EGU2023 (23-28 Apr. 2023) session Machine learning for Earth System modeling

[Julien Brajard]

Dear colleagues

We are pleased to invite you to submit an abstract to the session ITS1.13/AS5.2 Machine learning for Earth System modeling at EGU2023 (23-28 April 2023). 

The conference will be held in a hybrid format, so participation is open both online or in person. Everybody is welcome to submit an abstract: 

https://egu23.eu/about/meeting_format.html

Webpage of the session:

https://meetingorganizer.copernicus.org/EGU23/session/46861

The deadline for abstract submission is 10 January 2023, 13:00 CET.

Summary:

Unsupervised, supervised, semi-supervised as well as reinforcement learning are now increasingly used to address Earth system-related challenges for the atmosphere, the ocean, the land surface, or the sea ice.

Machine learning could help extract information from numerous Earth System data, such as in-situ and satellite observations, as well as improve model prediction through novel parameterizations or speed-ups. This session invites submissions spanning modeling and observational approaches towards providing an overview of state-of-the-art applications of these novel methods for predicting and monitoring the Earth System from short to decadal time scales. This includes (but is not restricted to):

- The use of machine learning to reduce or estimate model uncertainty

- Generate significant speedups

- Design new parameterization schemes

- Emulate numerical models

- Fundamental process understanding

Please consider submitting abstracts focused on ML applied to observations and modeling of the climate and its constituent processes to the companion "ML for Climate Science" session.

All the best

Julien Brajard (NERSC, Norway)
Alejandro Coca-Castro (The Alan Turing Institute, UK)

Redouane Lguensat (IPSL/IRD, France)

Francine Schevenhoven (CU Boulder, USA and UiB, Norway)

Maike Sonnewald (Princeton University/NOAA, USA)