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Following the invention of the telegraph, electronic computer, and remote sensing, &#x201C;big data&#x201D; 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 &#x201C;big simulation.&#x201D; 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 &#x201C;big data&#x201D; from next-generation sensors and big simulation. Therefore, we propose &#x201C;big data assimilation&#x201D; (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.

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