DO006
Cutting-edge Artificial Intelligence for Oceanographic Monitoring, Modeling, Forecasting, and Marine Knowledge Generation

Session ID#: 259575

Session Description:
The growing availability of multi-scale ocean data - spanning satellite observations, in situ measurements, audio-visual content, and numerical model output offers unprecedented opportunities for applying artificial intelligence (AI) to improve understanding and prediction of oceanic processes and systems.

After a steep and ongoing rise, machine learning (ML) and AI in marine sciences have reached a state of maturity, with broad adoption and acceptance across the spectrum in the oceanographic community.

We invite submissions on AI applications across all ocean sciences, including, inversion, reconstruction and forecasting of physical, biological, biogeochemical, or socioeconomic variables; oceanographic surveillance and impact modeling; detection and mitigation of oceanic hazards as marine pollution and tsunamis; AI-enhanced model improvement, data assimilation, and hybrid modeling.

While the dedicated AI research fields continue rapidly to develop novel methodologies, these advances often take time to permeate into domain-specific sciences (e.g., oceanography). Here, we are especially interested in those recently adapted, emerging AI methods that are just beginning to find application in the ocean sciences.

This session welcomes interdisciplinary contributions from biology and physical oceanography, machine learning, data science, and applied physics. We aim to showcase innovative AI tools that advance scientific insight and operational forecasting in ocean environments.

Co-Sponsor(s):
  • Climate and Ocean Change -
  • Ocean Biology and Biogeochemistry -
  • Ocean Technologies and Observatories -
Index Terms:

0545 Modeling [COMPUTATIONAL GEOPHYSICS]
0555 Neural networks, fuzzy logic, machine learning [COMPUTATIONAL GEOPHYSICS]
4599 General or miscellaneous [OCEANOGRAPHY: PHYSICAL]
4899 General or miscellaneous [OCEANOGRAPHY: BIOLOGICAL AND CHEMICAL]
Student/Early Career Chair:  Haoyu Wang, Institute of Oceanography, Chinese Academy of Sciences, Qingdao, China
Primary Chair:  Jan Saynisch Wagner, GFZ Helmholtz Centre for Geosciences, Potsdam, Germany
Co-chairs:  Xiaofeng Li, Institute of Oceanography, Chinese Academy of Sciences, Qingdao, China, Janet Sprintall, Univ California San Diego, La Jolla, United States and Maike Sonnewald, University of California Davis, Computer Science, Davis, United States
 
AI-driven short-term forecasts of sea surface currents using HF Radar data (1858506)
Luísa Lamas, Nuno Zacarias, Manuel Narayan and Teotónio Barroqueiro, Instituto Hidrográfico, Lisbon, Portugal
 
Predicting wave parameters on unstructured grids using a Graph Neural Network (1909621)
Federica Benassi1,2, Lorenzo Mentaschi1,2, Nadia Pinardi2, Salvatore Causio1, Daniel Holmberg3, Italo Epicoco1,4 and Ivan Federico1, (1)CMCC Foundation - Euro-Mediterranean Center on Climate Change, Lecce, Italy, (2)University of Bologna, Department of Physics and Astronomy, Bologna, Italy, (3)University of Helsinki, Department of Computer Science, Helsinki, Finland, (4)University of Salento, Department of Engineering for Innovation, Lecce, Italy
 
GDCM2: A Self-supervised Deep Model for Accurate Satellite Chlorophyll Reconstruction (2006866)
Haoyu Wang, University of Chinese Academy of Sciences, Beijing, China and Xiaofeng Li, Institute of Oceanography, Chinese Academy of Sciences, Qingdao, China
 
Machine Learning techniques reveal evolving patterns of ocean carbon uptake (2007119)
Ms. Sweety Mohanty1,2, Lavinia Patara2, Willi Rath2, Daniyal Kazempour1 and Peer Kröger1, (1)Christian-Albrechts-Universität zu Kiel, Kiel, Germany, (2)GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
 
Improving AI-Based Extreme ENSO Predictions using Conditional Nonlinear Optimal Perturbations (2008912)
Dr. Lu Zhou, NUIST Nanjing University of Information Science and Technology, Nanjing, China, Rong-Hua Zhang, Nanjing University of Information Science and Technolog, School of Marine Sciences, Nanjing, China and Lingjiang Tao, Nanjing University of Information Science and Technology, Nanjing, China
 
