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Starting from the regional air quality forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS), we propose a novel post-processing approach to improve and downscale results on a finer scale. Our approach is based on the combination of ensemble model output statistics (EMOS) with a spatio-temporal interpolation process performed through the stochastic partial differential equation–integrated nested laplace approximation (SPDE-INLA). Our interpolation approach includes several spatial and spatio-temporal predictors, including meteorological variables. A use case is provided that scales down the CAMS forecasts on the Italian peninsula. The calibration is focused on the concentrations of several air quality pollutants (PM 10 , PM 2.5 , NO 2 , and O 3) at a daily resolution from a set of 750 monitoring sites, distributed throughout the Italian country. Our results show the key role that conditioning variables play in improving the forecast capabilities of ensemble predictions, thus allowing for a net improvement in the calibration with respect to ordinary EMOS strategies. From a deterministic point of view, the performance of the predictive model shows a significant improvement in the performance of the raw ensemble forecast, with an almost-zero bias, significantly reduced root mean square errors, and correlations that are almost always higher than 0.9 for each pollutant; moreover, the post-processing approach is able to significantly improve the prediction of exceedances, even for very low thresholds, such as those recently recommended by the World Health Organisation. This is particularly significant if a forecasting approach is used to predict air quality conditions and plan adequate human health protection measures, even for low alert thresholds. From a probabilistic point of view, the quality of the forecast was verified in terms of reliability and credible intervals. After post-processing, the predictive probability density functions were sharp and much better calibrated than the raw ensemble forecast. Finally, we present some additional results based on a set of gridded (4 km × 4 km) maps covering the entire Italian country for the detection of areas where pollution peaks occur (exceedances of the current and/or proposed regulatory thresholds). [ABSTRACT FROM AUTHOR]

Starting from the regional air quality forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS), we propose a novel post-processing approach to improve and downscale results on a finer scale. Our approach is based on the combination of Ensemble Model Output Statistics (EMOS) with a spatio-temporal interpolation process performed through the Stochastic Partial Differential Equation-Integrated Nested Laplace Approximation (SPDE-INLA). Our interpolation approach includes several spatial and spatio-temporal predictors, including meteorological variables. A use-case is provided, scaling down the CAMS forecasts on the Italian peninsula. The calibration is focused on the concentrations of several air quality pollutants (PM10, PM2.5, NO2 and O3) at daily resolution from a set of 750 monitoring sites, distributed throughout the Italian country. Our results show the key role played by conditioning variables to improve the forecast capabilities of ensemble predictions, thus allowing a net improvement of the calibration with respect to ordinary EMOS strategies. From a deterministic point of view, the predictive model performance shows a significant improvement of the performance of the raw ensemble forecast, with an almost zero bias, significantly reduced root mean square errors and correlations almost always higher than 0.9 for each pollutant; moreover, the post-processing approach is able to significantly improve the prediction of exceedances, even for very low thresholds, such as those recently recommended by the World Health Organisation. This is particularly significant if a forecasting approach is to be used to predict air quality conditions and plan adequate human health protection measures, even for low alert thresholds. From a probabilistic point of view, the forecast quality was verified in terms of reliability and credible intervals. After post-processing, the predictive probability density functions were sharp, and much better calibrated than the raw ensemble forecast. Finally, we present some additional outcomes based on a set of gridded (4 km x 4 km) daily maps covering the whole Italian country, for the detection of areas where pollution peaks forecasts (exceedances of the regulatory thresholds) occur. [ABSTRACT FROM AUTHOR]

The main uncertainties regarding the estimation of changes in the Earth's energy budget are related to the role of atmospheric aerosols. These changes are caused by aerosol–radiation (ARIs) and aerosol–cloud interactions (ACIs), which heavily depend on aerosol properties. Since the 1980s, many international modeling initiatives have studied atmospheric aerosols and their climate effects. Phase 3 of the Air Quality Modelling Evaluation International Initiative (AQMEII) focuses on evaluating and intercomparing regional and linked global/regional modeling systems by collaborating with the Task Force on the Hemispheric Transport of Air Pollution Phase 2 (HTAP2) initiative. Within this framework, the main aim of this work is the assessment of the representation of aerosol optical depth (AOD) and the Ångström exponent (AE) in AQMEII Phase 3 simulations over Europe. The evaluation was made using remote-sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard the Terra and Aqua platforms, and the instruments belonging to the ground-based Aerosol Robotic Network (AERONET) and the Maritime Aerosol Network (MAN). Overall, the skills of AQMEII simulations when representing AOD (mean absolute errors from 0.05 to 0.30) produced lower errors than for the AE (mean absolute errors from 0.30 to 1). Regardless of the models or the emissions used, models were skillful at representing the low and mean AOD values observed (below 0.5). However, high values (around 1.0) were overpredicted for biomass burning episodes, due to an underestimation in the common fires' emissions, and were overestimated for coarse particles – principally desert dust – related to the boundary conditions. Despite this behavior, the spatial and temporal variability of AOD was better represented by all the models than AE variability, which was strongly underestimated in all the simulations. Noticeably, the impact of the model selection when representing aerosol optical properties is higher than the use of different emission inventories. On the other hand, the influence of ARIs and ACIs has a little visible impact compared to the impact of the model used. [ABSTRACT FROM AUTHOR]

The main uncertainties in estimates of changes in the Earth's energy budget are related to the role of atmospheric aerosols. These changes are caused mainly by aerosol-radiation (ARI) and aerosol-cloud interactions (ACI), which heavily depend on aerosol properties. From the 1980s, many international modelling initiatives have studied atmospheric aerosols and their climate effects. Phase 3 of the Air Quality Model Evaluation International Initiative (AQMEII) focuses on evaluating and intercomparing regional and linked global/regional modelling systems by collaborating with the Task Force on the Hemispheric Transport of Air Pollution Phase 2 (HTAP2) initiative. Within this framework, the main aim of this work was to evaluate the representation of aerosol optical depth (AOD) and the Ångström exponent (AE) by the AQMEII Phase 3 simulations over Europe. The evaluation was made using satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on board the Terra and Aqua platforms. The results indicated that the skills of AQMEII simulations in the AOD representation produced fewer errors than in the AE. Regardless of the models and emissions used, models were skilful at representing the low and medium AOD values observed (below 0.5). However, high values (close to 1.0) were underestimated for biomass burning episodes, and were overestimated for desert dust contributions, related mainly to emission and boundary conditions. Despite this behaviour, the spatial and temporal variability of this variable was well-represented by all the models. Generally, the AE evaluation showed more serious errors than the AOD evaluation. Moreover, the observed variability of this parameter was strongly underestimated in all the simulations. [ABSTRACT FROM AUTHOR]