Meteorological modelling influence on regional and urban air pollution predictability
| Autor: | Bande, S., D Allura, A., Sandro Finardi, Giorcelli, M., Muraro, M. |
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| Zdroj: | Scopus-Elsevier Hrvatski meteorološki časopis Volume 43 Issue 43/2 |
| ISSN: | 1330-0083 1849-0700 |
| Popis: | ARPA Piemonte performs yearly air quality assessment running a modelling system based on a chemical transport model. The model is capable to simulate air pollutant emission, transport, diffusion and chemical transformation, to provide concentration fields of the main atmospheric pollutants (CO, NOX, SO2, PM10, PM2.5, O3, and benzene) on a hourly basis and to compute all the indicators required by EU legislation. Meteorological fields to drive air quality simulations are reconstructed assimilating ARPA Piemonte meteorological network observations within background fields obtained by ECMWF analyses. The reliability of mesoscale and urban scale meteorology is one of the key issues in determining an air quality modelling system effectiveness. Diagnostic meteorological analysis takes advantage of the wide local measurement network but cannot guarantee the dynamic and thermodynamic variables consistency provided e.g. by prognostic weather prediction models. Since July 2006 ARPA Piemonte operationally uses an air quality forecasting system driven by a numerical weather prediction model. The simultaneous availability of the two systems results provides the possibility to compare different meteorological modelling techniques effects on air pollution predictability. The two modelling systems results are compared by means of model evaluation statistical indexes showing very similar performances over a six months period. The comparison is completed by the analysis of short term critical episodes to highlight meteorological modelling effectiveness in reproducing severe air pollution episodes and short term concentrations variation. The prognostic meteorological fields showed a better capability to simulate peak episodes even if weather forecast errors can cause “false alarm” conditions due to concentration overestimation. |
| Databáze: | OpenAIRE |
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