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, Giovanni

doi:10.1109/BigData.2016.7840941

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).

Titolo:	Distributed and cloud-based multi-model analytics experiments on large volumes of climate change data in the earth system grid federation eco-system
Autori:	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, Giovanni mostra contributor esterni nascondi contributor esterni
Data di pubblicazione:	2016
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).
Handle:	http://hdl.handle.net/11587/411829
ISBN:	978-1-4673-9005-7
Appare nelle tipologie:	Relazione di atto di convegno in volume

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