Data-driven Ocean Models with Multi-Scale Graph Neural Networks for Eddy-resolving Global Forecasting (2010668)
Yuta Hirabayashi, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan, Daisuke Matsuoka, Japan Agency for Marine-Earth Science and Technology, Yokohama-shi, Kanagawa, Japan and Konobu Kimura, FURUNO ELECTRIC CO., LTD., Nishinomiya, Japan
 
Resolving the Spatiotemporal Distribution of Substances in Fluids Using a Deep Learning Method (2013196)
Dr. Zaiyang Zhou, PhD, Jianzhong Ge2, Dirk Sebastiaan van Maren3 and Pingxing Ding2, (1)East China Normal University, Shanghai, China, (2)Delft University of Technology, Deltares, Delft, Netherlands
 
Machine Learning for Accelerating Ocean Spin-Up (2014049)
Isaac Akanho1, Matthew Archer1, Surbhi Goel1, Julie Deshayes2, Etienne Meunier2, Guillaume Gachon2 and Maud Tissot2, (1)University of Cambridge, Cambridge, United Kingdom, (2)Institut Pierre Simon Laplace, LOCEAN, Paris, France
 
Cloud-Free Sea Surface Temperature Reconstruction from Multi-Satellite Observations by Deep Learning (2014906)
Mengjiao Wang1, Haoyu Wang2 and Xiaofeng Li2, (1)Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China, (2)Institute of Oceanography, Chinese Academy of Sciences, Qingdao, China
 
Identifying Tsunami Hotspots through Multi-Hazard Assessment in the Indo-Sri Lankan-Andaman Region (2017088)
Prof Daya Shanker and Nazeel Sabah, Indian Institute of Technology Roorkee, Earthquake Engineering, Roorkee, India
 
Neural Networks Predicting Submesoscale Tracer Dispersion (2017487)
Mayank Kumar Bijay, International centre for theoretical sciences - Tata institute for theoretical sciences, Bangalore, India and Jim Thomas, International Centre for Theoretical Sciences, Tata Institute of Fundamental Research, Bengaluru, India
 
Jingwei: AI Foundation Model Reconstructs Global Ocean Element Cycles Under Climate Change (2017636)
Bin Lu1, Meng Jin2, Lei Zhou3, Takamitsu Ito4, Lijing Cheng5, Yuanlong Li6, Xiaoying Gan1, Luoyi Fu7, Xinbing Wang1, Chenghu Zhou8 and Jing Zhang9, (1)Shanghai Jiao Tong University, Department of Electronic Engineering, Shanghai, China, (2)Shanghai Jiao Tong University, School of Artificial Intelligence, Shanghai, China, (3)Shanghai Jiao Tong University, School of Oceanography, Shanghai, China, (4)Georgia Institute of Technology, School of Earth & Atmospheric Sciences, Atlanta, United States, (5)Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China, (6)Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China, (7)Shanghai Jiao Tong University, Department of Computer Science, Shanghai, China, (8)IGSNRR Institute of Geographic Sciences and Natural Resources Research, CAS, Institute of Geographic Sciences and Natural Resources Research, Beijing, China, (9)East China Normal University, State Key Laboratory of Estuarine and Coastal Research, Shanghai, China
 
High-Resolution Short-Term Prediction of Sea Surface Currents Around the Korean Peninsula Using Physics-Informed Neural Networks (PINN) Approach (2017820)
Ho-Jeong Ju1, Jae-Hun Park1, Jeong-Yeob Chae, PhD2, Young-Taeg Kim3, Min-su Kim4 and Byoung-Ju Choi5, (1)Inha University, Ocean Sciences, Incheon, South Korea, (2)University of Rhode Island, Narragansett, United States, (3)Korea Hydrographic and Oceanographic Agency, Busan, South Korea, (4)Korea Hydrographic and Oceanographic Agency, Ocean Research Division, Busan, South Korea, (5)Chonnam National University, Oceanography, Gwangju, South Korea
 
A Data-Driven Stochastic Model for Probabilistic Ocean Prediction Across Seasonal to Climate Scales (2018309)
Anass El Aouni1, Quentin Gaudel2, Simon van Gennip1, Yann Drillet3 and Marie Drevillon4, (1)Mercator Ocean International, Toulouse, France, (2)Mercator Ocean International, Digital Ocean, Toulouse, France, (3)Mercator Ocean international, Toulouse, France, (4)Mercator Océan, Toulouse, France
 
