Stefano Galmarini

Air Quality Model Evaluation International Initiative (AQMEII): Advancing the State of the Science in Regional Photochemical Modeling and Its Applications

aMErICan METEOrOLOGICaL SOCIETy | 23 AffiliAtions: Rao—Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina; GalmaRini—Institute of Environment and Sustainability, European Commission, Joint Research Center, Ispra, Italy; Puckett—Air Quality Research Division, Environment Canada, Toronto, Ontario, Canada Corresponding Author: Dr. S. T. Rao, Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 E-mail: rao.st@epa.gov

Turbulence Structure and Exchange Processes in an Alpine Valley: The Riviera Project.

During a special observing period (SOP) of the Mesoscale Alpine Programme (MAP), boundary layer processes in highly complex topography were investigated in the Riviera Valley in southern Switzerland. The main focus was on the turbulence structure and turbulent exchange processes near the valley surfaces and free troposphere. Due to the anticipated spatial inhomogeneity, a number of different turbulence probes were deployed on a cross section through the valley. Together with a suite of more conventional instrumentation, to observe mean meteorological structure in the valley, this effort yielded a highly valuable dataset. The latter is presently being exploited to yield insight into the turbulence structure in very complex terrain, and its relation to flow regimes and associated mean flow characteristics. Specific questions, such as a detailed investigation of turbulent exchange processes over complex topography and the validity of surface exchange parameterizations in numerical models for such surfaces, t...

Forecasting the consequences of accidental releases of radionuclides in the atmosphere from ensemble dispersion modelling

The RTMOD system is presented as a tool for the intercomparison of long-range dispersion models as well as a system for support of decision making. RTMOD is an internet-based procedure that collects the results of more than 20 models used around the world to predict the transport and deposition of radioactive releases in the atmosphere. It allows the real-time acquisition of model results and their intercomparison. Taking advantage of the availability of several model results, the system can also be used as a tool to support decision making in case of emergency. The new concept of ensemble dispersion modelling is introduced which is the basis for the decision-making application of RTMOD. New statistical parameters are presented that allow gathering the results of several models to produce a single dispersion forecast. The devised parameters are presented and tested on the results of RTMOD exercises.

Web-based system for decision support in case of emergency: ensemble modelling of long-range atmospheric dispersion of radionuclides

Abstract A system is presented that allows the centralised real-time acquisition, analysis, and redistribution of the results produced by a community of atmospheric long-range transport and dispersion models. The models are used operationally by national authorities in different countries in Europe and around the world to forecast the long-range (100–2000 km) dispersion of accidental releases of radioactive material in the atmosphere. The ENSEMBLE system is conceived to allow decision makers and scientific advisors to decision makers to consult in real-time several atmospheric predictions produced during and after the release. The availability of several model predictions through the system allows collating several results into few concise representations and the use of the multi-model ensemble technique to determine the degree of agreement of model results in the absence of monitoring data. The paper presents the technical concepts behind the ENSEMBLE system, the methodology adopted to acquire several model predictions in real-time and to produce the multi-model analysis. Some examples of application to fictitious releases are also presented. ENSEMBLE has been specifically designed for the management of long-range atmospheric releases as a consequence of nuclear accidents but its concept is applicable to several other fields of environmental sciences.

Technical note: Coordination and harmonization of the multi-scale, multi-model activities HTAP2, AQMEII3, and MICS-Asia3: simulations, emission inventories, boundary conditions, and model output formats

We present an overview of the coordinated global numerical modelling experiments performed during 2012–2016 by the Task Force on Hemispheric Transport of Air Pollution (TF HTAP), the regional experiments by the Air Quality Model Evaluation International Initiative (AQMEII) over Europe and North America, and the Model Intercomparison Study for Asia (MICS-Asia). To improve model estimates of the impacts of intercontinental transport of air pollution on climate, ecosystems, and human health and to answer a set of policy-relevant questions, these three initiatives performed emission perturbation modelling experiments consistent across the global, hemispheric, and continental/regional scales. In all three initiatives, model results are extensively compared against monitoring data for a range of variables (meteorological, trace gas concentrations, and aerosol mass and composition) from different measurement platforms (ground measurements, vertical profiles, airborne measurements) collected from a number of sources. Approximately 10 to 25 modelling groups have contributed to each initiative, and model results have been managed centrally through three data hubs maintained by each initiative. Given the organizational complexity of bringing together these three initiatives to address a common set of policy-relevant questions, this publication provides the motivation for the modelling activity, the rationale for specific choices made in the model experiments, and an overview of the organizational structures for both the modelling and the measurements used and analysed in a number of modelling studies in this special issue.

Evaluation and error apportionment of an ensemble of atmospheric chemistry transport modeling systems : Multivariable temporal and spatial breakdown

Through the comparison of several regional-scale chemistry transport modeling systems that simulate meteorology and air quality over the European and North American continents, this study aims at (i) apportioning error to the responsible processes using timescale analysis, (ii) helping to detect causes of model error, and (iii) identifying the processes and temporal scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition, and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overallsense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance, and covariance) can help assess the nature and quality of the error. Each of the error components is analyzed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intraday) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emission and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high interdependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. This will require evaluaion methods that are able to frame the impact on error of processes, conditions, and fluxes at the surface. For example, error due to emission and boundary conditions is dominant for primary species (CO, particulate matter (PM)), while errors due to meteorology and chemistry are most relevant to secondary species, such as ozone. Some further aspects emerged whose interpretation requires additional consideration, such as the uniformity of the synoptic error being region- and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.

