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Identifying Snowfall Clouds at Syowa Station, Antarctica via a Convolutional Neural Network

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Advances in Artificial Intelligence (JSAI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1357))

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Abstract

This study evaluated the impact of Atmospheric River (AR) clouds on snowfall amounts based on limited observation data to estimate the surface mass balance (SMB) of Antarctica. To accomplish this, we attempted to identify the snowfall cloud at Syowa Station, Antarctica. We constructed a new convolutional neural network (CNN) architecture with multinomial and binary classifications for National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images over five years. The CNN was based on VGG16, and concatenate layers were added as the inception module. We replaced all the convolution layers with global average pooling to reduce the number of parameters. Based on the positive CNN sample result, the multinomial classification emphasized the entire cloud structure, while the binary classification focused on cloud continuity. The results indicated accuracies of 71.00% and 65.37% for binary and multinomial classifications, respectively.

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Acknowledgements

This work was supported by ROIS-DS-JOINT (009RP2019) and JSPS KAKENHI Grant Number 16K21585, 20K11718.

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Authors and Affiliations

  1. Hosei University, 3-7-2, Kajinocho, Koganei-shi, Tokyo, Japan

    Kazue Suzuki

  2. Meiji University, 4-21-1, Nakano, Nakano-ku, Tokyo, Japan

    Masaki Shimomura & Kazuyuki Nakamura

  3. National Institute of Polar Research, 10-3, Midori-cho, Tachikawa-shi, Tokyo, Japan

    Naohiko Hirasawa, Hironori Yabuki & Takashi Yamanouchi

  4. Kyushu Institute of Technology, 680-4, Kawazu, Iizuka-shi, Fukuoka, Japan

    Terumasa Tokunaga

  5. Graduate University for Advanced Studies (SOKENDAI), Shonan Village, Hayama, Kanagawa, 240-0193, Japan

    Naohiko Hirasawa & Takashi Yamanouchi

Authors
  1. Kazue Suzuki
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  2. Masaki Shimomura
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  3. Kazuyuki Nakamura
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  4. Naohiko Hirasawa
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  5. Hironori Yabuki
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  6. Takashi Yamanouchi
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  7. Terumasa Tokunaga
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Corresponding author

Correspondence to Kazue Suzuki .

Editor information

Editors and Affiliations

  1. Kansai University, Suita, Osaka, Japan

    Katsutoshi Yada

  2. Department of Applied Computer Science, Tokyo Polytechnic University, Atsugi, Kanagawa, Japan

    Daisuke Katagami

  3. Graduate School of System Design, Tokyo Metropolitan University, Hino, Tokyo, Japan

    Yasufumi Takama

  4. Department of Social Informatics, Kyoto University, Kyoto, Japan

    Takayuki Ito

  5. Division of Behavioral Science, Faculty of Letters, Chiba University, Chiba, Chiba, Japan

    Akinori Abe

  6. Department of Computer Science, Graduate School of System Design, Tokyo Metropolitan University, Hino, Tokyo, Japan

    Eri Sato-Shimokawara

  7. Mathematics and Informatics Center, The University of Tokyo, Tokyo, Japan

    Junichiro Mori

  8. Graduate School of Economics, Osaka University, Toyonaka, Osaka, Japan

    Naohiro Matsumura

  9. Department of Intelligence Science and Technology, Kyoto University, Kyoto, Japan

    Hisashi Kashima

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Suzuki, K. et al. (2021). Identifying Snowfall Clouds at Syowa Station, Antarctica via a Convolutional Neural Network. In: Yada, K., et al. Advances in Artificial Intelligence. JSAI 2020. Advances in Intelligent Systems and Computing, vol 1357. Springer, Cham. https://doi.org/10.1007/978-3-030-73113-7_7

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