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Concorezzo, Italy

Enviroware srl

Concorezzo, Italy

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Brunner D.,Empa - Swiss Federal Laboratories for Materials Science and Technology | Savage N.,UK Met Office | Jorba O.,Barcelona Supercomputing Center | Eder B.,U.S. Environmental Protection Agency | And 33 more authors.
Atmospheric Environment | Year: 2015

Air pollution simulations critically depend on the quality of the underlying meteorology. In phase 2 of the Air Quality Model Evaluation International Initiative (AQMEII-2), thirteen modeling groups from Europe and four groups from North America operating eight different regional coupled chemistry and meteorology models participated in a coordinated model evaluation exercise. Each group simulated the year 2010 for a domain covering either Europe or North America or both. Here were present an operational analysis of model performance with respect to key meteorological variables relevant for atmospheric chemistry processes and air quality. These parameters include temperature and wind speed at the surface and in the vertical profile, incoming solar radiation at the ground, precipitation, and planetary boundary layer heights. A similar analysis was performed during AQMEII phase 1 (Vautard et al., 2012) for offline air quality models not directly coupled to the meteorological model core as the model systems investigated here. Similar to phase 1, we found significant overpredictions of 10-m wind speeds by most models, more pronounced during night than during daytime. The seasonal evolution of temperature was well captured with monthly mean biases below 2 K over all domains. Solar incoming radiation, precipitation and PBL heights, on the other hand, showed significant spread between models and observations suggesting that major challenges still remain in the simulation of meteorological parameters relevant for air quality and for chemistry-climate interactions at the regional scale. © 2014 The Authors.


Curci G.,University of L'Aquila | Hogrefe C.,U.S. Environmental Protection Agency | Bianconi R.,Enviroware srl | Im U.,European Commission - Joint Research Center Ispra | And 20 more authors.
Atmospheric Environment | Year: 2015

The calculation of aerosol optical properties from aerosol mass is a process subject to uncertainty related to necessary assumptions on the treatment of the chemical species mixing state, density, refractive index, and hygroscopic growth. In the framework of the AQMEII-2 model intercomparison, we used the bulk mass profiles of aerosol chemical species sampled over the locations of AERONET stations across Europe and North America to calculate the aerosol optical properties under a range of common assumptions for all models. Several simulations with parameters perturbed within a range of observed values are carried out for July 2010 and compared in order to infer the assumptions that have the largest impact on the calculated aerosol optical properties. We calculate that the most important factor of uncertainty is the assumption about the mixing state, for which we estimate an uncertainty of 30-35% on the simulated aerosol optical depth (AOD) and single scattering albedo (SSA). The choice of the core composition in the core-shell representation is of minor importance for calculation of AOD, while it is critical for the SSA. The uncertainty introduced by the choice of mixing state choice on the calculation of the asymmetry parameter is the order of 10%. Other factors of uncertainty tested here have a maximum average impact of 10% each on calculated AOD, and an impact of a few percent on SSA and g. It is thus recommended to focus further research on a more accurate representation of the aerosol mixing state in models, in order to have a less uncertain simulation of the related optical properties. © 2014 The Authors.


Im U.,European Commission - Joint Research Center Ispra | Bianconi R.,Enviroware Srl | Solazzo E.,European Commission - Joint Research Center Ispra | Kioutsioukis I.,European Commission - Joint Research Center Ispra | And 38 more authors.
Atmospheric Environment | Year: 2015

The second phase of the Air Quality Model Evaluation International Initiative (AQMEII) brought together sixteen modeling groups from Europe and North America, running eight operational online-coupled air quality models over Europe and North America on common emissions and boundary conditions. With the advent of online-coupled models providing new capability to quantify the effects of feedback processes, the main aim of this study is to compare the response of coupled air quality models to simulate levels of O3 over the two continental regions. The simulated annual, seasonal, continental and sub-regional ozone surface concentrations and vertical profiles for the year 2010 have been evaluated against a large observational database from different measurement networks operating in Europe and North America. Results show a general model underestimation of the annual surface ozone levels over both continents reaching up to 18% over Europe and 22% over North America. The observed temporal variations are successfully reproduced with correlation coefficients larger than 0.8. Results clearly show that the simulated levels highly depend on the meteorological and chemical configurations used in the models, even within the same modeling system. The seasonal and sub-regional analyses show the models' tendency to overestimate surface ozone in all regions during autumn and underestimate in winter. Boundary conditions strongly influence ozone predictions especially during winter and autumn, whereas during summer local production dominates over regional transport. Daily maximum 8-h averaged surface ozone levels below 50-60 μg m-3 are overestimated by all models over both continents while levels over 120-140 μg m-3 are underestimated, suggesting that models have a tendency to severely under-predict high O3 values that are of concern for air quality forecast and control policy applications. © 2014 Elsevier Ltd.


