Topic: Machine Learning

Thursday, 23 April 2026 from 7-9h UTC (*NOTE - NEW DATE*)

Organizers and Conveners: Michael Goodliff (RIKEN) and James Taylor (RIKEN,)

Advances in machine learning are rapidly transforming the way we model, predict, and understand complex geophysical systems. At the same time, data assimilation remains a cornerstone of numerical prediction, providing a rigorous framework for combining observations with dynamical models. This event will explore the growing synergy between machine learning and data assimilation, highlighting new methods that enhance forecast skill, improve uncertainty quantification, and enable efficient use of increasingly diverse observational data. Through invited talks and discussions, we aim to foster cross-disciplinary exchange and identify emerging directions for next-generation prediction systems.

Submit you abstract now! Deadline is the 15th of February. 

Presentations:

• t.b.a

Time Zones:
07 – 09 UTC
Europe:            08 – 10 am BST (London)      | 09 – 11 am CEST (Berlin)
Asia/Australia: 03 – 05 pm CST (Shanghai)   | 04 – 06 pm JST (Tokyo)      | 05 – 07 pm AEDT (Sydney)
Americas:        00 – 02 am PDT (San Fran.)  | 01 – 03 am MDT (Denver)   | 03 – 05 am EDT (New York)

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