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An Open-Source Interface to the Canadian Surface Prediction Archive

Published: 01 August 2020 Publication History

Abstract

Data-intensive research and decision-making continue to gain adoption across diverse organizations. As researchers and practitioners increasingly rely on analyzing large data products to both answer scientific questions and for operational needs, data acquisition and pre-processing become critical tasks. For environmental science, the Canadian Surface Prediction Archive (CaSPAr) facilitates easy access to custom subsets of numerical weather predictions. We demonstrate a new open-source interface for CaSPAr that provides easy-to-use map-based querying capabilities and automates data ingestion into the CaSPAr batch processing server.

References

[1]
T. Hey, S. Tansley, and K. M. Tolle (Eds.). 2009. The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research.
[2]
J. Mai, K. C. Kornelsen, B. A. Tolson, V. Fortin, N. Gasset, D. Bouhemhem, D. Schäfer, M. Leahy, F. Anctil, and P. Coulibaly. 2019. The Canadian Surface Prediction Archive (CaSPAr): A Platform to Enhance Environmental Modeling in Canada and Globally. Bulletin of the American Meteorological Society, Vol. 101, 3 (2019), E341--E356.

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cover image ACM Conferences
JCDL '20: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020
August 2020
611 pages
ISBN:9781450375856
DOI:10.1145/3383583
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 2020

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Author Tags

  1. data dissemination
  2. environmental data
  3. numerical weather predictions
  4. subsetting

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