EGU session on Machine Learning for Ocean Science

[Rachel Furner]

Dear colleagues

 

We are pleased to invite you to submit an abstract to the session ITS1.2/OS4.10 Machine Learning for Ocean Science at EGU2024 (14-19 April 2023). 

 

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

https://meetingorganizer.copernicus.org/EGU24/abstractsubmission/50283

 

Webpage of the session:

https://meetingorganizer.copernicus.org/EGU24/session/50283

 

The deadline for abstract submission is 10 January 2024, 13:00 CET.

The deadline for applying for financial support is 1 December 2024, 13.00 CET, more information here: https://egu24.eu/guidelines/supports_and_waivers.html

 

Summary:

Machine learning (ML) methods have emerged as powerful tools to tackle various challenges in ocean science, encompassing physical oceanography, biogeochemistry, and sea ice research.

This session aims to explore the application of ML methods in ocean science, with a focus on advancing our understanding and addressing key challenges in the field. Our objective is to foster discussions, share recent advancements, and explore future directions in the field of ML methods for ocean science.

A wide range of machine learning techniques can be considered including supervised learning, unsupervised learning, interpretable techniques, and physics-informed and generative models. The applications to be addressed span both observational and modeling approaches.

 

Observational approaches include for example:

- Identifying patterns and features in oceanic fields

- Filling observational gaps of in-situ or satellite observations

- Inferring unobserved variables or unobserved scales

- Automating quality control of data

 

Modeling approaches can address (but are not restricted to):

- Designing new parameterization schemes in ocean models

- Emulating partially or completely ocean models

- Parameter tuning and model uncertainty

 

The session welcomes also submissions at the interface between modeling and observations, such as data assimilation, data-model fusion, or bias correction.

 

All the best

 

Julien Brajard (NERSC, Norway)

Aida Alvera-Azcárate (University of Liege, Belgium)

Rachel Furner (British Antarctic Survey - BAS, UK)

Redouane Lguensat (IPSL/IRD, France)