Presentation
From Gordon Bell Finalist to the Osaka Expo: The Evolution of Real-Time Forecasting on Fugaku
SessionBig Data
DescriptionBuilding directly upon our work that was a finalist for the Gordon Bell Prize for Climate Modelling at SC23, this talk presents the next stage of our pioneering research in real-time weather prediction with unprecedented precision on the supercomputer Fugaku. Our new experiment for the Expo 2025 Osaka Kansai marks a world's first: the simultaneous use of two Multi-Parameter Phased Array Weather Radars for data assimilation (DA). This novel configuration provided an unprecedented data stream assimilated in real time by our Big Data Assimilation system on the supercomputer Fugaku, enabling 30-second-refresh, 30-minute-lead precipitation forecasts at a 500-meter resolution.
Building on the capabilities of this system, the talk then shifts focus to the future of prediction science, exploring our multifaceted research at RIKEN to fuse DA with AI/ML. Motivated by the need to combine the strengths of physics-based models with data-driven techniques and adapt to modern GPU-centric architectures, we will present five examples of our hybrid methodologies. These include integrating convolutional LSTMs with numerical weather prediction, developing deep neural network observation operators for satellite data, and using DA to iteratively refine AI surrogate models. The talk will conclude with a forward-looking perspective on fully Bayesian estimation, discussing how emerging techniques like conditional diffusion models could achieve the ultimate goal of DA: directly sampling atmospheric states from observations.
Building on the capabilities of this system, the talk then shifts focus to the future of prediction science, exploring our multifaceted research at RIKEN to fuse DA with AI/ML. Motivated by the need to combine the strengths of physics-based models with data-driven techniques and adapt to modern GPU-centric architectures, we will present five examples of our hybrid methodologies. These include integrating convolutional LSTMs with numerical weather prediction, developing deep neural network observation operators for satellite data, and using DA to iteratively refine AI surrogate models. The talk will conclude with a forward-looking perspective on fully Bayesian estimation, discussing how emerging techniques like conditional diffusion models could achieve the ultimate goal of DA: directly sampling atmospheric states from observations.
Event Type
Invited Talk
TimeThursday, 20 November 202510:30am - 11:15am CST
LocationAmerica's Ballroom Tu-Th
AI, Machine Learning, & Deep Learning
Big Data
Weather Prediction