Combining deterministic and statistical approaches for PM10 forecasting in Europe

Well documented adverse health effects of airborne particulate matter (PM) stimulate intensive research aimed at understanding and forecasting its behaviour. Forecasting of PM levels is commonly performed with either statistical or deterministic chemistry-transport models (CTM). In this study, we investigate advantages of combining deterministic and statistical approaches for PM10 forecasting over Europe one day ahead. The proposed procedure involves statistical postprocessing of deterministic forecasts by using PM10 monitoring data. A series of experiments is performed using a state-of-the-art CTM (CHIMERE) and statistical models based on linear regressions. It is found that performance of both CTM simulations and "pure" statistical models is inferior to that of the combined models. In particular, the root mean squared error of the deterministic forecasts can be reduced, on the average, by up to 45 percent (specifically, from 12.8 to 6.9I14g/m3 at urban sites in summer) and the coefficient of determination can be almost doubled. Importantly, it is found that the combined models for rural sites in summer and for urban and suburban sites in both summer and winter are representative, on the average, not only for a given monitoring site used for their training, but also of territories of similar type of environment (rural, suburban or urban) within several hundreds of kilometers away.