Can the confidence in long range atmospheric transport models be increased? The pan-european experience of ensemble

Galmarini, Stefano; Bianconi, Roberto; Klug, W.; Mikkelsen, Torben; Addis, R.P.; Andronopoulos, S.; Astrup, Poul; Baklanov, Alexander; Bartniki, J.; Bartzis, J.C.; Bellasio, Roberto; Bompay, F.; Buckley, Ron; Bouzom, M.; Champion, H. J.; D’Amours, R.; Davakis, E.; Eleveld, H.; Geertsema, Gertie; Glaab, H.; Kollax, M.; Ilvonen, Mikko; Manning, A. J.; Pechinger, U.; Persson, Christer; Polreich, E.; Potemski, S.; Prodanova, Maria; Saltbones, Jørgen; Harry, Slaper; Sofiev, M.; Syrakov, Dimiter; Sørensen, Jens Havskov; Auwera, L. Van Der; Valkama, I.; Zelazny, R.

doi:10.1093/rpd/nch261

Cited by 20 publications

(9 citation statements)

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“…They differ in terms of concept and numerical code and make use of weather forecasts produced by different either global or limited area models. The basis for the ENSEMBLE operational activity is the multimodel approach, which takes advantage of the diversity of atmospheric dispersion models and tries to reduce the intrinsic uncertainties of various predictions by appropriately analyzing, comparing, and combining the latter in various statistical representations [ Galmarini et al , 2004a; Potempski et al , 2008]. The ENSEMBLE system has been recently coupled with the European Radiological Data Exchange Platform (EURDEP) [ Galmarini et al , 2008], which allows all European countries to rapidly exchange monitoring data on radiological contamination levels in the air in support of nuclear emergency response [ De Cort and de Vries , 1996].…”

Section: Introductionmentioning

confidence: 99%

Bayesian model averaging for emergency response atmospheric dispersion multimodel ensembles: Is it really better? How many data are needed? Are the weights portable?

Potempski¹,

Galmarini²,

Riccio³

et al. 2010

J. Geophys. Res.

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[1] In this paper, we investigate applicability of Bayesian model averaging (BMA) methodology to atmospheric dispersion multimodel ensemble system within the context of emergency response applications. The BMA method can be used both to evaluate model predictions and to combine model results using BMA weighing factors. We analyze time evolution of BMA weights and include a detailed quantitative comparison of different combinations of model results performed by the means of statistical indicators. The analysis allows us to identify similarities and differences among different combined models. Finally, we question the portability of BMA weights among various cases. From the analysis it follows that BMA can be applied in considered problems; however, the median of the model results also performs well and produces more conservative results.Citation: Potempski, S., S. Galmarini, A. Riccio, and G. Giunta (2010), Bayesian model averaging for emergency response atmospheric dispersion multimodel ensembles: Is it really better? How many data are needed? Are the weights portable?,

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Section: Introductionmentioning

confidence: 99%

Bayesian model averaging for emergency response atmospheric dispersion multimodel ensembles: Is it really better? How many data are needed? Are the weights portable?

Potempski¹,

Galmarini²,

Riccio³

et al. 2010

J. Geophys. Res.

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“…Lagrangian dispersion models have also been studied with Monte Carlo sensitivity analyses [ Irwin et al , 1987; Stuart et al , 1996; Bergin et al , 1999; Dabberdt and Miller , 2000]. Unlike photochemical simulations, however, Lagrangian model sensitivity analysis has also utilized variable meteorology arising from different initial conditions or model configurations [ Straume , 2001; Warner et al , 2002], culminating in the multinational ENSEMBLE project [ Galmarini et al , 2004a]. These experiments include realistic meteorological scenarios by design, but in many cases the meteorological variability has been large‐scale in nature.…”

Section: Introductionmentioning

confidence: 99%

Impacts of meteorological uncertainties on ozone pollution predictability estimated through meteorological and photochemical ensemble forecasts

et al. 2007

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[1] This study explores the sensitivity of ozone predictions from photochemical grid point simulations to small meteorological initial perturbations that are realistic in structure and evolution. Through both meteorological and photochemical ensemble forecasts with the Penn State/NCAR mesoscale model MM5 and the EPA Community Multiscale Air Quality (CMAQ) Model-3, the 24-hour ensemble mean of meteorological conditions and the ozone concentrations compared fairly well against the observations for a highozone event that occurred on 30 August during the Texas Air Quality Study of 2000 (TexAQS2000). Moreover, it was also found that there were dramatic uncertainties in the ozone prediction in Houston and surrounding areas due to initial meteorological uncertainties for this event. The high uncertainties in the ozone prediction in Houston and surrounding areas due to small initial wind and temperature uncertainties clearly demonstrated the importance of accurate representation of meteorological conditions for the Houston ozone prediction and the need for probabilistic evaluation and forecasting for air pollution, especially those supported by regulating agencies.

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“…It was certainly a judgemental process, but one supported by computer models and calculations. In the ENSEMBLE project [71,125], several meteorological offices were asked to predict 'in real time' the spread of a contamination plume using details of a hypothetical release that were given to them and their best assessment of the weather at the time. Each office used a mix of judgement on the input parameters to their atmospheric dispersion models and current weather data to produce a time series of maps giving their best assessment of the likely path of the plume.…”

Section: Experts Data and Modelsmentioning

confidence: 99%

Aggregating expert judgement

French

2011

RACSAM

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In a paper written some 25 years ago, I distinguished three contexts in which one might wish to combine expert judgements of uncertainty: the expert problem, the group decision problem and the textbook problem. Over the intervening years much has been written on the first two, which have the focus of a single decision context, but little on the third, though the closely related field of meta-analysis has developed considerably. With many developments in internet technology, particularly in relation to interactivity and communication, the textbook problem is gaining in importance since data and expert judgements can be made available over the web to be used by many different individuals to shape their own beliefs in many different contexts. Moreover, applications such as web-based decision support, e-participation and e-democracy are making algorithmic 'solutions' to the group decision problem attractive, despite many results showing we know that such solutions are, at best, rare and, at worst, illusory. In this paper I survey developments since my earlier paper and note some unresolved issues. Then I turn to how expert judgement might be used within web-based group decision support, as well as in e-participation and e-democracy contexts. The latter points to a growing importance of the textbook problem and suggests that Cooke's principles for scientific reporting of expert judgement studies may need enhancing for such studies to be used by a wider audience.

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Can the confidence in long range atmospheric transport models be increased? The pan-european experience of ensemble

Cited by 20 publications

References 0 publications

Bayesian model averaging for emergency response atmospheric dispersion multimodel ensembles: Is it really better? How many data are needed? Are the weights portable?

Bayesian model averaging for emergency response atmospheric dispersion multimodel ensembles: Is it really better? How many data are needed? Are the weights portable?

Impacts of meteorological uncertainties on ozone pollution predictability estimated through meteorological and photochemical ensemble forecasts

Aggregating expert judgement

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