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    <titleInfo>
        <title>An Open-Source Interface to the Canadian Surface Prediction Archive</title>
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        <namePart type="given">Martin</namePart>
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    <name type="personal">
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    <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.</abstract>
    <identifier type="citekey">Gauch-2020-An</identifier>
    <identifier type="doi">10.1145/3383583.3398626</identifier>
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%0 Journal Article
%T An Open-Source Interface to the Canadian Surface Prediction Archive
%A Gauch, Martin
%A Bai, James
%A Mai, Juliane
%A Lin, Jimmy
%J Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020
%D 2020
%I ACM
%F Gauch-2020-An
%X 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.
%R 10.1145/3383583.3398626
%U https://gwf-uwaterloo.github.io/gwf-publications/G20-77001
%U https://doi.org/10.1145/3383583.3398626

Markdown (Informal)

[An Open-Source Interface to the Canadian Surface Prediction Archive](https://gwf-uwaterloo.github.io/gwf-publications/G20-77001) (Gauch et al., GWF 2020)

• An Open-Source Interface to the Canadian Surface Prediction Archive (Gauch et al., GWF 2020)

ACL

• Martin Gauch, James Bai, Juliane Mai, and Jimmy Lin. 2020. An Open-Source Interface to the Canadian Surface Prediction Archive. Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020.