Articles | Volume 5-opsr
https://doi.org/10.5194/sp-5-opsr-15-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/sp-5-opsr-15-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An introduction to operational chains in ocean forecasting
Liying Wan
CORRESPONDING AUTHOR
Key Laboratory of Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Ministry of Natural Resources, Beijing, China
Marcos Garcia Sotillo
Nologin Oceanic Weather Systems, Santiago de Compostela, Spain
Mike Bell
Met Office,nExeter, UK
Yann Drillet
Mercator Ocean International, Toulouse, France
Roland Aznar
Nologin Oceanic Weather Systems, Santiago de Compostela, Spain
Stefania Ciliberti
Nologin Oceanic Weather Systems, Santiago de Compostela, Spain
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Operational ocean prediction relies on computationally expensive numerical models and complex workflows, known as data assimilation, in which models are fit to observations to produce optimal initial conditions for prediction. Machine learning has the potential to vastly accelerate ocean prediction, improve uncertainty quantification through massive surrogate model-based ensembles, and render simulations more accurate through better model calibration. We review the developments and challenges.
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This paper explores how using multiple predictions instead of just one can improve ocean forecasts and help prepare for changes in ocean conditions. By combining different forecasts, scientists can better understand the uncertainty in predictions, leading to more reliable forecasts and better decision-making. This method is useful for responding to hazards like oil spills, improving climate forecasts, and supporting decision-making in fields like marine safety and resource management.
Patrick Heimbach, Fearghal O'Donncha, Timothy A. Smith, Jose Maria Garcia-Valdecasas, Alain Arnaud, and Liying Wan
State Planet, 5-opsr, 22, https://doi.org/10.5194/sp-5-opsr-22-2025, https://doi.org/10.5194/sp-5-opsr-22-2025, 2025
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Operational ocean prediction relies on computationally expensive numerical models and complex workflows, known as data assimilation, in which models are fit to observations to produce optimal initial conditions for prediction. Machine learning has the potential to vastly accelerate ocean prediction, improve uncertainty quantification through massive surrogate model-based ensembles, and render simulations more accurate through better model calibration. We review the developments and challenges.
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State Planet, 5-opsr, 6, https://doi.org/10.5194/sp-5-opsr-6-2025, https://doi.org/10.5194/sp-5-opsr-6-2025, 2025
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Ocean forecast systems provide information about a future state of the ocean. This information is provided in the form of decision support tools, or downstream applications, that can be accessed by various stakeholders to support livelihoods, coastal resilience and the good governance of the marine environment. This paper provides an overview of the various downstream applications of ocean forecast systems that are utilized around the world.
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This preprint is open for discussion and under review for Ocean Science (OS).
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Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
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Jozef Skakala, David Ford, Keith Haines, Amos Lawless, Matthew Martin, Philip Browne, Marcin Chrust, Stefano Ciavatta, Alison Fowler, Daniel Lea, Matthew Palmer, Andrea Rochner, Jennifer Waters, Hao Zuo, Mike Bell, Davi Carneiro, Yumeng Chen, Susan Kay, Dale Partridge, Martin Price, Richard Renshaw, Georgy Shapiro, and James While
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Preprint archived
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The assimilation of altimetry data corrects and improves the forecast of a global ocean forecasting system. Until now, the use of altimetry observations from nadir altimeters has had a major impact on the quality of ocean forecasts. Our study shows that the use of observations from swath altimeters will have a greater impact than the quality of these forecasts and will better constrain mesoscale structures.
Álvaro de Pascual-Collar, Roland Aznar, Bruno Levier, and Marcos García-Sotillo
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The article comprises the analysis of the ocean heat content in the northeastern Atlantic Iberian–Biscay–Ireland (IBI) region. The variability of ocean heat content is studied, and results are linked with the variability of the main water masses found in the region. Results show how the coupled interannual variability of water masses accounts for an important part of the total ocean heat content variability in the region.
