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Assimilation of Meteorological Data in Online Integrated Atmospheric Transport Model—Example of Air Quality Forecasts for Poland

  • Maciej KryzaEmail author
  • Małgorzata WernerEmail author
  • Jakub GuzikowskiEmail author
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

In this work we analyse the impact of meteorological data assimilation on the performance of the air quality forecasting system for the area of Poland (Central Europe). The forecasting system uses the WRF-Chem model, which is online integrated meteorology and air chemistry transport model. The forecasts are run each day for the next 48 h, using two nested domains of 12 km × 12 km (Europe) and 4 km × 4 km (Poland) and 35 vertical levels. In this work we analyse the period of 11–25 February 2017, during which poor air quality was observed at the beginning, followed by unusually warm days with low concentrations of pollutants. Two sets of forecasts are compared. In the first group, we use the forecasts with no data assimilation. Secondly, we use the community Gridpoint Statistical Interpolation system (GSI) to assimilate surface and radiosonde meteorological data. Both sets of forecasts are compared with hourly measurements of PM10 and PM2.5 for Poland. Assimilation of meteorological data overall improves the air quality forecasts, but not always leads to better representation of high-concentration episode.

Keywords

Particulate matter WRF-Chem Data assimilation Poland 

Notes

Acknowledgements

The study was supported by the National Science Centre, Poland project no. UMO-2016/23/B/ST10/01797. We are grateful to the CIEP for providing the emissions data from the project “Supporting the air quality assessment system with application of modelling of PM10, PM2.5, SO2, NO2, B(a)P”.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Climatology and Atmosphere Protection, Institute of Geography and Regional DevelopmentWrocław UniversityWrocławPoland

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