Machine Learning for ocean science
Orals
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Fri, 19 Apr, 08:30–12:30 (CEST) Room E2
Posters on site
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Attendance Thu, 18 Apr, 16:15–18:00 (CEST) | Display Thu, 18 Apr, 14:00–18:00 Hall X5
Posters virtual
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Attendance Thu, 18 Apr, 14:00–15:45 (CEST) | Display Thu, 18 Apr, 08:30–18:00 vHall X4
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.
Session assets
DATA ASSIMILATION
08:30–08:40
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EGU24-4587
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On-site presentation
08:40–08:50
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EGU24-17731
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On-site presentation
08:50–09:00
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EGU24-17199
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ECS
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Highlight
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On-site presentation
09:00–09:07
Q&A
ML FOR INSIGHTS
09:07–09:17
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EGU24-21905
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On-site presentation
09:17–09:27
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EGU24-120
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ECS
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On-site presentation
09:27–09:37
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EGU24-8942
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ECS
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On-site presentation
09:37–09:44
Q&A
PHYSICS INFORMED METHODS
09:44–09:54
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EGU24-3372
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ECS
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On-site presentation
09:54–10:04
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EGU24-18663
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ECS
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On-site presentation
10:04–10:10
Q&A
CARBON CYCLE
Coffee break
Chairpersons: Aida Alvera-Azcárate, Redouane Lguensat
10:45–10:55
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EGU24-13571
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ECS
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Virtual presentation
10:55–11:05
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EGU24-6735
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On-site presentation
11:05–11:15
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EGU24-14839
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ECS
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Virtual presentation
11:15–11:24
Q&A
MULTISOURCE DATA
11:24–11:34
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EGU24-16166
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ECS
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On-site presentation
11:44–11:54
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EGU24-3954
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On-site presentation
11:54–12:03
Q&A
EMULATORS
12:03–12:13
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EGU24-19104
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Highlight
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On-site presentation
12:13–12:23
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EGU24-4488
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On-site presentation
12:23–12:30
Q&A
X5.238
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EGU24-880
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ECS
Intercomparison of high resolution ocean reanalysis products with observations, for exploring the spatiotemporal characteristics in the Indian Ocean
(withdrawn after no-show)
X5.240
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EGU24-21267
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ECS
Seabed substrate mapping based on machine learning using MBES data
(withdrawn)
X5.243
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EGU24-21554
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ECS
X5.251
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EGU24-6927
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ECS
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Highlight
X5.254
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EGU24-15594
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Highlight
X5.258
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EGU24-347
A Probabilistic Forecast for Multi-year ENSO Using Bayesian Convolutional Neural Network
(withdrawn after no-show)
X5.259
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EGU24-18493
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ECS
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Highlight
X5.261
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EGU24-22070
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ECS
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Highlight
vX4.12
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EGU24-8790
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ECS
Harvesting Global Oceanic Lead Data Using Machine Learning: Standardization, Alignment and Spatio-temporal Puzzle Assembly with Large Language Model
(withdrawn)