Forkel R.,Karlsruhe Institute of Technology | Balzarini A.,RSE SpA | Baro R.,University of Murcia | Bianconi R.,Enviroware srl | And 12 more authors.
Atmospheric Environment | Year: 2015

As a contribution to phase2 of the Air Quality Model Evaluation International Initiative (AQMEII), eight different simulations for the year 2010 were performed with WRF-Chem for the European domain. The four simulations using RADM2 gas-phase chemistry and the MADE/SORGAM aerosol module are analyzed in this paper. The simulations included different degrees of aerosol-meteorology feedback, ranging from no aerosol effects at all to the inclusion of the aerosol direct radiative effect as well as aerosol cloud interactions and the aerosol indirect effect. In addition, a modification of the RADM2 gas phase chemistry solver was tested. The yearly simulations allow characterizing the average impact of the consideration of feedback effects on meteorology and pollutant concentrations and an analysis of the seasonality. Pronounced feedback effects were found for the summer 2010 Russian wildfire episode, where the direct aerosol effect lowered the seasonal mean solar radiation by 20Wm-3 and seasonal mean temperature by 0.25°. This might be considered as a lower limit as it must be taken into account that aerosol concentrations were generally underestimated by up to 50%. The high aerosol concentrations from the wildfires resulted in a 10%-30% decreased precipitation over Russia when aerosol cloud interactions were taken into account. The most pronounced and persistent feedback due to the indirect aerosol effect was found for regions with very low aerosol concentrations like the Atlantic and Northern Europe. The low aerosol concentrations in this area result in very low cloud droplet numbers between 5 and 100dropletscm-1 and a 50-70% lower cloud liquid water path. This leads to an increase in the downward solar radiation by almost 50%. Over Northern Scandinavia, this results in almost one degree higher mean temperatures during summer. In winter, the decreased liquid water path resulted in increased long-wave cooling and a decrease of the mean temperature by almost the same amount. Precipitation over the Atlantic Ocean was found to be enhanced by up to 30% when aerosol cloud interactions were taken into account. The inclusion of aerosol cloud interactions can reduce the bias or improve correlations of simulated precipitation for some episodes and regions. However, the domain and time averaged performance statistics do not indicate a general improvement when aerosol feedbacks are taken into account. Except for conditions with either very low or very high aerosol concentrations, the impact of aerosol feedbacks on pollutant distributions was found to be smaller than the effect of the choice of the chemistry module or wet deposition implementation. © 2014 The Authors.


Vautard R.,CEA Saclay Nuclear Research Center | Moran M.D.,Environment Canada | Solazzo E.,European Commission - Joint Research Center Ispra | Gilliam R.C.,U.S. Environmental Protection Agency | And 14 more authors.
Atmospheric Environment | Year: 2012

Accurate regional air pollution simulation relies strongly on the accuracy of the mesoscale meteorological simulation used to drive the air quality model. The framework of the Air Quality Model Evaluation International Initiative (AQMEII), which involved a large international community of modeling groups in Europe and North America, offered a unique opportunity to evaluate the skill of mesoscale meteorological models for two continents for the same period. More than 20 groups worldwide participated in AQMEII, using several meteorological and chemical transport models with different configurations. The evaluation has been performed over a full year (2006) for both continents. The focus for this particular evaluation was meteorological parameters relevant to air quality processes such as transport and mixing, chemistry, and surface fluxes. The unprecedented scale of the exercise (one year, two continents) allowed us to examine the general characteristics of meteorological models' skill and uncertainty. In particular, we found that there was a large variability between models or even model versions in predicting key parameters such as surface shortwave radiation. We also found several systematic model biases such as wind speed overestimations, particularly during stable conditions. We conclude that major challenges still remain in the simulation of meteorology, such as nighttime meteorology and cloud/radiation processes, for air quality simulation. © 2011 Elsevier Ltd.


Solazzo E.,European Commission - Joint Research Center Ispra | Bianconi R.,Enviroware srl | Vautard R.,French Climate and Environment Sciences Laboratory | Appel K.W.,U.S. Environmental Protection Agency | And 33 more authors.
Atmospheric Environment | Year: 2012

More than ten state-of-the-art regional air quality models have been applied as part of the Air Quality Model Evaluation International Initiative (AQMEII). These models were run by twenty independent groups in Europe and North America. Standardised modelling outputs over a full year (2006) from each group have been shared on the web-distributed ENSEMBLE system, which allows for statistical and ensemble analyses to be performed by each group. The estimated ground-level ozone mixing ratios from the models are collectively examined in an ensemble fashion and evaluated against a large set of observations from both continents. The scale of the exercise is unprecedented and offers a unique opportunity to investigate methodologies for generating skilful ensembles of regional air quality models outputs. Despite the remarkable progress of ensemble air quality modelling over the past decade, there are still outstanding questions regarding this technique. Among them, what is the best and most beneficial way to build an ensemble of members? And how should the optimum size of the ensemble be determined in order to capture data variability as well as keeping the error low? These questions are addressed here by looking at optimal ensemble size and quality of the members. The analysis carried out is based on systematic minimization of the model error and is important for performing diagnostic/probabilistic model evaluation. It is shown that the most commonly used multi-model approach, namely the average over all available members, can be outperformed by subsets of members optimally selected in terms of bias, error, and correlation. More importantly, this result does not strictly depend on the skill of the individual members, but may require the inclusion of low-ranking skill-score members. A clustering methodology is applied to discern among members and to build a skilful ensemble based on model association and data clustering, which makes no use of priori knowledge of model skill. Results show that, while the methodology needs further refinement, by optimally selecting the cluster distance and association criteria, this approach can be useful for model applications beyond those strictly related to model evaluation, such as air quality forecasting. © 2012 Elsevier Ltd.


