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'Big Data Assimilation' Revolutionizing Severe Weather Prediction

Authors :
Shinsuke Satoh
Hiromu Seko
Juan Ruiz
Kotaro Bessho
Tomoo Ushio
Guo-Yuan Lien
Masaru Kunii
Yutaka Ishikawa
Hirofumi Tomita
Takemasa Miyoshi
Source :
Bulletin of the American Meteorological Society. 97:1347-1354
Publication Year :
2016
Publisher :
American Meteorological Society, 2016.

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.

Details

ISSN :
15200477 and 00030007
Volume :
97
Database :
OpenAIRE
Journal :
Bulletin of the American Meteorological Society
Accession number :
edsair.doi...........68e6d8ca0d48f31c3ca51e9fa8398bb8