Stefania A. Ciliberti, Enrique Alvarez Fanjul, Jay Pearlman, Kirsten Wilmer-Becker, Pierre Bahurel, Fabrice Ardhuin, Alain Arnaud, Mike Bell, Segolene Berthou, Laurent Bertino, Arthur Capet, Eric Chassignet, Stefano Ciavatta, Mauro Cirano, Emanuela Clementi, Gianpiero Cossarini, Gianpaolo Coro, Stuart Corney, Fraser Davidson, Marie Drevillon, Yann Drillet, Renaud Dussurget, Ghada El Serafy, Katja Fennel, Marcos Garcia Sotillo, Patrick Heimbach, Fabrice Hernandez, Patrick Hogan, Ibrahim Hoteit, Sudheer Joseph, Simon Josey, Pierre-Yves Le Traon, Simone Libralato, Marco Mancini, Pascal Matte, Angelique Melet, Yasumasa Miyazawa, Andrew M. Moore, Antonio Novellino, Andrew Porter, Heather Regan, Laia Romero, Andreas Schiller, John Siddorn, Joanna Staneva, Cecile Thomas-Courcoux, Marina Tonani, Jose Maria Garcia-Valdecasas, Jennifer Veitch, Karina von Schuckmann, Liying Wan, John Wilkin, and Romane Zufic
State Planet, 1-osr7, 2, https://doi.org/10.5194/sp-1-osr7-2-2023, https://doi.org/10.5194/sp-1-osr7-2-2023, 2023
Jean-Michel Lellouche, Eric Greiner, Olivier Le Galloudec, Gilles Garric, Charly Regnier, Marie Drevillon, Mounir Benkiran, Charles-Emmanuel Testut, Romain Bourdalle-Badie, Florent Gasparin, Olga Hernandez, Bruno Levier, Yann Drillet, Elisabeth Remy, and Pierre-Yves Le Traon
Ocean Sci., 14, 1093–1126, https://doi.org/10.5194/os-14-1093-2018, https://doi.org/10.5194/os-14-1093-2018, 2018
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In the coming decades, a strong growth of the ocean economy is expected. Scientific advances in operational oceanography will play a crucial role in addressing many environmental challenges and in the development of ocean-related economic activities. In this context, remarkable improvements have been achieved with the current Mercator Ocean system. 3-D water masses, sea level, sea ice and currents have been improved, and thus major oceanic variables are hard to distinguish from the data.
Giovanni Coppini, Palmalisa Marra, Rita Lecci, Nadia Pinardi, Sergio Cretì, Mario Scalas, Luca Tedesco, Alessandro D'Anca, Leopoldo Fazioli, Antonio Olita, Giuseppe Turrisi, Cosimo Palazzo, Giovanni Aloisio, Sandro Fiore, Antonio Bonaduce, Yogesh Vittal Kumkar, Stefania Angela Ciliberti, Ivan Federico, Gianandrea Mannarini, Paola Agostini, Roberto Bonarelli, Sara Martinelli, Giorgia Verri, Letizia Lusito, Davide Rollo, Arturo Cavallo, Antonio Tumolo, Tony Monacizzo, Marco Spagnulo, Rorberto Sorgente, Andrea Cucco, Giovanni Quattrocchi, Marina Tonani, Massimiliano Drudi, Paola Nassisi, Laura Conte, Laura Panzera, Antonio Navarra, and Giancarlo Negro
Nat. Hazards Earth Syst. Sci., 17, 533–547, https://doi.org/10.5194/nhess-17-533-2017, https://doi.org/10.5194/nhess-17-533-2017, 2017
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SeaConditions aims to support the users by providing the environmental information in due time and with adequate accuracy in the marine and coastal environments, enforcing users' sea situational awareness. SeaConditions consists of a web and mobile application for the provision of meteorological and oceanographic observation and forecasting products. The iOS/Android apps were downloaded by more than 105 000 users and more than 100 000 users have visited the web version (www.sea-conditions.com).
Nadia Pinardi, Vladyslav Lyubartsev, Nicola Cardellicchio, Claudio Caporale, Stefania Ciliberti, Giovanni Coppini, Francesca De Pascalis, Lorenzo Dialti, Ivan Federico, Marco Filippone, Alessandro Grandi, Matteo Guideri, Rita Lecci, Lamberto Lamberti, Giuliano Lorenzetti, Paolo Lusiani, Cosimo Damiano Macripo, Francesco Maicu, Michele Mossa, Diego Tartarini, Francesco Trotta, Georg Umgiesser, and Luca Zaggia
Nat. Hazards Earth Syst. Sci., 16, 2623–2639, https://doi.org/10.5194/nhess-16-2623-2016, https://doi.org/10.5194/nhess-16-2623-2016, 2016
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A multiscale sampling experiment was carried out in the Gulf of Taranto (eastern Mediterranean) providing the first synoptic evidence of the large-scale circulation structure and associated mesoscale variability. The circulation is shown to be dominated by an anticyclonic gyre and upwelling areas at the gyre periphery.
Cited articles
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Short summary
Operating the ocean value chain requires the implementation of steps that must work systematically and automatically to generate ocean predictions and deliver this information. The paper illustrates the main challenges foreseen by operational chains in integrating complex numerical frameworks from the global to coastal scale and discusses existing tools that facilitate orchestration, including examples of existing systems and their capacity to provide high-quality and timely ocean forecasts.
Operating the ocean value chain requires the implementation of steps that must work...
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