Study on the influence of ground and satellite observations on the numerical air-quality for PM10 over Romanian territory. (October 2016)

• Record Type: Journal Article
• Authors: Dumitrache, Rodica Claudia; Iriza, Amalia; Maco, Bogdan Alexandru; Barbu, Cosmin Danut; Hirtl, Marcus; Mantovani, Simone; Nicola, Oana; Irimescu, Anisoara; Craciunescu, Vasile; Ristea, Alina; Diamandi, Andrei
• Is Part Of: Atmospheric environment. Volume 143(2016)
• Page Start: 278
• Page End: 289
• Publication Date: 2016-10
• Subjects: Air quality modeling -- WRF-CHEM -- PM10 -- Ground and satellite data assimilation; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2016.08.063
• Languages: English
• ISSNs: 1352-2310

Abstract

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 assimilation of PM10 observations into the Weather Research and Forecasting model coupled with Chemistry (WRF-CHEM) numerical air quality model for Romanian territory. 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 assimilation of PM10 observations on the forecast of the WRF-CHEM model. Integrations of the WRF-CHEM model in data assimilation enabled/disabled configurations allowed the evaluation of satellite and ground data assimilation impact on the PM10 forecast performance for the Romanian territory. The model integration and evaluation were performed for two months, one in winter conditions (January 2013) and one in summer conditions (June 2013). Highlights: Impact of ground and satellite PM10 data assimilation into the WRF-CHEM model. MODIS AOD PM10-maps derived with a Support Vector Registration technique are used. A better PM10 forecast is obtained using both ground and satellite PM10 estimates. Simulations are conducted for Romanian territory.