&amp;lt;i&amp;gt;Est modus in rebus&amp;lt;/i&amp;gt;: analytical properties of multi-model ensembles

Potempski, S.; Galmarini, Stefano

doi:10.5194/acp-9-9471-2009

Cited by 63 publications

(79 citation statements)

References 47 publications

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“…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). Figure 6 shows the effect of the various ensemble treatments for the two groups of models separately and presented as a Taylor diagram.…”

Section: Analysis Of the Ensembles And Building The Hybrid One 41 Enmentioning

confidence: 99%

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

et al. 2018

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Abstract. In this study we introduce a hybrid ensemble consisting of air quality models operating at both the global and regional scale. The work is motivated by the fact that these different types of models treat specific portions of the atmospheric spectrum with different levels of detail, and it is hypothesized that their combination can generate an ensemble that performs better than mono-scale ensembles. A detailed analysis of the hybrid ensemble is carried out in the attempt to investigate this hypothesis and determine the real benefit it produces compared to ensembles constructed from only global-scale or only regional-scale models. The study utilizes 13 regional and 7 global models participating in the Hemispheric Transport of Air Pollutants phase 2 (HTAP2)-Air Quality Model Evaluation International Initiative phase 3 (AQMEII3) activity and focuses on surface ozone concentrations over Europe for the year 2010. Observations from 405 monitoring rural stations are used for the evaluation of the ensemble performance. The analysis first compares the modelled and measured power spectra of all models and then assesses the properties of the mono-scale ensembles, particularly their level of redundancy, in order to inform the process of constructing the hybrid ensemble. This study has been conducted in the attempt to identify that the improvements obtained by the hybrid ensemble relative to the mono-scale ensembles can be attributed to its hybrid nature. The improvements are visible in a slight increase of the diversity (4 % for the hourly time series, 10 % for the daily maximum time series) and a smaller improvement of the accuracy compared to diversity. Root mean square error (RMSE) improved by 13-16 % compared to G and by 2-3 % compared to R. Probability of detection (POD) and false-alarm rate (FAR) show a remarkable improvement, with a steep increase in the largest POD values and smallest values of FAR across the concentration ranges. The results show that the optimal set is constructed from an equal number of global and regional models at only 15 % of the stations. This implies that for the majority of the cases the regional-scale set of models governs the ensemble. However given the high degree of redundancy that characterizes the regional-scale models, no further improvement could be expected in the ensemble performance by adding yet more regional models to it. Therefore the improvement obtained with the hybrid set can confidently be attributed to the different nature of the global models. The study strongly reaffirms the importance of an in-depth inspection of any ensemble of opportunity in order to extract the maximum amount of information and to have full control over the data used in the construction of the ensemble.

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Section: Analysis Of the Ensembles And Building The Hybrid One 41 Enmentioning

confidence: 99%

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

et al. 2018

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“…The model intercomparison studies have demonstrated that a model ensemble generally provides the best performance in comparison to observations, (e.g. Vautard et al, 2009) compared to the performance of individual models, although this requires that models or model versions are independent (Potempski and Galmarini, 2009). …”

