic approach for the meteorology shows a worse agreement with observations than the prognostic approach but still providing, in a much shorter simulation time, fields that are suitable and reliable for driving the dispersion model. [ABSTRACT FROM AUTHOR]

The presence of natural or artificial barriers placed near the air quality measuring stations located in an urban environment poses some problems in the assessment of their siting and significance. The atmospheric flow distortion induced by these structures can create situations that affect the concentration levels of measured pollutants. A modelling study at microscale of the pollutant dispersion in an urban environment has been performed to quantify these possible effects on measurements. The target domain is located in a central section of the city of Turin (northern Italy) around via della Consolata, near the location of an existing station of the regional monitoring network. The study involved the use of the modelling system Micro-Swift–Spray, which takes into account the presence of complex topographical barriers, including street canyons and squares surrounded by buildings. Two entire days have been simulated, to characterise both a critical and a more standard condition, with reconstruction of average flow, turbulence and hourly ground-level pollutant concentrations. Contributions from both traffic and building-heating sources have been considered and a comparison with measured concentrations has been performed to verify the representativeness of the measuring station with respect to both average conditions and spatial variability inside the considered region, otherwise very difficult to asses through a single and isolated measuring point. [ABSTRACT FROM PUBLISHER]

The interannual variability of the Madden– Julian Oscillation (MJO) is investigated in an ensemble of 15 experiments performed with the ECHAM4 T30 general circulation model (GCM). The model experiments have been performed with AMIP conditions from January 1979 to December 1993. The MJO signal has been identified applying a principal oscillation pattern (POP) analysis to the 200-mb tropical velocity potential. The results obtained from the model ensemble are compared with 15 y of ECMWF re-analysis and OLR observations. The results suggest that the warm and cold phases of El Niño have some influence on the spatial propagation of the oscillation. Both in the re-analysis and in the model ensemble, the results indicate that during La Niña conditions the MJO is mostly confined west of the date line, with the largest activity located over the Indian Ocean and the western Pacific. In warm El Niño conditions, the convective anomalies associated with the oscillation appear to penetrate farther into the central Pacific. These changes in the MJO convective forcing seem to affect the zonal mean of the rotational component of the flow anomaly, which tends to weaken during warm El Niño periods. Some weak reproducibility of the interannual variability of the MJO activity is found. The results obtained from four-member and eight-member subsamples of the ensemble indicate that the reproducibility of the interannual behaviour of the MJO can be detected by choosing an ensemble of a larger size. Corresponding to the emergence of reproducibility with the increasing size of the sample, the correlation between the MJO activity and the Niño-3 SST anomaly appears to in-tensify. [ABSTRACT FROM AUTHOR]

A particle model LAMDA suitable for dealing with the atmospheric dispersion is presented. The reliability of the model is tested comparing the results of its simulations to the wind tunnel measurements by Khurshudyan et al. (1981). Two versions of the model, both based on the Langevin equation and askewed distribution of the vertical wind velocity fluctuations, are considered. To develop the second version of the model we derived a proper scheme to produce skewed distributions of particle velocities consistent with the observed first and second moments of turbulent fluctuating velocities, with their horizontal and vertical derivatives, and their cross-correlations. The 2D geometry of the obstacle (in the wind tunnel experiment considered) allowed some simplifications. Due to the lack of some input data (such as the vertical profiles of crosswind standard deviation of wind velocity fluctuations and of the Lagrangian time scales) we looked for three different parametrizations. It was found that particular combinations of measured and parametrized data could give rise to critical vertical regions in which the derived scheme for the generation of random vertical velocity fluctuations cannot be applied. The best vertical distributions of the Lagrangian time scales (third parametrization) were estimated by fitting simple formulations for the average plume height and lateral variances to the measured data. The main results of this work were the model performance in simulating dispersion in shear flow over flat terrain, and its sensivity to the shape of the crosscorrelation term $$\overline {u'w'} $$ . [ABSTRACT FROM AUTHOR]

Conditions of low windspeed associated to strong stability and air stagnation correspond to highly nonstationary and inhomogeneous diffusion situations. In such «poor» diffusion cases, integrated models (like Gaussian models) are no longer valid. New numerical methodologies, like Monte Carlo methods, are needed. Recently a Monte Carlo particle model for airborne pollutant dispersion has been developed by our team. Its ability to predict ground-level concentrations in convective and neutral conditions with a high degree of accuracy was verified against tracer experiments. In the present paper our model is used to simulate the dispersion under low windspeed conditions. Particular emphasis is given to the problem of wind meandering. We have demonstrated the need of recording the wind statistics over averaging periods of the order of a few minutes and an ad hoc algorythm to treat low windspeed situation when only hourly average data are available is suggested. The model results are compared to tracer data obtained by the Idaho National Engineering Laboratory (U.S.A.) in 1974. [ABSTRACT FROM AUTHOR]

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