Using machine learning to interpolate historic ocean observations of the Atlantic subpolar gyre towards prediction of tipping points (2019598)
Jonathan Coney1, Alejandra Sanchez-Franks2, Tiago Segabinazzi Dotto2, Alberto Arribas2, Ben I Moat2, Tillys Petit2, Jennifer Mecking2, Amber Walsh2, Didier Swingedouw3, Sybren Sjoerd Drijfhout4 and Eric Orenstein2, (1)National Oceanography Centre, Liverpool, United Kingdom, (2)National Oceanography Centre, Southampton, United Kingdom, (3)University Bordeaux, CNRS, Bordeaux INP, EPOC, UMR 5805, Pessac, France, (4)University of Southampton, Southampton, United Kingdom
 
Deep Random Features for Scalable Spatiotemporal Interpolation of Ocean Data (2022829)
Weibin Chen, University College London, Department of Earth Sciences, London, United Kingdom, Azhir Mahmood, University College London, London, United Kingdom, Michel Tsamados, UCL CPOM, London, United Kingdom and So Takao, California Institute of Technology, Pasadena, United States
 
Partial-convolution-based 3D deep learning framework for coastal prediction around the Korean Peninsula (2023007)
NAM-Hoon KIM, Sung-Hwan Park, Ki-Young Heo and Jin-Yong CHOI, Korea Institute of Ocean Science & Technology, Marine Natural Disaster Research Department, Busan, South Korea
 
Sea Surface Temperature Space-Time Interpolation Using Deep Learning (2023536)
Usman Ahmed Raza, The University of Naples Federico II, Computational Intelligence, Naples, Italy, Claudia Fanelli, CNR Institute of Marine Sciences, Naples, Italy, Giuseppe Longo, The University of Naples Federico II, Naples, Italy and Bruno Buongiorno Nardelli, Institute of Marine Sciences (ISMAR), National Research Council (CNR), Naples, Italy
 
Learning to Learn Ahead: Parameter Forecasting in Neural Networks for Multi-Step Time Series Prediction of Remote Sensing Observations (2023926)
Saran Rajendran Sari, Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Geodesy: Earth System Modelling, Potsdam, Germany and Jan Saynisch Wagner, GFZ Helmholtz Centre for Geosciences, Potsdam, Germany
 
AI-quifer – Predicting Offshore Groundwater Occurrences through the Application of Artificial Intelligence (2024393)
Laura Haffert1, Marion D Jegen1, Christian Siebert2 and Tino Rödiger3, (1)GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany, (2)Helmholtz Centre for Environmental Research UFZ Halle, Catchment Hydrology, Halle, Germany, (3)Helmholtz Centre for Environmental Research UFZ Halle, Halle, Germany
 
Integrating Physical and Biogeochemical Ocean Data with Deep Learning: Toward Multivariable Understanding of Ocean Behavior (2024806)
Luther Ollier, Laboratoire d’Océanologie et de Géosciences (LOG), Wimereux, France, Roy El Hourany, Université du Littoral Côte d’Opale, LOG, Wimereux, France, Marina Lévy, Laboratoire d’Océanographie et du Climat: Expérimentations et Analyses Numériques de l’Institut Pierre Simon Laplace (LOCEAN-IPSL), Paris, France, Gregory Beaugrand, LOG Laboratoire d'Océanologie et de Géosciences, Wimereux, France and Sylvie Thiria, LOCEAN, Paris Cedex 05, France
 
AI-Driven Global Sea Surface Salinity Retrieval from Ocean Color Using Optical Water Types (2025143)
SoHyun Kim1, Sihun Jung1, Eunna Jang2 and Jungho Im1, (1)Ulsan National Institute of Science and Technology, Civil, Urban, Earth, and Environmental Engineering, Ulsan, South Korea, (2)Korea Institute of Ocean Science & Technology, Korea Ocean Satellite Center, Busan, South Korea
 
End-to-end forecast of the Arctic sea ice initialized directly from observations (2026505)
Julien Brajard1, Léo Edel1, Célia Roess1, Cyril Palerme2, Anton A Korosov1 and Laurent Bertino1, (1)Nansen Environmental and Remote Sensing Center, Bergen, Norway, (2)Norwegian Meteorological Institute, Oslo, Norway
 
