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

  • Hosni SnounEmail author
  • Ghazi Bellakhal
  • Hatem Kanfoudi
  • Xiaole Zhang
  • Jamel Chahed
Research Article
  • 28 Downloads

Abstract

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.

Keywords

Flux-adjusting surface data assimilation system Wind data assimilation Improve dispersion prediction Model evaluation, Southern Mediterranean 

Notes

Acknowledgements

The authors would like to acknowledge the computational resources and support provided by the Cyprus Institute Cy-Tera Project, which is co-funded by the European Regional Development Fund and the Republic of Cyprus through the Research Promotion Foundation. We are also grateful for the National Centre for Environmental Prediction (NCEP) for the 4.0.1 version of WRF model, the Obsgrid-FASDAS and the WRFDA packages tested in our study and the GDAS-FNL dataset that were employed for meteorological simulations.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Modeling in hydraulic and environment laboratory, National Engineering School of TunisUniversity of Tunis El ManarTunisTunisia
  2. 2.ETH Zürich, Institute of Environmental EngineeringZürichSwitzerland

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