Articles | Volume 22, issue 3
https://doi.org/10.5194/os-22-2083-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-22-2083-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A Digital Twin Ocean: can we improve coastal ocean forecasts using targeted marine autonomy?
Plymouth Marine Laboratory, Plymouth, PL1 2LP, UK
National Centre for Earth Observation, Leicester, LE4 5SP, UK
Deep Banerjee
Plymouth Marine Laboratory, Plymouth, PL1 2LP, UK
National Centre for Earth Observation, Leicester, LE4 5SP, UK
David Ford
Met Office, Exeter, EX1 3PB, UK
Ke Wang
Shanghai Jiao Tong University, Shanghai, 200240, China
University of Exeter, Exeter, EX4 4PY, UK
Jozef Skákala
Plymouth Marine Laboratory, Plymouth, PL1 2LP, UK
National Centre for Earth Observation, Leicester, LE4 5SP, UK
Juliane Wihsgott
Plymouth Marine Laboratory, Plymouth, PL1 2LP, UK
Prathyush P. Menon
University of Exeter, Exeter, EX4 4PY, UK
Susan Kay
Plymouth Marine Laboratory, Plymouth, PL1 2LP, UK
Met Office, Exeter, EX1 3PB, UK
Daniel Clewley
Plymouth Marine Laboratory, Plymouth, PL1 2LP, UK
National Centre for Earth Observation, Leicester, LE4 5SP, UK
Andrea Rochner
Met Office, Exeter, EX1 3PB, UK
Emma Sullivan
Plymouth Marine Laboratory, Plymouth, PL1 2LP, UK
National Centre for Earth Observation, Leicester, LE4 5SP, UK
Matthew Palmer
Plymouth Marine Laboratory, Plymouth, PL1 2LP, UK
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Ocean Sci., 22, 1457–1481, https://doi.org/10.5194/os-22-1457-2026, https://doi.org/10.5194/os-22-1457-2026, 2026
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Yumeng Chen, Dale Partridge, and Lars Nerger
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Jeff Polton, James Harle, Jason Holt, Anna Katavouta, Dale Partridge, Jenny Jardine, Sarah Wakelin, Julia Rulent, Anthony Wise, Katherine Hutchinson, David Byrne, Diego Bruciaferri, Enda O'Dea, Michela De Dominicis, Pierre Mathiot, Andrew Coward, Andrew Yool, Julien Palmiéri, Gennadi Lessin, Claudia Gabriela Mayorga-Adame, Valérie Le Guennec, Alex Arnold, and Clément Rousset
Geosci. Model Dev., 16, 1481–1510, https://doi.org/10.5194/gmd-16-1481-2023, https://doi.org/10.5194/gmd-16-1481-2023, 2023
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The aim is to increase the capacity of the modelling community to respond to societally important questions that require ocean modelling. The concept of reproducibility for regional ocean modelling is developed: advocating methods for reproducible workflows and standardised methods of assessment. Then, targeting the NEMO framework, we give practical advice and worked examples, highlighting key considerations that will the expedite development cycle and upskill the user community.
Marion Mittermaier, Rachel North, Jan Maksymczuk, Christine Pequignet, and David Ford
Ocean Sci., 17, 1527–1543, https://doi.org/10.5194/os-17-1527-2021, https://doi.org/10.5194/os-17-1527-2021, 2021
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Regions of enhanced chlorophyll-a concentrations can be identified by applying a threshold to the concentration value to a forecast and observed field (or analysis). These regions can then be treated and analysed as features using diagnostic techniques to consider of the evolution of the chlorophyll-a blooms in space and time. This allows us to understand whether the biogeochemistry in the model has any skill in predicting these blooms, their location, intensity, onset, duration and demise.
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Short summary
This study outlines the development and testing of a Digital Twin Ocean (DTO) framework, aimed at improving coastal ocean forecasts through the use of autonomous underwater gliders. A fleet of gliders were deployed in the western English Channel during August–September 2024 to collect measurements of temperature, salinity, chlorophyll and oxygen, aiming to track the movement of the harmful algal bloom Karenia mikimotoi.
This study outlines the development and testing of a Digital Twin Ocean (DTO) framework, aimed...