One-way coupling of WRF with a Gaussian dispersion model: a focused fine-scale air pollution assessment on southern Mediterranean.

Numerous uncertainty factors in dispersion models should be taken into account in order to improve the reliability of predictions. The ability of a mesoscale meteorological model to assimilate observational data is an efficient way to improve operational air quality model forecasts. In this study, local weather data assimilation based on a flux-adjusting surface data assimilation system (FASDAS) is introduced to a Gaussian atmospheric dispersion model for a period with reported stable meteorological conditions. After evaluating the vulnerabilities of FASDAS, a combined data assimilation method is proposed to simultaneously improve the model weather prediction and retrieve the representation of accurate concentration distributions for short-range dispersion modeling against a control run. The two main uncertainty parameters considered are the wind speed and direction. A twin experiment demonstrates that the combined technique effectively improves the distribution of simulated concentrations. Comparison between results before and after the implement of data assimilation demonstrates that discrepancies between the reference simulation and the model forecast are mitigated after introducing the combined method, with more than 70 % of the predictions within a factor of two of the measurements. The errors in wind predictions in the FASDAS influenced the dispersion calculations, and the implementation of wind data assimilation in conjunction with the FASDAS has an indirect effect on further alleviating pollutant transport modeling errors.