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Hindcasting with Cluster-Based Analogues

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Advanced Research in Technologies, Information, Innovation and Sustainability (ARTIIS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1485))

Abstract

The reconstruction of meteorological observations or deterministic predictions for a certain variable and station may be performed with data from other variables at that station, or from other nearby stations. This is a hindcasting problem, known from some time to be solvable using the Analogues Ensemble (AnEn) method. However, depending on the dimension and granularity of the datasets used for the reconstruction, this method may be computationally very demanding, even if parallelization is used. In this paper, the AnEn method is combined with K-means clustering, allowing for a considerable acceleration of the reconstruction task, while keeping the accuracy of the results.

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Author information

Authors and Affiliations

  1. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal

    Carlos Balsa & José Rufino

  2. Vestas Wind Systems A/S, Design Centre Porto, Porto, Portugal

    Carlos V. Rodrigues

  3. Universidade Tecnológica Federal do Paraná, Campus de Ponta Grossa, Ponta Grossa, 84017-220, Brazil

    Leonardo Araújo

Authors
  1. Carlos Balsa
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  2. Carlos V. Rodrigues
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  3. Leonardo Araújo
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  4. José Rufino
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Corresponding author

Correspondence to Carlos Balsa .

Editor information

Editors and Affiliations

  1. Universidad Estatal Península de Santa Elena, La Libertad, Ecuador

    Teresa Guarda

  2. Universidade do Minho, Guimarães, Portugal

    Filipe Portela

  3. Universidade do Minho, Guimarães, Portugal

    Manuel Filipe Santos

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Balsa, C., Rodrigues, C.V., Araújo, L., Rufino, J. (2021). Hindcasting with Cluster-Based Analogues. In: Guarda, T., Portela, F., Santos, M.F. (eds) Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2021. Communications in Computer and Information Science, vol 1485. Springer, Cham. https://doi.org/10.1007/978-3-030-90241-4_27

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  • DOI: https://doi.org/10.1007/978-3-030-90241-4_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90240-7

  • Online ISBN: 978-3-030-90241-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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