  The CAMS air quality multi-model forecasts have been assessed and calibrated
for PM10, PM2.5, O3, NO2, and CO against observations collected by the Regional
Monitoring Network of the Liguria region (northwestern Italy) in the years 2019
and 2020. The calibration strategy used in the present work has 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 emerge
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. The best calibration strategy we have
identified makes the CAMS multi-model forecast system more reliable than other
raw air quality models running at higher spatial resolution which exploit more
detailed information from inventory emission. We expect positive impacts of our
research for identifying and set up reliable and economic air pollution early
warning systems.
