*Online by Zoom (see the description for a link) *Only AIP researchers can join on-site at the open space of Nihonbashi Office.
Join Zoom Meeting
https://riken-jp.zoom.us/j/99285949815?pwd=dm5Gc2JPZzNtUHpwT2txdTF6dGJKUT09
14:30-15:30 So Takao.
Title: Improving data-assimilation for weather forecasting: A graph-based Bayesian perspective
Abstract:
Data assimilation plays a crucial role in weather forecasting by fusing physical models with observations to enhance forecast accuracy. However, achieving exact inference in this context is computationally infeasible due to the exceedingly large state dimension and observation size, as well as the nonlinearities that are present in the physical models. To address these challenges, we present a novel approach that casts data assimilation as Bayesian inference on a graph derived from the physical model. This in turn allows us to employ local message passing techniques for approximate inference, enabling us to utilise parallel computation across GPU threads, accelerating the compute time and reducing memory requirements. In this talk, we will showcase the initial results of our proposed method and discuss how our approach opens up new possibilities for advancing data assimilation techniques in weather forecasting.
Bio:
Dr. So Takao is formerly a Senior Research Fellow at the Centre for Artificial Intelligence in University College London specialising in statistical machine learning. His main interests are in developing cutting-edge machine learning models with useful inductive biases, such as coming from physics or geometry, and adopting this to address complex challenges posed by weather/climate science. Prior to this, he obtained his PhD in Mathematics from Imperial College London under the mentorship of Professor Darryl Holm, where he focused on the development and analysis of stochastic fluid models using differential geometric techniques.
