Abstract
Regional authorities need tools helping them to understand the spatial distribution of an air pollutant over a certain domain. Models can provide spatially consistent air quality data, as they consider all the main physical and chemical processes governing the atmosphere. However, uncertainties in formalization and input data (emission fields, initial and boundary conditions, meteorological patterns) can affect simulation results. On the other hand, measurements, which are considered more accurate, have a limited spatial coverage. The integration of monitoring data and model simulations can potentially increase the accuracy of the resulting spatial fields. This integration can be performed using either deterministic techniques or algorithms which allow for uncertainties in both modeled and observed data, to obtain the best physical assessment of concentration fields. In this study ozone daily fields, simulated by the Transport Chemical Aerosol Model (TCAM), are reanalyzed from April to September 2004. The study area covers all the Northern Italy, divided in 64x41 cells with a spatial resolution of 10x10 km 2. Ozone data measured by ground stations are assimilated using Inverse Distance Weighting (IDW), kriging, cokriging and Optimal Interpolation (OI) in order to obtain reanalyzed ozone fields. Results show that these methods highly improve the accuracy of the retrieved ozone maps.
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Candiani, G., Carnevale, C., Pisoni, E., Volta, M. (2010). Assimilation of Chemical Ground Measurements in Air Quality Modeling. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2009. Lecture Notes in Computer Science, vol 5910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12535-5_17
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DOI: https://doi.org/10.1007/978-3-642-12535-5_17
Publisher Name: Springer, Berlin, Heidelberg
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