The Fukushima-137Cs deposition case study: properties of the multi-model ensemble

In this paper we analyse the properties of an eighteen-member ensemble generated by the combination of five atmospheric dispersion modelling systems and six meteorological data sets. The models have been applied to the total deposition of (137)Cs, following the nuclear accident of the Fukushima power plant in March 2011. Analysis is carried out with the scope of determining whether the ensemble is reliable, sufficiently diverse and if its accuracy and precision can be improved. Although ensemble practice is becoming more and more popular in many geophysical applications, good practice guidelines are missing as to how models should be combined for the ensembles to offer an improvement over single model realisations. We show that the ensemble of models share large portions of bias and variance and make use of several techniques to further show that subsets of models can explain the same amount of variance as the full ensemble mean with the advantage of being poorly correlated, allowing to save computational resources and reduce noise (and thus improving accuracy). We further propose and discuss two methods for selecting subsets of skilful and diverse members, and prove that, in the contingency of the present analysis, their mean outscores the full ensemble mean in terms of both accuracy (error) and precision (variance).

Ensemble Techniques to Improve Air Quality Assessment: Focus on O3 and PM

Five air quality models were applied over Portugal for July 2006 with an ensemble purpose. These models were used, with their own meteorology, parameterizations, boundary conditions and chemical mechanisms, but with the same emission data. The validation of the individual models and its ensemble for ozone (O3) and particulate matter was performed using monitoring data from 22 background stations over Portugal. After removing the bias from each model, different ensemble techniques were applied and compared. Besides the median, several weighted ensemble approaches were tested and intercompared: static (SLR) and dynamic (DLR) multiple linear regressions (using less-square optimization method) and the Bayesian Model Averaging (BMA) methodology. The goal of the comparison is to estimate to what extent the ensemble analysis is an improvement with respect to the single model results. The obtained results revealed that no one of the 4 tested ensembles clearly outperforms the others on the basis of statistical parameters and probabilistic analysis (reliability and resolution properties). Nevertheless, statistical results have shown that the application of the weights slightly improves ensemble performance when compared to those obtained from the median ensemble. The same statistical analysis together with the probabilistic measures demonstrates that the SLR and BMA methods are the best performers amongst the assessed methodologies.

Independent Data Model Selection for Ensemble Dispersion Forecasting

This work aims at introducing an approach to analyze the independence between different data model in a multi-model ensemble context. The models belong to operational long-range transport and dispersion models, but they are also used for the real-time simulation of pollutant dispersion or the accidental release of radioactive nuclides in the atmosphere. In order to compare models, an approach based on the hierarchical agglomeration of distributions of predicted radionuclide concentrations is proposed. We use two different similarity measures: Negentropy information and Kullback-Leibler divergence. These approaches are used to analyze the data obtained during the ETEX-1 exercise, and we show how to exploit these approaches to select subsets of independent models whose performance is comparable to those from the whole ensemble.

Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3

The impact of air pollution on human health and the associated external costs in Europe and the United States (US) for the year 2010 are modeled by a multi-model ensemble of regional models in the frame of the third phase of the Air Quality Modelling Evaluation International Initiative (AQMEII3). The modeled surface concentrations of O3, CO, SO2 and PM2.5 are used as input to the Economic Valuation of Air Pollution (EVA) system to calculate the resulting health impacts and the associated external costs from each individual model. Along with a base case simulation, additional runs were performed introducing 20 % anthropogenic emission reductions both globally and regionally in Europe, North America and east Asia, as defined by the second phase of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP2). Health impacts estimated by using concentration inputs from different chemistry–transport models (CTMs) to the EVA system can vary up to a factor of 3 in Europe (12 models) and the United States (3 models). In Europe, the multi-model mean total number of premature deaths (acute and chronic) is calculated to be 414 000, while in the US, it is estimated to be 160 000, in agreement with previous global and regional studies. The economic valuation of these health impacts is calculated to be EUR 300 billion and 145 billion in Europe and the US, respectively. A subset of models that produce the smallest error compared to the surface observations at each time step against an all-model mean ensemble results in increase of health impacts by up to 30 % in Europe, while in the US, the optimal ensemble mean led to a decrease in the calculated health impacts by ~ 11 %. A total of 54 000 and 27 500 premature deaths can be avoided by a 20 % reduction of global anthropogenic emissions in Europe and the US, respectively. A 20 % reduction of North American anthropogenic emissions avoids a total of ~ 1000 premature deaths in Europe and 25 000 total premature deaths in the US. A 20 % decrease of anthropogenic emissions within the European source region avoids a total of 47 000 premature deaths in Europe. Reducing the east Asian anthropogenic emissions by 20 % avoids ~ 2000 total premature deaths in the US. These results show that the domestic anthropogenic emissions make the largest impacts on premature deaths on a continental scale, while foreign sources make a minor contribution to adverse impacts of air pollution.