<i>E pluribus unum</i>*: ensemble air quality predictions

Abstract

Abstract. In this study we present a novel approach for improving the air quality predictions using an ensemble of air quality models generated in the context of AQMEII (Air Quality Model Evaluation International Initiative). The development of the forecasting method makes use of modelled and observed time series (either spatially aggregated or relative to single monitoring stations) of ozone concentrations over different areas of Europe and North America. The technique considers the underlying forcing mechanisms on ozone by means of spectrally decomposed previsions. With the use of diverse applications, we demonstrate how the approach screens the ensemble members, extracts the best components and generates bias-free forecasts with improved accuracy over the candidate models. Compared to more traditional forecasting methods such as the ensemble median, the approach reduces the forecast error and at the same time it clearly improves the modelled variance. Furthermore, the result is not a mere statistical outcome depended on the quality of the selected members. The few individual cases with degraded performance are also identified and analysed. Finally, we show the extensions of the approach to other pollutants, specifically particulate matter and nitrogen dioxide, and provide a framework for its operational implementation. *One out of many

<i>De praeceptis ferendis</i>: good practice in multi-model ensembles

“…Like mmeS, mmeW improves the error and also replicates better the observed variance (Fig. 3c) (similar results apply also to the ensemble product generated from spectral optimization demonstrated in Galmarini et al, 2013). The distribution around the truth in all those ensemble products has always higher symmetry compared to mme, as can be seen in Fig.…”

“…Data of several types were collected and model evaluated . The community of the participating models, which forms a multi-model set in terms of meteorological drivers, air quality models, emissions and chemical boundary conditions, is presented in detail in Galmarini et al (2013). The model settings and input data are described in detail in Solazzo et al (2012a, b), Schere et al (2012), Pouliot et al (2012), where references about model development and history are also provided.…”

Two-scale multi-model ensemble: is a hybrid ensemble of opportunity telling us more?

“…In this study, we would also like to build upon the research performed in other multi-model ensembles over the years; rather than calculating only the classical model average or median ensemble (mme), we shall also calculate three ensembles based on the findings of Potempski and Galmarini (2009), Riccio et al (2012), Solazzo et al (2012aSolazzo et al ( , b, 2013, Galmarini et al (2013), and Kioutsioukis and Galmarini (2014). We shall therefore refer to the ensemble made by the optimal subset of models that produce the minimum RMSE as mmeS (Solazzo et al, 2012a, b); the ensemble produced by filtering measurements and all model results using the Kolmogorov-Zurbenko decomposition presented earlier and recombining the four components that best compare with the observed components into a new model set as kzFO ; and the optimally weighted combination as mmeW (Potempski and Galmarini, 2009;Kioutsioukis and Galmarini, 2014;Kioutsioukis et al, 2016).…”

Pauci ex tanto numero: reduce redundancy in multi-model ensembles

“…Recently, Galmarini et al (2013) have investigated the possibility of forecasting AQ starting from the combination of well-behaved spectral properties extracted from the AQMEII ensemble. The results show that the approach outruns even the ensemble median.…”