Section: Model Evaluationmentioning

confidence: 99%

Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model

et al. 2017

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Abstract. The development and application of chemistry transport models has a long tradition. Within the Netherlands the LOTOS-EUROS model has been developed by a consortium of institutes, after combining its independently developed predecessors in 2005. Recently, version 2.0 of the model was released as an open-source version. This paper presents the curriculum vitae of the model system, describing the model's history, model philosophy, basic features and a validation with EMEP stations for the new benchmark year 2012, and presents cases with the model's most recent and key developments. By setting the model developments in context and providing an outlook for directions for further development, the paper goes beyond the common model description.With an origin in ozone and sulfur modelling for the models LOTOS and EUROS, the application areas were gradually extended with persistent organic pollutants, reactive nitrogen, and primary and secondary particulate matter. After the combination of the models to LOTOS-EUROS in 2005, the model was further developed to include new source parametrizations (e.g. road resuspension, desert dust, wildfires), applied for operational smog forecasts in the Netherlands and Europe, and has been used for emission scenarios, source apportionment, and long-term hindcast and climate change scenarios. LOTOS-EUROS has been a front-runner in data assimilation of ground-based and satellite observations and has participated in many model intercomparison studies. The model is no longer confined to applications over Europe but is also applied to other regions of the world, e.g. China. The increasing interaction with emission experts has also contributed to the improvement of the model's performance. The philosophy for model development has always been to use knowledge that is state of the art and proven, to keep a good balance in the level of detail of process description and accuracy of input and output, and to keep a good record on the effect of model changes using benchmarking and validation. The performance of v2.0 with respect to EMEP observations is good, with spatial correlations around 0.8 or higher for concentrations and wet deposition. Temporal correlations are around 0.5 or higher. Recent innovative applications include source apportionment and data assimilation, particle number modelling, and energy transition scenarios including corresponding land use changes as well as Saharan dust forecasting. Future developments would enable more flexibility with respect to model horizontal and vertical resolution and further detailing of model input data.Published by Copernicus Publications on behalf of the European Geosciences Union.

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“…It has been widely demonstrated (e.g Potempsky and Galmarini, 2009) that when 81 multiple model results are distilled to retain only original and independent 82 contributions (Solazzo et al 2012) and thereafter statistically combined in what is 83 usually called an ensemble, one obtains results that are systematically superior to 84 the performance of the individual models and therefore can provide more accurate 85 and robust assessments or predictions. 86…”

Section: Introduction 80mentioning

confidence: 99%

“…96 Galmarini et al 2004;Tebaldi and Knutti al. (2007); Potempsky and Galmarini, 2009, 97 Solazzo et al 2012Solazzo and Galmarini, 2015), which combine results from 98 different models applied to the same case study, it is customary to consider as 99 members those obtained from a homogeneous group of models. In particular, the 100 scale at which models operate seems to be a discriminant in all such studies that 101 have been performed to date.…”

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confidence: 99%

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

Galmarini¹,

Kioutsioukis²,

Solazzo³

et al. 2018

Preprint

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56 57In this study we introduce a hybrid ensemble consisting of air quality models 58 operating at both the global and regional scale. The work is motivated by the fact 59 that these different types of models treat specific portions of the atmospheric 60 spectrum with different levels of detail and it is hypothesized that their combination 61 can generate an ensemble that performs better than mono-scale ensembles. A 62 detailed analysis of the hybrid ensemble is carried out in the attempt to investigate 63 this hypothesis and determine the real benefit it produces compared to ensembles 64 constructed from only global scale or only regional scale models. The study utilizes 65 13 regional and 7 global models participating in the HTAP2/AQMEII3 activity and 66 focuses on surface ozone concentrations over Europe for the year 2010. 67Observations from 405 monitoring stations are used for the evaluation of the 68 ensemble performance. The analysis first compares the modelled and measured 69 spectra and then assesses the properties of the mono-scale ensembles, particularly 70 their level of redundancy, in order to inform the process of constructing the hybrid 71 ensemble. The main conclusion of this study is that the improvements obtained by 72 the hybrid ensemble relative to the mono-scale ensembles can be attributed to its 73 hybrid nature. Moreover, the optimal set is constructed from an equal number of 74 global and regional models at only 15% of the stations. Finally, the study reaffirms 75 the importance of an in-depth inspection of any ensemble of opportunity in order to 76 extract the maximum amount of information and to have full control over the data 77 used in the construction of the ensemble. 79Atmos. Chem. Phys. Discuss., https://doi

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<i>Est modus in rebus</i>: analytical properties of multi-model ensembles

Cited by 63 publications

References 47 publications

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

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

Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model

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

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