A case study on climate models intercomparison data analysis addressing several classes of multi-model experiments is being implemented in the context of the EU H2020 INDIGO-DataCloud project. Such experiments require the availability of large amount of data (multi-terabyte order) related to the output of several climate models simulations as well as the exploitation of scientific data management tools for large-scale data analytics. More specifically, the paper discusses in detail a use case on precipitation trend analysis in terms of requirements, architectural design solution, and infrastructural implementation. The experiment has been tested and validated on CMIP5 datasets, in the context of a large scale distributed testbed across EU and US involving three ESGF sites (LLNL, ORNL, and CMCC) and one central orchestrator site (PSNC).

Distributed and cloud-based multi-model analytics experiments on large volumes of climate change data in the earth system grid federation eco-system / Fiore, S.; Plociennik, M.; Doutriaux, C.; Palazzo, C.; Boutte, J.; Zok, T.; Elia, D.; Owsiak, M.; D'Anca, A.; Shaheen, Z.; Bruno, R.; Fargetta, M.; Caballer, M.; Molto, G.; Blanquer, I.; Barbera, R.; David, M.; Donvito, G.; Williams, D. N.; Anantharaj, V.; Salomoni, D.; Aloisio, G.. - (2016), pp. 2911-2918. (Intervento presentato al convegno 4th IEEE International Conference on Big Data, Big Data 2016 tenutosi a USA nel 2016) [10.1109/BigData.2016.7840941].

Distributed and cloud-based multi-model analytics experiments on large volumes of climate change data in the earth system grid federation eco-system

Fiore S.;
2016-01-01

Abstract

A case study on climate models intercomparison data analysis addressing several classes of multi-model experiments is being implemented in the context of the EU H2020 INDIGO-DataCloud project. Such experiments require the availability of large amount of data (multi-terabyte order) related to the output of several climate models simulations as well as the exploitation of scientific data management tools for large-scale data analytics. More specifically, the paper discusses in detail a use case on precipitation trend analysis in terms of requirements, architectural design solution, and infrastructural implementation. The experiment has been tested and validated on CMIP5 datasets, in the context of a large scale distributed testbed across EU and US involving three ESGF sites (LLNL, ORNL, and CMCC) and one central orchestrator site (PSNC).
2016
Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
Piscataway (New Jersey)‎
Institute of Electrical and Electronics Engineers Inc.
978-1-4673-9005-7
Fiore, S.; Plociennik, M.; Doutriaux, C.; Palazzo, C.; Boutte, J.; Zok, T.; Elia, D.; Owsiak, M.; D'Anca, A.; Shaheen, Z.; Bruno, R.; Fargetta, M.; Caballer, M.; Molto, G.; Blanquer, I.; Barbera, R.; David, M.; Donvito, G.; Williams, D. N.; Anantharaj, V.; Salomoni, D.; Aloisio, G.
Distributed and cloud-based multi-model analytics experiments on large volumes of climate change data in the earth system grid federation eco-system / Fiore, S.; Plociennik, M.; Doutriaux, C.; Palazzo, C.; Boutte, J.; Zok, T.; Elia, D.; Owsiak, M.; D'Anca, A.; Shaheen, Z.; Bruno, R.; Fargetta, M.; Caballer, M.; Molto, G.; Blanquer, I.; Barbera, R.; David, M.; Donvito, G.; Williams, D. N.; Anantharaj, V.; Salomoni, D.; Aloisio, G.. - (2016), pp. 2911-2918. (Intervento presentato al convegno 4th IEEE International Conference on Big Data, Big Data 2016 tenutosi a USA nel 2016) [10.1109/BigData.2016.7840941].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/331710
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