INVESTIGADORES
RUIZ Juan Jose
artículos
Título:
?Big Data Assimilation? Revolutionizing Severe Weather Prediction
Autor/es:
TAKEMASA MIYOSHI; MASARU KUNII; JUAN RUIZ; GUO-YUAN LIEN; SHINSUKE SATOH; TOMOO USHIO; HIROMU SEKO; HIROFUMI TOMITA; YUTAKA ISHIKAWA
Revista:
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
Editorial:
AMER METEOROLOGICAL SOC
Referencias:
Lugar: Boston; Año: 2016
ISSN:
0003-0007
Resumen:
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 seconds, two orders of magnitude more data than the currently-used parabolic-antenna radar. This ?Big Data Assimilation? system refreshes 30-minute forecasts every 30 seconds, 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-second-update Big Data Assimilation system.
