Study on the influence of ground and satellite observations on the numerical air-quality for PM10 over Romanian territory

Abstract The numerical forecast of particulate matter concentrations in general, and PM10 in particular is a theme of high socio-economic relevance. The aim of this study was to investigate the impact of ground and satellite data assimilationof PM10 observations into the Weather Researchand Forecasting modelcoupled with Chemistry (WRF-CHEM) numerical air quality model for Romanianterritory. This is the first initiative of the kind for this domain of interest. Assimilation of satellite information – e.g. AOT's in air quality models is of interest due to the vast spatial coverage of the observations. Support Vector Regression (SVR) techniques are used to estimate the PM content from heterogeneous data sources, including EO products (Aerosol Optical Thickness), ground measurements and numerical model data (temperature, humidity, wind, etc.). In this study we describe the modeling framework employed and present the evaluation of the impact from the data assimilationof PM10 observations on the forecast of the WRF-CHEM model. Integrations of the WRF-CHEM model in data assimilationenabled/disabled configurations allowed the evaluation of satellite and ground data assimilationimpact on the PM10 forecast performance for the Romanianterritory. The model integration and evaluation were performed for two months, one in winter conditions (January 2013) and one in summer conditions (June 2013).