Can Assimilation of Ground Particulate Matter Observations Improve Air Pollution Forecasts for Highly Polluted Area of Europe?

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Abstract

In this study we present the influence of assimilation of ground PM2.5 measurements on forecasted concentrations of particulate matters for low air quality episode observed in the year 2017 over Poland. The episode was not reproduced by a standard forecasting system, based on the Weather Research and Forecasting with Chemistry model (WRF-Chem), working operationally without data assimilation. Here, we used Grid point Statistical Interpolation (GSI) system to assimilate ground observations from 42 stations measuring PM2.5 concentrations. The results show that the assimilation of PM2.5 concentrations has a positive impact on modelled concentration of PM2.5 and PM10. The greatest positive impact is noticed for the period with the high measured concentrations of pollutants. The results also show that for some stations the assimilation of PM2.5 and PM10 may lead to overestimation of concentrations at the warmer period characterised by lower overall PM concentrations. Further study with an application of degree-day factors for residential emissions and GSI assimilation is planned as the next step.

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APA

Werner, M., Kryza, M., & Guzikowski, J. (2020). Can Assimilation of Ground Particulate Matter Observations Improve Air Pollution Forecasts for Highly Polluted Area of Europe? In Springer Proceedings in Complexity (pp. 267–271). Springer. https://doi.org/10.1007/978-3-030-22055-6_42

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