Fusing Big Data and Big Computation in Numerical Weather Prediction

Abstract

At RIKEN, we have been exploring a fusion of big data and big computation, and now with AI techniques and machine learning (ML). The new Japan’s flagship supercomputer “Fugaku” is designed to be efficient for both double-precision big simulations and reduced-precision ML applications, aiming to play a pivotal role in creating super-smart “Society 5.0.” Our group in RIKEN has been pushing the limits of numerical weather prediction (NWP) through two orders of magnitude bigger computations using the previous Japan’s flagship “K computer”. The efforts include 100-m mesh, 30-second update “Big Data Assimilation” (BDA) fully exploiting the big data from a novel Phased Array Weather Radar. We have achieved the first-ever real-time BDA application with 500-m mesh NWP in the summer of 2020 using a supercomputer Oakforest-PACS of the University of Tokyo and Tsukuba University. In 2021, we used the new Fugaku and performed real-time 30-second update NWP during the periods of Tokyo Olympic and Paralympic games. With Fugaku, we have been exploring ideas for fusing BDA and AI. The data produced by NWP models become bigger and moving around the data to other computers for ML may not be feasible. Having a next-generation computer like Fugaku, good for both big NWP computation and ML, may bring a breakthrough toward creating a new methodology of fusing data-driven (inductive) and process-driven (deductive) approaches in meteorology. This presentation will introduce the most recent results from data assimilation and NWP experiments, followed by perspectives toward a general theory of prediction and control beyond meteorology.