From surface structures to subsurface flow: physics-informed reconstruction of near-surface turbulence from surface data using the SINDy-SHRED framework (2027309)
Kristoffer Sosulski Moen1, Jørgen Røysland Aarnes2, Simen Ellingsen1 and Nathan Kutz3, (1)Norwegian University of Science and Technology, Energy and Process Engineering, Trondheim, Norway, (2)Norwegian University of Science and Technology, Department of Energy and Process Engineering, Trondheim, Norway, (3)University of Washington Seattle Campus, Departments of Applied Mathematics and Electrical & Computer Engineering, Seattle, United States
 
Internal Waves Service: Unifying Global Internal Solitary Wave Observations via Daily Sentinel-1 SAR Imagery (2027934)
Adriana Ferreira1, Joao Pinelo1, José C B da Silva2, Johnny A. Johannessen3, Joao Gonçalves1, Jorge M Magalhaes2, Bertrand Chapron4, Gael Forget5, Maarten C Buijsman6, Edwin Goh7, Muriel Pinheiro8, Christine Gommenginger9, Magda Carr10, Benjamin Holt11, Marek Stastna12, Samuel Hartharn-Evans13, Εmmanoyha Oikonomoy14, Jesús Pineda15, Kateryna Terletska16, Mihaela Hnatiuc17, Daniel Hayes18, Michael Dunphy19, Jinbo Wang20, Mirel Paun17, Jamie Shutler21 and Fabrice Collard22, (1)Atlantic International Research Centre, Data Science, Cloud Infrastructure, and Development, Angra do Heroísmo, Portugal, (2)CIMAR/CIIMAR – Interdisciplinary Centre of Marine and Environmental Research, Department of Geosciences, Environment and Spatial Planning, Porto, Portugal, (3)Nansen Environmental and Remote Sensing Center, Bergen, Norway, (4)IFREMER, Univ. Brest, CNRS, IRD, Laboratoire d'Océanographie Physique et Spatiale, Brest, France, (5)Massachusetts Institute of Technology, EAPS, Cambridge, United States, (6)University of Southern Mississippi, Division of Marine Science, Stennis Space Center, MS, United States, (7)NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, (8)European Space Agency (ESA), Frascati, Italy, (9)National Oceanography Centre, Southampton, United Kingdom, (10)Newcastle University, Newcastle, United Kingdom, (11)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, (12)University of Waterloo, Waterloo, ON, Canada, (13)Northumbria University, Newcastle, United Kingdom, (14)University of West Attica, Athens, Greece, (15)Woods Hole Oceanographic Institution, Biology, Woods Hole, United States, (16)Institute of Mathematical Machines and Systems Problems of the National Academy of Sciences of Ukraine (IMMSP), Kyiv, Ukraine, (17)Maritime University of Constanta, Constanta, Romania, (18)Cyprus Marine and Maritime Institute, Larnaca, Cyprus, (19)Fisheries and Oceans Canada, Sidney, British Columbia, Canada, (20)Texas A&M University College Station, College Station, United States, (21)University of Exeter, Exeter, United Kingdom, (22)Organization Not Listed, Washington, DC, United States
 
Seastates-NET: A Machine Learning Forecasting Model for Understanding Wave-Current Interactions. (2028128)
Inès Larroche, Laboratoire d’Océanographie Physique et Spatiale (LOPS), Univ. Brest, CNRS IRD, IFREMER, Amphitrite, France., Brest, France, Oussama Akar, Ecole Polytechnique, Amphitrite R&D, Palaiseau, France, Bertrand Chapron, IFREMER, Univ. Brest, CNRS, IRD, Laboratoire d'Océanographie Physique et Spatiale, Brest, France, Fabrice Ardhuin, University of Bretagne Occidentale, CNRS, IFREMER, IRD, Plouzané, France, Evangelos Moschos, Amphitrite, R&D, Paris, France and Hannah Bull, Amphitrite, R&D, Palaiseau, France
 
Identifying and Predicting Global Marine Ecological Provinces (2029709)
Makayla McDevitt, University of California, Davis, Applied Mathematics, Davis, CA, United States and Maike Sonnewald, University of California Davis, Computer Science, Davis, United States
 
