AI-TT workshop
Overview
—– News —- REGISTRATION now OPEN —- News —-
Machine Learning for Ocean Prediction: Methods, Applications & Challenges
Recent developments in artificial intelligence (AI) capabilities (including neural network approaches, machine learning and deep learning related tools) have demonstrated the ability to provide accurate forecasts in weather and environmental forecasting. The OceanPredict Artificial Intelligence Task Team (AI-TT) aims to create a forum to discuss recent developments in the application of AI to ocean prediction, to share best practices, and to explore areas for future development.
This 2-day meeting will provide an overview of ongoing and planned activities in machine learning ocean prediction across the global community. Topics span the full breadth of ocean prediction applications, and include downscaling, forecast emulation, hybrid modelling, data assimilation and ensembles, and challenges in evaluating machine learning emulators compared to numerical models.
The workshop will include keynote speakers (details to follow shortly), oral presentations, posters and breakout discussions.
Workshop objectives
- Create a forum to share progress and experience in the application of AI methods to Ocean Forecasting
- Provide networking opportunities promoting future collaborations and partnerships
- Enable breakout discussions on challenges in this field, including best practices in the evaluation methodology used to understand the added value of AI based forecast (improved performance on which variables, physical consistency, etc….)
- Identify areas for future work and development
Date and time
- AI-TT workshop – Montreal
- 2-day event
- 13 & 14 April 2026
- In-person with hybrid options
Call for abstracts & key topics
We invite submissions for the upcoming workshop, “Machine Learning for Ocean Prediction: Methods, Applications & Challenges”. This workshop aims to bring together experts and practitioners exploring the transformative role of machine learning for modelling and prediction of the ocean. Interest areas span global and regional focuses, ocean, sea ice and bio-geo-chemistry, time frames from hours to seasons and beyond, and reanalysis and data-assimilation.
Abstract submission extended to Friday, 2 January 2026.
Key topics of interest include (but workshop scope is not limited to the following):
- Machine Learning Emulators: Design, development, and application of machine learning, including generative approaches, as fast, surrogate models for complex ocean processes
- Hybrid Approaches: Integration of physics-based ocean models and machine learning techniques to enhance predictive accuracy and efficiency, eg. use of ML components (parameterisations, etc) in state-of-the-art physical ocean models, and combining physics and deep learning within in a single differentiable programming framework
- Deep Learning for Data Assimilation, and inversion schemes: Innovative uses of deep learning architectures to assimilate diverse oceanographic datasets, including satellite and in-situ observations. Use of ML for ocean state estimation and forecasting.
- Evaluation Challenges: Strategies and benchmarks for assessing the performance, robustness, and reliability of deep learning-based emulators in operational settings.
- Technical challenges: Operationalization, Novel architectures, Managing and sharing large datasets, etc.
- Other relevant ML applications for Ocean prediction (e.g. downscaling applications, ensemble forecasting)
We encourage contributions in the form of oral presentations and posters. Submissions should clearly outline objectives, methodologies, and relevance to the workshop themes.
Join us to advance the science of ocean prediction through cutting-edge machine learning approaches!
Registration and abstract submission
Please note that everyone who is planning to attend the AI-TT meeting must register using the link below.
If you like to submit an abstract you have to use the abstract submission form in addition.
| REGISTRATION
OPEN (until 20 Feb 2026)
|
ABSTRACT SUBMISSION
NOW CLOSED You can upload a maximum of 2 abstract. The abstract should be provided as a .doc or .docx file, be no longer than 300 words and should ideally not include a graphic. Please view a simple template here. |
Abstracts
All submitted abstracts are available in the table below in pdf format (some exceptions).
Sorted alphabetically by author.
| No/ ID | First name | Last name | Affiliation | Abstract title |
|---|---|---|---|---|
| 1 | Kunihiro | Aoki | Meteorological Research Institute, Japan Meteorological Agency | Variational autoencoder-based clustering for geophysical fluid circulations with small sample size |
| 2 | Federica | Benassi | Euro-Mediterranean Center on Climate Change (CMCC) and University of Bologna | Predicting wave parameters on unstructured grids using a Graph Neural Network |
| 3 | Daniele | Bigoni | CMCC Foundation, Italy | Assimilation of sea surface temperature in the Mediterranean Sea using ML-based operators |
| 4 | Daria | Botvynko | IMT Atlantique | Short-term neural forecasts of ocean dynamics from sparse satellite observations |
| 5 | Annalisa | Bracco (1) | Euro-Mediterranean Center on Climate Change (CMCC) Foundation | A Physics Informed Emulator for Ocean Oxygen |
| 6 | Annalisa | Bracco (2) | Euro-Mediterranean Center on Climate Change (CMCC) Foundation | Uncovering marine connectivity through sea surface temperature and machine learning |