Solazzo E.,European Commission - Joint Research Center Ispra | Bianconi R.,Enviroware srl | Pirovano G.,INERIS | Pirovano G.,RSE SpA | And 29 more authors.
Atmospheric Environment | Year: 2012

Ten state-of-the-science regional air quality (AQ) modeling systems have been applied to continental-scale domains in North America and Europe for full-year simulations of 2006 in the context of Air Quality Model Evaluation International Initiative (AQMEII), whose main goals are model inter-comparison and evaluation. Standardised modeling outputs from each group have been shared on the web-distributed ENSEMBLE system, which allows statistical and ensemble analyses to be performed. In this study, the one-year model simulations are inter-compared and evaluated with a large set of observations for ground-level particulate matter (PM 10 and PM 2.5) and its chemical components. Modeled concentrations of gaseous PM precursors, SO 2 and NO 2, have also been evaluated against observational data for both continents. Furthermore, modeled deposition (dry and wet) and emissions of several species relevant to PM are also inter-compared. The unprecedented scale of the exercise (two continents, one full year, fifteen modeling groups) allows for a detailed description of AQ model skill and uncertainty with respect to PM.Analyses of PM 10 yearly time series and mean diurnal cycle show a large underestimation throughout the year for the AQ models included in AQMEII. The possible causes of PM bias, including errors in the emissions and meteorological inputs (e.g., wind speed and precipitation), and the calculated deposition are investigated. Further analysis of the coarse PM components, PM 2.5 and its major components (SO 4, NH 4, NO 3, elemental carbon), have also been performed, and the model performance for each component evaluated against measurements. Finally, the ability of the models to capture high PM concentrations has been evaluated by examining two separate PM 2.5 episodes in Europe and North America. A large variability among models in predicting emissions, deposition, and concentration of PM and its precursors during the episodes has been found. Major challenges still remain with regards to identifying and eliminating the sources of PM bias in the models. Although PM 2.5 was found to be much better estimated by the models than PM 10, no model was found to consistently match the observations for all locations throughout the entire year. © 2012 Elsevier Ltd.


Solazzo E.,European Commission - Joint Research Center Ispra | Galmarini S.,European Commission - Joint Research Center Ispra | Bianconi R.,Enviroware srl | Rao T.,U.S. Environmental Protection Agency
NATO Science for Peace and Security Series C: Environmental Security | Year: 2013

Ten state-of-the-science regional air quality (AQ) modeling systems have been applied to continental-scale domains in North America and Europe for full-year simulations of 2006 for the Air Quality Model Evaluation International Initiative (AQMEII), whose main goals are model inter-comparison and model evaluation. Model simulations are inter-compared and evaluated with a large set of observations for ground-level particulate matter (PM10 and PM2.5) and its chemical components. Analyses of PM10 time series show a large model underestimation throughout the year. Moreover, a large variability among models in predictions of emissions, deposition, and concentration of PM and its precursors has been found. © Springer Science+Business Media Dordrecht 2014.


Galmarini S.,European Commission - Joint Research Center Ispra | Bianconi R.,Enviroware srl | Appel W.,U.S. Environmental Protection Agency | Solazzo E.,European Commission - Joint Research Center Ispra | And 5 more authors.
Atmospheric Environment | Year: 2012

The complexity of air quality modeling systems, air quality monitoring data make ad-hoc systems for model evaluation important aids to the modeling community. Among those are the ENSEMBLE system developed by the EC-Joint Research Center, and the AMET software developed by the US-EPA. These independent systems provide two examples of state of the art tools to support model evaluation. The two systems are described here mostly from the point of view of the support to air quality model users or developers rather than the technological point of view. While ENSEMBLE is a web based platform for model evaluation that allows the collection, share and treatment of model results as well as monitoring data, AMET is a standalone tool that works directly on single model data. The complementarity of the two approaches makes the two systems optimal for operational, diagnostic and probabilistic evaluations. ENSEMBLE and AMET have been extended in occasion of the AQMEII two-continent exercise and the new developments are described in this paper, together with those foreseen for the future. © 2011 Elsevier Ltd.

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