Improving Sea Surface Height Resolution Using Satellite-Derived Chlorophyll-a and Temperature with Machine Learning (2031338)
Jorge Eduardo Velasco Zavala1, Olmo Zavala-Romero1, Julio Sheinbaum2, Luna Hiron3, Jose Roberto Miranda1, Alexandra Bozec4, Subrahmanyam Bulusu5, Eric Chassignet4 and Olmo Zavala-Romero, Julio Sheinbaum, Luna Hiron, Jose Miranda, Alexandra Bozec, Subrahmanyam Bulusu, Eric Chassignet, (1)Florida State University, Scientific Computing, Tallahassee, United States, (2)CICESE, Physical Oceanography, Ensenada, BJ, Mexico, (3)Florida State University, Center for Ocean-Atmospheric Prediction Studies, Tallahassee, United States, (4)Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, United States, (5)University of South Carolina Columbia, Columbia, SC, United States
 
A Hybrid Deep Learning Architecture for Sensor-Agnostic, Probabilistic Retrieval of Water Quality from Hyperspectral Imagery (2031946)
Sandip Banerjee, Pixxel Space India Pvt Ltd, Bengaluru, India and Jeremy Kravitz, Pixxel Space Technologies, El Segundo, United States
 
NcDashboard: Web-Native Exploratory Visualization of Ocean Data Enhanced by Large Language Models (2031979)
Olmo Zavala-Romero1, Jose Roberto Miranda1, Anand Kamble1 and Eric Chassignet2, (1)Florida State University, Scientific Computing, Tallahassee, United States, (2)Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, United States
 
Application of 3DCNN for Wave Breaker Type Classification Using Wave Flume Video Imagery (2032006)
Miyoung Yun1, Kideok Do1, Jinah Kim2, Sungwon Shin3, SeungHyun Ha1 and Byungmo Kim1, (1)National Korea Maritime & Ocean University, Ocean Engineering, Busan, South Korea, (2)Korea Institute of Ocean Science and Technology, Coastal Disaster Research Center, Korea Institute of Ocean Science and Technology, Busan, South Korea, (3)Hanyang University ERICA, Department of Marine Science and Convergent Engineering, Ansan, South Korea
 
Denoising Diffusion Probabilistic Models for Ocean Current Prediction with Sparse Observations. (2032099)
Jeffrey Caley1,2, David Luna Guitron2, Andrew Sims3, Ashley Akamine2, Ember McEwen2, Seth McCammon4 and Weifeng Gordon Zhang5, (1)UNITED STATES, (2)Pacific Lutheran University, Computer Science, Tacoma, United States, (3)Pacific Lutheran University, Physics, Tacoma, United States, (4)Woods Hole Oceanographic Institution, Applied Ocean Physics and Engineering, Woods Hole, United States, (5)WHOI, Physical Oceanography, Woods Hole, United States
 
Advancing Sea Surface Temperature Forecasting with a Focus on Cold Water Events Using Process-informed Deep Learning (2033484)
Deoksu Kim1, Seongho Song2, NAM-Hoon KIM1, Sung-Hwan Park1, Ki-Young Heo1 and Jae-IL Kwon1, (1)Korea Institute of Ocean Science & Technology, Marine Natural Disaster Research Department, Busan, South Korea, (2)University of Cincinnati Main Campus, Department of Mathematical Sciences, Division of Statistics and Data Science, Cincinnati, United States
 
Historical Local Tsunamis Along the Eastern Coast of the Korean Peninsula (2034021)
Kwang-Hee Kim, Pusan National University, Department of Geological Science, Busan, Korea, Republic of (South), Su Young Kang, Pusan National University, Institute of Geohazard Research, Busan, Korea, Republic of (South) and Taemin Ha, Kangwon National University, Department of Civil Engineering, Samcheok, South Korea
 
BG4Sea: Groundwork for Machine-Learned Multivariate Seasonal Biogeochemical Forecasting (2034329)
Gabriela Martinez Balbontin, Mercator Ocean International, Toulouse, FRANCE; Sorbonne Univeristé, LIP6, Paris, France, Anastase Charantonis, INRIA, Paris, France, Dominique Béréziat, Sorbonne Université, LIP6, Paris, France and Stefano Ciavatta, Mercator Ocean International, Toulouse, France
 