| 7 | Emanuela | Clementi | CMCC Foundation-Euro-Mediterranean Center on Climate Change, Italy | Accurate Mediterranean Sea forecasting via graph-based deep learning |
| 8 | Jacopo | Dall’Aglio | University of Bologna | Machine Learning Forecast Correction for Geophysical Fields |
| 9 | Davide | Donno | Department of Engineering for Innovation, University of Salento AND CMCC Foundation – Euro-mediterranean Center on Climate Change | Atlas Ocean – Fourier Neural Operators for S2S to Decadal Global Ocean Forecasting |
| 10 | Michael | Dunphy | Institute of Ocean Sciences, Fisheries and Oceans Canada, Sidney, BC, Canada | Development of machine-learning emulators for harbour-scale ocean prediction |
| 11 | Sandupal | Dutta | Johns Hopkins University, Baltimore, MD, US 21218 | Using Machine Learning to Improve Plankton Dynamics in Earth System Models |
| 12 | Anass | El Aouni (1) | Mercator Ocean International | OceanBench: A Benchmark for Data-Driven Global Ocean Forecasting systems |
| 13 | Anass | El Aouni (2) | Mercator Ocean International | Toward Scalable and Probabilistic Neural Ocean Forecasting |
| 14 | Italo | Epicoco | CMCC Foundation | Data-Driven approach for Data Assimilation in the Ocean |
| 15 | Ronan | Fablet | IMT Atlantique | Training end-to-end neural mapping schemes from simulation data for the reconstruction of global-scale sea surface fields |
| 16 | Rachel | Furner | ECMWF | Data-driven ocean modelling at ECMWF |
| 17 | Fang | Hou | National Marine Environmental Forecasting Center | Application and Verification of the Global Wave Intelligent Forecast Model |
| 18 | Xudong | Huang | Department of Oceanography, Dalhousie University | Detection and Discrimination of Marine Oil Spills and Look-alike Phenomena in Synthetic Aperture Radar Imagery |
| 19 | Nam-Hoon | Kim | Korea Institute of Ocean Science & Technology | Preliminary study for development of an AI-based ocean forecasting system |
| 20 | Geon Min | Lee | Pukyong National University, Republic of Korea | A Deep-Learning Observation Operator for Subsurface Thermohaline Reconstruction from Satellite Surface Observations |
| 21 | Xiaoyan | Li | Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) | AI-driven Nanming Model for Three-dimension Ocean Variable Forecasting |
| 22 | Gabriela | Martinez Balbontin | Mercator Ocean International | BG4Sea: Machine-Learned Multivariate Seasonal Biogeochemical Forecasting |
| 23 | Mateusz | Matuszak | Norwegian Meteorological Institute | A data driven limited area storm surge model |
| 24 | Andrew | Moore | University of California Santa Cruz | Linear Stochastic Emulators of the Ocean Circulation – A Lesson, perhaps, for Machine Learning |
| 25 | Sung-Hwan | Park | Korea Institute of Ocean Science and Technology | A Deep Learning Emulator for Storm Surge Forcing: GAN-Based Enhancement of Parametric Wind Models |
| 26 | Jae-Hun | Park | Department of Ocean Sciences, Inha University | Prediction of sea surface currents around the Korean peninsula using artificial neural networks |
| 27 | Francois | Roy | ECCC | Application of Deep learning (DL) in the Gulf of St. Lawrence and Estuary |
| 28 | Leonardo | Saccotelli | CMCC Foundation – Euro-Mediterranean Center on Climate Change | From Coarse Models to Coastal Detail: A Deep Learning Approach to AI based Statistical Downscaling in the Adriatic Sea |
| 29 | Simone | Spada | National Institute of Oceanography and Applied Geophysics – OGS | Bridging Scales in Mediterranean Biogeochemical Prediction: A High-Order Ensemble Assimilation Coupled with AI-Driven Downscaling |
| 30 | Joanna | Staneva | Helmholtz Zentrum HEREON | Integrated AI_Physics Approaches for Coastal Prediction Across the Open-to-Coastal Ocean Continuum |
| 31 | Christopher | Subich | Environment & Climate Change Canada | Fix the double penalty in data-driven forecasting by modifying the loss function |
| 32 | Teresa | Tonelli (1) | OGS (National Institute of Oceanography and Applied Geophysics), University of Trieste | Two-Phase CNN for Model Data Fusion: Predicting 3D Chlorophyll-a in the Mediterranean Sea |
| 33 | Teresa | Tonelli (2) | OGS (National Institute of Oceanography and Applied Geophysics), University of Trieste | Filling the Ocean’s Gaps: a Self-Supervised Neural Network for Argo Profiles Data Augmentation |
| 34 | Liying | WAN | National Marine Environmental Forecasting Center | AI perform on high resolution three-dimensional ocean forecasting: remote sensing data driven becomes a new possibility |
| 35 | Biao | Zhang | Dalhousie University | Hybrid Physics-Guided Data-Driven Estimation of Wave-Induced Doppler shifts for SAR Ocean Surface Current Retrival |
| 36 | Xueming | Zhu | Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) | Intelligent forecasting of marine environmental elements in the South China Sea |
Important dates
| Date | Description |
|---|---|
| September 2025 | Save the date announcement |
| 28 October 2025 | Opening of Call for abstracts |
| 2 January 2026 | Call for abstracts closes – now closed |
| Mid-Jan 2026 | Registration opens |
| End-Jan 2026 | Abstract confirmation & early schedule announcement |
| Mid-February 2026 | Registration deadline |
| 13 & 14 April 2026 | Workshop |
Organising Committee
- AI-TT co-chairs
- Santha Akella, NOAA
- Rachel Furner, ECMWF
- AI-TT members
- Kristian Mogensen, ECMWF
- Simon van Gennip, MOi
- OPST co-chairs
- Marie Drevillon, MOi
- Greg Smith, ECCC
- Local host
- Greg Smith, ECCC
- Frederic Dupont, ECCC
- Fraser Davidson, ECCC
- OP programme office
- Stephanie Cuven, MOi
- Kirsten Wilmer-Becker, Met Office
Status: 28 Oct 2025