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“Big Data Assimilation” Revolutionizing Severe Weather Prediction

Takemasa MiyoshiRIKEN Advanced Institute for Computational Science, Kobe, Japan, Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, and Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

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Masaru KuniiRIKEN Advanced Institute for Computational Science, Kobe, Japan, and Meteorological Research Institute, Tsukuba, Japan

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Juan RuizRIKEN Advanced Institute for Computational Science, Kobe, Japan, and CIMA, CONICET-University of Buenos Aires, Buenos Aires, Argentina

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Guo-Yuan LienRIKEN Advanced Institute for Computational Science, Kobe, Japan

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Shinsuke SatohNational Institute of Information and Communications Technology, Koganei, Japan

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Tomoo UshioOsaka University, Suita, Japan

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Kotaro BesshoMeteorological Satellite Center, Kiyose, Japan

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Hiromu SekoMeteorological Research Institute, Tsukuba, Japan

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Hirofumi TomitaRIKEN Advanced Institute for Computational Science, Kobe, Japan

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Yutaka IshikawaRIKEN Advanced Institute for Computational Science, Kobe, Japan

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Abstract

Sudden local severe weather is a threat, and we explore what the highest-end supercomputing and sensing technologies can do to address this challenge. Here we show that using the Japanese flagship “K” supercomputer, we can synergistically integrate “big simulations” of 100 parallel simulations of a convective weather system at 100-m grid spacing and “big data” from the next-generation phased array weather radar that produces a high-resolution 3-dimensional rain distribution every 30 s—two orders of magnitude more data than the currently used parabolic-antenna radar. This “big data assimilation” system refreshes 30-min forecasts every 30 s, 120 times more rapidly than the typical hourly updated systems operated at the world’s weather prediction centers. A real high-impact weather case study shows encouraging results of the 30-s-update big data assimilation system.

CORRESPONDING AUTHOR: Takemasa Miyoshi, RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-minami-machi, Chuo-ku, Kobe 650-0047, Japan, E-mail: takemasa.miyoshi@riken.jp

A supplement to this article is available online (10.1175/BAMS-D-15-00144.2)

Abstract

Sudden local severe weather is a threat, and we explore what the highest-end supercomputing and sensing technologies can do to address this challenge. Here we show that using the Japanese flagship “K” supercomputer, we can synergistically integrate “big simulations” of 100 parallel simulations of a convective weather system at 100-m grid spacing and “big data” from the next-generation phased array weather radar that produces a high-resolution 3-dimensional rain distribution every 30 s—two orders of magnitude more data than the currently used parabolic-antenna radar. This “big data assimilation” system refreshes 30-min forecasts every 30 s, 120 times more rapidly than the typical hourly updated systems operated at the world’s weather prediction centers. A real high-impact weather case study shows encouraging results of the 30-s-update big data assimilation system.

CORRESPONDING AUTHOR: Takemasa Miyoshi, RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-minami-machi, Chuo-ku, Kobe 650-0047, Japan, E-mail: takemasa.miyoshi@riken.jp

A supplement to this article is available online (10.1175/BAMS-D-15-00144.2)

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