Spatio-temporal Kriging for Air Quality Forecasting
Abstract
The national PREV'AIR system (www2.prevair.org) delivers daily analyses and forecasts of different atmospheric pollutant concentrations over Europe and France. Analysed maps of the previous day situation (D-1) are produced by combining in-situ measurement data with output data from the chemistry-transport model CHIMERE. To perform this combination, kriging with external drift is applied using a moving neighbourhood. Forecast maps for the current and next two days (D+0, D+1, D+2) are computed according to the same methodology, except that the observation data are replaced by statistical forecasts at the monitoring sites obtained by station specific multilinear regression models. A constraint in that approach { denoted as statistical adaptation - lies in the availability of one to several years of training and validation data in order to build robust and efficient regression models. A way to get round this issue is to consider the analysis and adjustment of the forecast as a unique procedure in a spatio-temporal framework. In this study a spatio-temporal external drift kriging using a spatio-temporal moving neighbourhood has been developed and evaluated over one year on both the European and national scales. In that approach the prediction of D+0 concentrations is similar to an analysis where the available data in the neighbourhood are past observations until D-1 and CHIMERE outputs until D+0. The spatio-temporal covariance model and the results of the evaluation will be presented and commented. Those results will be compared to the performance of the current statistical adaptation. Possible areas for improvement and development will be discussed.