We have calibrated the European CAMS air quality multi-model against observations collected by the Regional Monitoring Network of the Liguria region. The calibration strategy used in such activity had its roots in the well-established Ensemble Model Output Statistics (EMOS) through which a raw ensemble forecast can be accurately transformed into a predictive probability density function, with a simultaneous correction of biases and dispersion errors. The strategy also provides a calibrated forecast of model uncertainties. As a result of our analysis, the key role of pollutant real-time observations to be
ingested in the calibration strategy clearly emerges especially in the shorter look-ahead forecast hours. Our dynamic calibration strategy turns out to be superior with respect to its analogous where real-time data are not taken into account. We are currently working to exploit state-of-the-art deep learning algorithms to increase, even more, the final accuracy of our predictions.
Andrea Mazzino, Francesco
Ferrari, Mattia Cavaiola
G. Casciaro, M. Cavaiola, A. Mazzino, Calibrating the CAMS European multi-model air quality forecasts for regional air pollution monitoring, Atmospheric Environment 287, 119259 (2022)