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LEXIS Weather and Climate Large-Scale Pilot

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Complex, Intelligent and Software Intensive Systems (CISIS 2020)

Abstract

The LEXIS Weather and Climate Large-scale Pilot will deliver a system for prediction of water-food-energy nexus phenomena and their associated socio-economic impacts. The system will be based on multiple model layers chained together, namely global weather and climate models, high-resolution regional weather models, domain-specific application models (such as hydrological, forest fire risk forecasts), impact models providing information for key decision and policy makers (such as air quality, agriculture crop production, and extreme rainfall detection for flood mapping). This paper will report about the first results of this pilot in terms of serving model output data and products with Cloud and High Performance Data Analytics (HPDA) environments, on top a Weather Climate Data APIs (ECMWF), as well as the porting of models on the LEXIS Infrastructure via different virtualization strategies (virtual machine and containers).

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Notes

  1. 1.

    https://irods.org/.

  2. 2.

    https://eudat.eu/.

  3. 3.

    https://openid.net/.

  4. 4.

    https://www.keycloak.org/.

  5. 5.

    Yorc: https://github.com/ystia/yorc.

  6. 6.

    HEAppE Middleware: http://heappe.eu.

  7. 7.

    https://github.com/cima-lexis/wps.docker.

  8. 8.

    https://github.com/cima-lexis/fp-docker.

  9. 9.

    https://github.com/cima-lexis/risico-docker.

  10. 10.

    https://www.cimafoundation.org/foundations/research-development/wrf.html.

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Acknowledgements

This work was supported by the LEXIS project funded by the EU’s Horizon 2020 research and innovation programme (2014–2020) under grant agreement no. 825532.

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Correspondence to Antonio Parodi .

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Parodi, A. et al. (2021). LEXIS Weather and Climate Large-Scale Pilot. In: Barolli, L., Poniszewska-Maranda, A., Enokido, T. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2020. Advances in Intelligent Systems and Computing, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-50454-0_25

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