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Proceedings of SAI Intelligent Systems Conference

IntelliSys 2018: Intelligent Systems and Applications pp 1139–1149Cite as

Method for Audible Representation of Meteorological Data Derived from Remote Sensing Satellites

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 869))

Abstract

Method for audible representation of meteorological data derived from remote sensing satellites is proposed. Audible representation of multi-dimensional meteorological data layered wind direction and speed data derived from Earth observation satellite data is developed. Experimental results show rapidly changing meteorological data can be listened with the different musical scale (melody), harmony, rhythm and music instrument types. More importantly, weather analysts may much easily be aware of the inversely proportional relation between altitude and air temperature by hearing the multi-dimensional meteorological data.

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Notes

  1. 1.

    https://ja.wikipedia.org/wiki/TIROS-N/NOAA.

  2. 2.

    http://www.forest.impress.co.jp/lib/pic/music/sequencer/image/sakura_r.html.

    http://www.vector.co.jp/vpack/browse/pickup/pw4/pw004690.html.

  3. 3.

    https://ja.wikipedia.org/wiki/MIDI.

  4. 4.

    https://ja.wikipedia.org/wiki/MTSAT.

  5. 5.

    https://ja.wikipedia.org/wiki/TIROS-N/NOAA.

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Acknowledgment

The author would like to thank Mr. Tsuyoshi Morita of Saga University for his effort to conduct the experiments.

Author information

Authors and Affiliations

  1. Graduate School of Science and Engineering, Saga University, Saga City, Japan

    Kohei Arai

Authors
  1. Kohei Arai
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Correspondence to Kohei Arai .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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Arai, K. (2019). Method for Audible Representation of Meteorological Data Derived from Remote Sensing Satellites. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_83

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