Vector and Temporal Forecasting Operators in the Latent Space of Ocean Systems (2035174)
Aaron Hornschild1, Simone Kam Yung Carsey1, Helge Friedrich Goessling2 and Jan Saynisch Wagner1, (1)GFZ Helmholtz Centre for Geosciences, Potsdam, Germany, (2)Alfred Weger Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
 
Spinup Evaluation: A Physics-Based Benchmarking Platform for ML-based Spinup of Ocean Model NEMO (2036003)
Surbhi Goel1, Matthew Archer1 and Julie Deshayes2, (1)University of Cambridge, Cambridge, United Kingdom, (2)Institut Pierre Simon Laplace, LOCEAN, Paris, France
 
Using Artificial Intelligence to Provide High Resolution Seabed Mapping in Real-time from Interferometric Sonar (2036305)
Georgina Jeffries1, Ben Milner2, Danny Websdale3 and Jacob Newman2, (1)University of East Anglia, Norwich, United Kingdom, (2)University of East Anglia, Computing Sciences, Norwich, United Kingdom, (3)GeoAcoustics, Great Yarmouth, United Kingdom
 
Sliding Window Observation Spatio Temporal Assimilation for Mesoscale Ocean Reconstruction (2037028)
Pierre Garcia1,2, Hannah Bull1, Théo Archambault2, Anastase Charantonis3 and Dominique Béréziat4, (1)Amphitrite, R&D, Palaiseau, France, (2)CNRS, LIP6, Paris Cedex 16, France, (3)INRIA, Paris, France, (4)Sorbonne Université, LIP6, Paris, France
 
Title : Constrained generative model for oceanic states generation (2037701)
Blandine Gorce, Institut Pierre Simon Laplace LOCEAN, Paris, France, Etienne Meunier, INRIA, Paris, France, Luther Ollier, Laboratoire d’Océanologie et de Géosciences (LOG), Wimereux, France, David Kamm, LOCEAN, Paris Cedex 05, France and Julie Deshayes, Institut Pierre Simon Laplace, LOCEAN, Paris, France
 
Improving Seasonal Forecasts of Global Surface Marine Heatwaves through Deep Learning (2038668)
Zeya Li, CSIRO Environment, Perth, Australia, Ming Feng, CSIRO, Environment, Crawley, Western Australia, Australia, John Taylor, CSIRO Data61, Canberra, Australia, Fabio Boschetti, CSIRO Environment, Indian Ocean Marine Research Centre, Crawley, WA 6009, Australia, Perth, Western Australia, Australia and Foivos Diakogiannis, CSIRO Data61, Perth, Australia
 
Forecasting sea ice in the Baltic Sea using deep learning (2039700)
Henri Vuollekoski, Finnish Meteorological Institute, Helsinki, Finland
 
Toward an AI-enhanced Hydro-morphodynamic Model for Nature-based Solutions in Coastal Erosion Mitigation (2044448)
Nour Dammak, Wei Chen and Joanna Staneva, Helmholtz-Zentrum Hereon, Institute of Coastal Systems-Analysis and Modelling, Geesthacht, Germany
 
Building an Intelligent Data Exploring Assistant for Oceanographers (2045519)
Matthew J Widlansky, Nemanja Komar and Yuxin Wang, University of Hawai'i at Mānoa, Cooperative Institute for Marine and Atmospheric Research, Honolulu, United States
 
Regional Emulation of Modular Ocean Model 6 (MOM6) in the Caribbean (2046638)
Aidan Janney1,2, Aneesh Subramanian1, Giovanni Gabriel Seijo-Ellis2,3, John Schreck2, Frederic S Castruccio2 and Daniel E Amrhein2, (1)University of Colorado at Boulder, Atmospheric and Oceanic Sciences, Boulder, United States, (2)NSF National Center for Atmospheric Research, Boulder, United States, (3)University of Puerto Rico Mayaguez, Mayaguez, United States
 
Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence and Oceanic Flows (2047509)
Akhil Sadam1, Anantha Narayanan Suresh Babu2, Christopher Mirabito2, Patrick J Haley Jr2 and Pierre F J Lermusiaux3, (1)Massachusetts Institute of Technology, Center for Computational Science and Engineering, Cambridge, United States, (2)Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, United States, (3)Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, MA, United States
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