A full forecast system of air quality for the South East of the Iberian Peninsula.

Air pollution forecasting can be used to alert about dangerous health effects caused by airborne pollutants and, in consequence, to take  actions to reduce pollutant concentrations (i.e reducing traffic, control industrial activities, etc..). Therefore, the development of reliable  air quality forecast systems is a of great interest.

The system consist of two main branchs. A statistical method based on  Neural Networks is used to forecast (10 days) several dayily air quality
index at the sites were historical data is available (i.e. pollution  measurement stations). A dynamical method based on WRF-CHEM to forecast hourly (48h) values of a large variety of species in a high resolution  domain (2km). Both subsystems use GFS and ECMWF forecasts as driving  conditions. The  dynamical subsystem incorporates 4DVAR data assimilation  of meteorological data (first 12 hours of forecast), and dynamical  emissions. The dynamical  emissions consist in changing the emissions of  large factories and trafficc. The emissions data are obtained by machine  learning methods based on historical series and meteorological conditions (mainly big energy factories). The WRF-CHEM configuration consist of several domains one way nested. The mother domain covers the entire Saharian desert in order to incorporante the dust transport contribution to particulate matter concentration. In addition, the base emission data is continuously updated.    The system also incorporates a module for automatic verification by comparing forecast with observed data, and analysis runs (in order to minimize meteorological forecast uncertainty). This verification process permit us to construct a MOS (Model Output statistics) in order to correct
possible model bias.