Topic: Machine Learning in Data Assimilation

Thursday, February 11, 2025 from 15-17 pm UTC

Machine Learning in Data Assimilation

Organisers and Conveners: Hristo Chipilski (Florida State University, US), Takeshi Enomoto (Kyoto University, Japan), Nabir Mamnun (Imperial College London, UK), Tobias Necker (ECMWF, Germany)

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.

Presentations:

• t.b.a

Time Zones:
15 – 17 UTC
Europe:           03 – 05 pm GMT (London)     | 04 – 06 pm CET (Berlin)
Asia/Australia: 11 – 01 am CST (Shanghai)  | 00 – 02 am JST (Tokyo)      | 02 – 04 am AEDT (Sydney)
Americas:        07 – 09 am PST (San Fran.) | 08 – 10 am MST (Denver)   | 10 – 12 am EST (New York)

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