Articles | Volume 5-opsr
https://doi.org/10.5194/sp-5-opsr-25-2025
© 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-25-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
End-user applications for ocean forecasting: present status description
Antonio Novellino
CORRESPONDING AUTHOR
ETT S.p.A., Genoa, Italy
Alain Arnaud
Mercator Ocean International, Toulouse, France
Andreas Schiller
CSIRO Environment, Castray Esplanade, Hobart, Tasmania, Australia
Liying Wan
National Marine Environmental Forecasting Center Beijing, Beijing, China
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This paper discusses the vital role of observations in ocean predictions and forecasting, highlighting the need for effective access, management, and integration of data to improve models and decision-making. The paper also explores opportunities for standardizing protocols and the potential of citizen-based, cost-effective data collection methods.
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Ocean prediction relies on the integration between models and satellite and in situ observations through data assimilation techniques. The authors discuss the role of observations in operational ocean forecasting systems, describing the state of the art of satellite and in situ observing networks and defining the paths for addressing multi-scale monitoring and forecasting.
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This study explored how sea level is changing along the China-Europe Sea Route. By combining satellite and in-situ observations with advanced modeling, the research identified ongoing sea level rise and an increasing frequency of extreme water level events in some regions. These findings underscore the importance of continued monitoring and provide useful knowledge to support long-term planning, coastal resilience, and informed decision-making.
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The study illustrates the way atmospheric fields are used in ocean models as boundary conditions for the provisioning of the exchanges of heat, freshwater, and momentum fluxes. Such fluxes can be based on remote sensing instruments or provided directly by numerical weather prediction systems. Air–sea flux datasets are defined by their spatial and temporal resolutions and are limited by associated biases. Air–sea flux datasets for ocean models should be chosen with the applications in mind.
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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.
Liying Wan, Marcos Garcia Sotillo, Mike Bell, Yann Drillet, Roland Aznar, and Stefania Ciliberti
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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.
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This paper discusses the vital role of observations in ocean predictions and forecasting, highlighting the need for effective access, management, and integration of data to improve models and decision-making. The paper also explores opportunities for standardizing protocols and the potential of citizen-based, cost-effective data collection methods.
Pierre-Yves Le Traon, Antonio Novellino, and Andrew M. Moore
State Planet, 5-opsr, 7, https://doi.org/10.5194/sp-5-opsr-7-2025, https://doi.org/10.5194/sp-5-opsr-7-2025, 2025
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Ocean prediction relies on the integration between models and satellite and in situ observations through data assimilation techniques. The authors discuss the role of observations in operational ocean forecasting systems, describing the state of the art of satellite and in situ observing networks and defining the paths for addressing multi-scale monitoring and forecasting.
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We provide an introduction to physical ocean models, at elementary and intermediate levels, describing the properties they represent, the principles and equations they use to evolve these properties, the physical phenomena they simulate, and the wider context and prospects for their further development. We also outline, at a more technical level, the methods and approximations that they use and the difficulties that limit their accuracy or reliability.
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Short summary
The paper describes the significant role that ocean forecasting systems play in the blue economy, demonstrating their direct benefits in improving prediction accuracy and downstream applications.
The paper describes the significant role that ocean forecasting systems play in the blue...
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