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“Big Data Assimilation” Toward Post-Petascale Severe Weather Prediction: An Overview and Progress

机译：后大尺度天气预报的“大数据同化”：概述和进展

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摘要

Following the invention of the telegraph, electronic computer, and remote sensing, “big data” is bringing another revolution to weather prediction. As sensor and computer technologies advance, orders of magnitude bigger data are produced by new sensors and high-precision computer simulation or “big simulation.” Data assimilation (DA) is a key to numerical weather prediction (NWP) by integrating the real-world sensor data into simulation. However, the current DA and NWP systems are not designed to handle the “big data” from next-generation sensors and big simulation. Therefore, we propose “big data assimilation” (BDA) innovation to fully utilize the big data. Since October 2013, the Japan's BDA project has been exploring revolutionary NWP at 100-m mesh refreshed every 30 s, orders of magnitude finer and faster than the current typical NWP systems, by taking advantage of the fortunate combination of next-generation technologies: the 10-petaflops K computer, phased array weather radar, and geostationary satellite Himawari-8. So far, a BDA prototype system was developed and tested with real-world retrospective local rainstorm cases. This paper summarizes the activities and progress of the BDA project, and concludes with perspectives toward the post-petascale supercomputing era.
机译：继电报，电子计算机和遥感技术的发明之后，“大数据”正在为天气预报带来另一次革命。随着传感器和计算机技术的发展，新传感器和高精度计算机仿真或“大仿真”将产生更大数量级的数据。通过将真实世界的传感器数据集成到模拟中，数据同化（DA）是数字天气预报（NWP）的关键。但是，当前的DA和NWP系统并非旨在处理来自下一代传感器和大型仿真的“大数据”。因此，我们提出“大数据同化”（BDA）创新以充分利用大数据。自2013年10月以来，日本的BDA项目一直在探索革命性的NWP，每30 s刷新一次100 m的网格，比当前的典型NWP系统精细和快几个数量级，这要利用下一代技术的幸运组合： 10 petaflops K计算机，相控阵气象雷达和对地静止卫星Himawari-8。到目前为止，已经开发了BDA原型系统，并在实际的回顾性局部暴雨案例中进行了测试。本文总结了BDA项目的活动和进展，并以对后Petascale超级计算时代的观点作了总结。

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  • 来源
    《Proceedings of the IEEE》 |2016年第11期|2155-2179|共25页
  • 作者

    Takemasa Miyoshi; Guo-Yuan Lien; Shinsuke Satoh; Tomoo Ushio; Kotaro Bessho; Hirofumi Tomita; Seiya Nishizawa; Ryuji Yoshida; Sachiho A. Adachi; Jianwei Liao; Balazs Gerofi; Yutaka Ishikawa; Masaru Kunii; Juan Ruiz; Yasumitsu Maejima; Shigenori Otsuka; Michiko Otsuka; Kozo Okamoto; Hiromu Seko;

  • 作者单位

    Department of Atmospheric and Oceanic Science, Application Laboratory, RIKEN Advanced Institute for Computational Science, University of Maryland, Japan Agency for Marine-Earth Science and Technology, Chuo-ku, Kobe, College Park, Yokohama, MD, JapanUSAJapan;

    RIKEN Advanced Institute for Computational Science, Chuo-ku, Kobe, Japan;

    National Institute of Information and Communications Technology, Koganei, Japan;

    Osaka University, Suita, Japan;

    Forecast Department, Meteorological Satellite Center, Japan Meteorological Agency, Kiyose, Tokyo, JapanJapan;

    RIKEN Advanced Institute for Computational Science, Chuo-ku, Kobe, Japan;

    RIKEN Advanced Institute for Computational Science, Chuo-ku, Kobe, Japan;

    RIKEN Advanced Institute for Computational Science, Chuo-ku, Kobe, Japan;

    RIKEN Advanced Institute for Computational Science, Chuo-ku, Kobe, Japan;

    RIKEN Advanced Institute for Computational Science, Southwest University of China, Chuo-ku, Kobe, Chongqing, JapanChina;

    RIKEN Advanced Institute for Computational Science, Chuo-ku, Kobe, Japan;

    RIKEN Advanced Institute for Computational Science, Chuo-ku, Kobe, Japan;

    RIKEN Advanced Institute for Computational Science, Meteorological Research Institute, Chuo-ku, Kobe, Tsukuba, Japan;

    CIMA, RIKEN Advanced Institute for Computational Science, CONICET-University of Buenos Aires, Chuo-ku, Kobe, Buenos Aires, JapanArgentina;

    RIKEN Advanced Institute for Computational Science, Chuo-ku, Kobe, Japan;

    RIKEN Advanced Institute for Computational Science, Chuo-ku, Kobe, Japan;

    RIKEN Advanced Institute for Computational Science, Meteorological Research Institute, Chuo-ku, Kobe, Tsukuba, Japan;

    RIKEN Advanced Institute for Computational Science, Meteorological Research Institute, Chuo-ku, Kobe, Tsukuba, Japan;

    Meteorological Research Institute, Tsukuba, Japan;

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  • 正文语种 eng
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  • 关键词

    Big data; Weather forecasting; Atmospheric modeling; Computational modeling; Atmospheric measurements; Data models; Computer applications; Kalman filtering; Optimal control; Remote sensing; Supercomputers; Weather forecasting;

    机译：大数据;天气预报;大气建模;计算模型;大气测量;数据模型;计算机应用;卡尔曼滤波;最优控制;遥感;超级计算机;天气预报;

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