Abstract. This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in the MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The paper gives an overall picture of its status at the end of MACC-II (summer 2014) and analyses the performance of the multimodel ensemble. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O 3 , NO 2 , SO 2 , CO, PM 10 , PM 2.5 , NO, NH 3 , total NMVOCs (non-methane volatile organic compounds) and PAN+PAN Published by Copernicus Publications on behalf of the European Geosciences Union. V. Marécal et al.:A regional air quality forecasting system over Europe precursors) over eight vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations.The performance of the system is assessed daily, weekly and every 3 months (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the ensemble median to forecast regional ozone pollution events. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO 2 and PM 10 . The statistical indicators for ozone in summer 2014 show that the ensemble median gives on average the best performances compared to the seven models. There is very little degradation of the scores with the forecast day but there is a marked diurnal cycle, similarly to the individual models, that can be related partly to the prescribed diurnal variations of anthropogenic emissions in the models. During summer 2014, the diurnal ozone maximum is underestimated by the ensemble median by about 4 µg m −3 on average. Locally, during the studied ozone episodes, the maxima from the ensemble median are often lower than observations by 30-50 µg m −3 . Overall, ozone scores are generally good with average values for the normalised indicators of 0.14 for the modified normalised mean bias and of 0.30 for the fractional gross error. Tests have also shown that the ensemble median is robust to reduction of ensemble size by one, that is, if predictions are unavailable from one model. Scores are also discussed for PM 10 for winter 2013-1014. There is an underestimation of most models leading the ensemble median to a mean bias of −4.5 µg m −3 . The ensemble median fractional gross error is larger for PM 10 (∼ 0.52) than for ozone and the correlation is lower (∼ 0.35 for PM 10 and ∼ 0.54 for ...

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Abstract. This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. The paper gives an overall picture of its status at the end of MACC-II (summer 2014). This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs and PAN + PAN precursors) over 8 vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performances of the system are assessed daily, weekly and 3 monthly (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the median ensemble to forecast regional ozone pollution events. The time period of this case study is also used to illustrate that the median ensemble generally outperforms each of the individual models and that it is still robust even if two of the seven models are missing. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10 and show an overall improvement over time. The change of the skills of the ensemble over the past two summers for ozone and the past two winters for PM10 are discussed in the paper. While the evolution of the ozone scores is not significant, there are improvements of PM10 over the past two winters that can be at least partly attributed to new developments on aerosols in the seven individual models. Nevertheless, the year to year changes in the models and ensemble skills are also linked to the variability of the meteorological conditions and of the set of observations used to calculate the statistical indicators. In parallel, a scientific analysis of the results of the seven models and of the ensemble is also done over the Mediterranean area because of the specificity of its meteorology and emissions. The system is robust in terms of the production availability. Major efforts have been done in MACC-II towards the operationalisation of all its components. Foreseen developments and research for improving its performances are discussed in the conclusion.

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(1), isabel Martínez (1) . introducciónLa transmisión de información al público sobre el estado de la contaminación del aire ha recibido una creciente atención en los planes de calidad del aire, ya que una de las tareas que debe llevarse a cabo por parte de las administraciones para cumplir con las directivas europeas es poner a disposición del público la información adecuada sobre la calidad del aire.En este aspecto, la Agencia Estatal de Meteorología proporciona información sobre la composición química de la atmósfera mediante un sistema de predicción de la calidad del aire con el que se realizan predicciones operativas de diferentes contaminantes sobre la península ibérica y Baleares. Dicho sistema de predicción se basa en el modelo de transporte químico MOCAGE (Modèle de Chimie Atmosphérique de Grande Échelle), desarrollado por Météo-France y utilizado en AEMET en virtud de un acuerdo de colaboración con el citado servicio meteorológico. Actualmente, el sistema utiliza el inventario de emisiones TNO-MACCIII (2011), que es la referencia más actual en inventarios de alta resolución sobre Europa que hay disponible.Las predicciones se elaboran diariamente y comprenden diferentes especies químicas como son el ozono (O 3 ), el dióxido de nitrógeno (NO 2 ), el monóxido de nitrógeno (NO), el monóxido de carbono (CO) o el dióxido de azufre (SO 2 ). Recientemente además, se han incluido dos nuevas variables de material particulado de tamaños menores de 10 micras y menores de 2,5 micras, el PM10 y el PM2.5 respectivamente, tras implantar una nueva versión del modelo que incluye el pronóstico de aerosoles.Sin embargo, esta información puede llegar a ser demasiado técnica y compleja si tenemos en cuenta que debe informar a un público que no tiene por qué ser experto en materia de calidad del aire. Es por ello que el siguiente objetivo es presentar las predicciones de calidad del aire de una manera más clara, sencilla y comprensible a través de un índice de calidad del aire.Un índice de calidad del aire es un valor adimensional, establecido a partir de la información procedente de las directivas vigentes relacionadas con los distintos contaminantes atmosféricos. En nuestro caso, el objetivo es presentar un índice diario de calidad del aire calculado con las predicciones que se obtienen de manera operativa mediante el sistema de predicción de calidad del aire de AEMET.Se han utilizado diferentes índices como el Air Quality Index for Health o AQIH