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

S. Fiore, M. Plociennik, C. Doutriaux, C. Palazzo, J. Boutte, T. Zok, D. Elia, M. Owsiak, A. D'Anca, Z. Shaheen, R. Bruno, M Fargetta, M. Caballer, G. Molto, I. Blanquer, R. Barbera, M. David, G. Donvito, D. N. Williams, V. Anantharaj, D. Salomoni, and G. Aloisio. Distributed and cloud-based multi-model analytics experiments on large volumes of climate change data in the earth system grid federation eco-system. In 2016 IEEE International Conference on Big Data (Big Data), pp. 2911–2918, IEEE, 12 2016.

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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)

BibTeX Entry

@inproceedings{Fiore2016dcb,
   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)},
   author = {S. Fiore and M. Plociennik and C. Doutriaux and C. Palazzo and J. Boutte and T. Zok and D. Elia and M. Owsiak and A. D'Anca and Z. Shaheen and R. Bruno and M Fargetta and M. Caballer and G. Molto and I. Blanquer and R. Barbera and M. David and G. Donvito and D. N. Williams and V. Anantharaj and D. Salomoni and G. Aloisio},
   doi = {10.1109/BigData.2016.7840941},
   isbn = {978-1-4673-9005-7},
   booktitle = {2016 IEEE International Conference on Big Data (Big Data)},
   month = {12},
   pages = {2911-2918},
   publisher = {IEEE},
   title = {Distributed and cloud-based multi-model analytics experiments on large volumes of climate change data in the earth system grid federation eco-system},
   url = {http://ieeexplore.ieee.org/document/7840941/},
   year = {2016}
}