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ENHANCING MESOSCALE METEOROLOGICAL MODELLING CAPABILITIES FOR AIR POLLUTION AND DISPERSION APPLICATIONS
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An international workshop on 25-26 February 2010, Geneva, Switzerland
This international workshops aims to bring together leading groups involved in the development and application of mesoscale models for air pollution research. Much progress has been achieved over the past decade in areas such as parameterisation schemes, downscaling capabilities, online coupling of meteorological and air pollution models as well as in extending the range of applications to which the models can be applied. With the enhanced capabilities the complexity of the models and their use is also increasing. The need for user trainings, therefore, becomes more pressing and specialised training in the use of mesoscale models for air pollution problems becomes important. Key international groups that have played a major role in furthering this research area include: COST 728 (Enhancing Mesoscale Meteorological Modelling Capabilities for Air Pollution and Dispersion Applications), WMO/GURME Programme on the GAW Urban Research Meteorology and Environment), MEGAPOLI (FP7 Project on Megacities), CITYZEN (FP7 project on Megacities), ACCENT (FP6 Network of Excellence), COST 602 (Towards a European Network on Chemical Weather Forecasting and Information Systems), COST 732 (Quality Assurance and Improvement of Micro-Scale Meteorological Models), AQMEII (Air Quality Model Evaluation International Initiative), FAIRMODE (Forum for AIR quality MODelling in Europe) and MACC (FP7 project on Monitoring Atmospheric Composition and Climate). National groups such as MESOMAQ/MESO-NET (Mesoscale Modelling for Air Quality research) have also been instrumental in forwarding the uptake, coordination and development of mesoscale models. Further details will follow soon. For further details please contact
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Disclaimer - This link refers to an important message which should be read in conjunction with this information. In case of any queries, contact Prof Ranjeet S Sokhi. |
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