Preprints
https://doi.org/10.5194/nhess-2021-96
https://doi.org/10.5194/nhess-2021-96

  09 Apr 2021

09 Apr 2021

Review status: this preprint is currently under review for the journal NHESS.

A tailored multi-model ensemble for air traffic management: Demonstration and evaluation for the Eyjafjallajökull eruption in May 2010

Matthieu Plu1, Barbara Scherllin-Pirscher2, Delia Arnold Arias2, Rocio Baro2, Guillaume Bigeard1, Luca Bugliaro3, Ana Carvalho4, Laaziz El Amraoui1, Kurt Eschbacher5, Marcus Hirtl2, Christian Maurer2, Marie Mulder2, Dennis Piontek3, Lennart Robertson4, Carl-Herbert Rokitansky5, Fritz Zobl5, and Raimund Zopp6 Matthieu Plu et al.
  • 1CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
  • 2Zentralanstalt für Meteorologie und Geodynamik (ZAMG), Vienna, A-1190, Austria
  • 3Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Oberpfaffenhofen, Germany
  • 4Swedish Meteorological and Hydrological Institute (SMHI), Norrkoping, SE-601 76, Sweden
  • 5Paris Lodron University of Salzburg (PLUS), Salzburg, A-5020, Austria
  • 6Flightkeys GmbH, Vienna, A-1060, Austria

Abstract. High quality volcanic ash forecasts are crucial to minimize the economic impact of volcanic hazards on air traffic. Decision-making is usually based on numerical dispersion modeling with only one model realization. Given the inherent uncertainty of such approach, a multi-model multi-source term ensemble has been designed and evaluated for the Eyjafjallajökull eruption in May 2010. Its use for air traffic management is discussed. Two multi-model ensembles were built: the first is based on the output of four dispersion models and their own implementation of ash ejection. All a priori model source terms were constrained by observational evidence of the volcanic ash cloud top as a function of time. The second ensemble is based on the same four dispersion models, which were run with three additional source terms: (i) a source term obtained with background modeling constrained with satellite data (a posteriori source term), (ii) its lower bound estimate, and (iii) its upper bound estimate. The a priori ensemble gives valuable information about the probability of ash dispersion during the early phase of the eruption, when observational evidence is limited. However, its evaluation with observational data reveals lower quality compared to the second ensemble. While the second ensemble ash column load and ash horizontal location compare well to satellite observations, 3D ash concentrations are negatively biased. This might be caused by the vertical distribution of ash, which is too much diluted in all model runs, probably due to defaults in the a posteriori source term and vertical transport and/or diffusion processes in all models. Relevant products for the air traffic management are horizontal maps of ash concentration quantiles (median, 75 %, 99 %) at a fine-resolved flight level grid. These maps can be used for route optimization in the areas where ash does not pose a direct and urgent threat to aviation. Cost-optimized consideration of such hazards will result in much less impact on flight cancellations, reroutings, and traffic flow congestions.

Matthieu Plu et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-96', Tatjana Bolic, 04 May 2021
  • RC2: 'Comment on nhess-2021-96', Dr. Andreas Becker, 16 May 2021
  • RC3: 'Comment on nhess-2021-96', Claire Witham, 17 May 2021
  • RC4: 'Comment on nhess-2021-96', Ole Ross, 18 May 2021

Matthieu Plu et al.

Matthieu Plu et al.

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Short summary
Past volcanic eruptions that spread out ash over large areas, like Eyjafjallajökull in 2010, forced to cancel thousands of flights and have huge economic consequences. In this article, an international team in the H2020 EU-funded EUNADICS-AV project has designed a probabilistic model approach to quantify the ash concentrations, evaluates it against measurements and discusses its potential use to mitigate the impact of future large-scale eruptions.
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