Articles | Volume 5-opsr
https://doi.org/10.5194/sp-5-opsr-4-2025
© 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-4-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
02 Jun 2025
| OPSR | Chapter 2.3
| 02 Jun 2025 | OPSR | Chapter 2.3
Solving coastal dynamics: introduction to high-resolution ocean forecasting services
Institute of Coastal Systems – Analysis and Modelling, Helmholtz-Zentrum Hereon, Geesthacht, Germany
Angelique Melet
Mercator Ocean International, Toulouse, France
Jennifer Veitch
Egagasini Node, South African Environmental Observation Network (SAEON), Cape Town, South Africa
Pascal Matte
Meteorological Research Division, Environment and Climate Change Canada, Québec, QC, Canada
Related authors
Pascal Matte, John Wilkin, and Joanna Staneva
State Planet, 5-opsr, 19, https://doi.org/10.5194/sp-5-opsr-19-2025, https://doi.org/10.5194/sp-5-opsr-19-2025, 2025
Short summary
Short summary
Rivers, vital to the Earth's system, connect the ocean with the land, governing hydrological and biogeochemical contributions and influencing processes like upwelling and mixing. This paper reviews methods to represent river runoff in operational ocean forecasting systems, from coarse-resolution models to coastal coupling approaches. It discusses river data sources and examines how river forcing is treated in global to coastal operational systems, highlighting challenges and future directions.
Mauro Cirano, Enrique Alvarez-Fanjul, Arthur Capet, Stefania Ciliberti, Emanuela Clementi, Boris Dewitte, Matias Dinápoli, Ghada El Serafy, Patrick Hogan, Sudheer Joseph, Yasumasa Miyazawa, Ivonne Montes, Diego A. Narvaez, Heather Regan, Claudia G. Simionato, Gregory C. Smith, Joanna Staneva, Clemente A. S. Tanajura, Pramod Thupaki, Claudia Urbano-Latorre, and Jennifer Veitch
State Planet, 5-opsr, 5, https://doi.org/10.5194/sp-5-opsr-5-2025, https://doi.org/10.5194/sp-5-opsr-5-2025, 2025
Short summary
Short summary
Operational ocean forecasting systems (OOFSs) are crucial for human activities, environmental monitoring, and policymaking. An assessment across eight key regions highlights strengths and gaps, particularly in coastal and biogeochemical forecasting. AI offers improvements, but collaboration, knowledge sharing, and initiatives like the OceanPrediction Decade Collaborative Centre (DCC) are key to enhancing accuracy, accessibility, and global forecasting capabilities.
Roderik van de Wal, Angélique Melet, Debora Bellafiore, Paula Camus, Christian Ferrarin, Gualbert Oude Essink, Ivan D. Haigh, Piero Lionello, Arjen Luijendijk, Alexandra Toimil, Joanna Staneva, and Michalis Vousdoukas
State Planet, 3-slre1, 5, https://doi.org/10.5194/sp-3-slre1-5-2024, https://doi.org/10.5194/sp-3-slre1-5-2024, 2024
Short summary
Short summary
Sea level rise has major impacts in Europe, which vary from place to place and in time, depending on the source of the impacts. Flooding, erosion, and saltwater intrusion lead, via different pathways, to various consequences for coastal regions across Europe. This causes damage to assets, the environment, and people for all three categories of impacts discussed in this paper. The paper provides an overview of the various impacts in Europe.
Wei Chen and Joanna Staneva
State Planet, 4-osr8, 7, https://doi.org/10.5194/sp-4-osr8-7-2024, https://doi.org/10.5194/sp-4-osr8-7-2024, 2024
Short summary
Short summary
Marine heatwaves (MHWs), which are the unusually warm periods in the ocean, are becoming more frequent and lasting longer in the northwest European Shelf (NWES), particularly near the coast, from 1993 to 2023. However, thermal stratification is weakening, implying that the sea surface warming caused by MHWs is insufficient to counteract the overall stratification decline due to global warming. Moreover, the varying salinity has a notable impact on the trend of density stratification change.
Carolina B. Gramcianinov, Joanna Staneva, Celia R. G. Souza, Priscila Linhares, Ricardo de Camargo, and Pedro L. da Silva Dias
State Planet, 1-osr7, 12, https://doi.org/10.5194/sp-1-osr7-12-2023, https://doi.org/10.5194/sp-1-osr7-12-2023, 2023
Short summary
Short summary
We analyse extreme wave event trends in the south-western South Atlantic in the last 29 years using wave products and coastal hazard records. The results show important regional changes associated with increased mean sea wave height, wave period, and wave power. We also find a rise in the number of coastal hazards related to waves affecting the state of São Paulo, Brazil, which partially agrees with the increase in extreme waves in the adjacent ocean sector but is also driven by local factors.
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
Kathrin Wahle, Emil V. Stanev, and Joanna Staneva
Nat. Hazards Earth Syst. Sci., 23, 415–428, https://doi.org/10.5194/nhess-23-415-2023, https://doi.org/10.5194/nhess-23-415-2023, 2023
Short summary
Short summary
Knowledge of what causes maximum water levels is often key in coastal management. Processes, such as storm surge and atmospheric forcing, alter the predicted tide. Whilst most of these processes are modeled in present-day ocean forecasting, there is still a need for a better understanding of situations where modeled and observed water levels deviate from each other. Here, we will use machine learning to detect such anomalies within a network of sea-level observations in the North Sea.
Wei Chen, Joanna Staneva, Sebastian Grayek, Johannes Schulz-Stellenfleth, and Jens Greinert
Nat. Hazards Earth Syst. Sci., 22, 1683–1698, https://doi.org/10.5194/nhess-22-1683-2022, https://doi.org/10.5194/nhess-22-1683-2022, 2022
Short summary
Short summary
This study links the occurrence and persistence of density stratification in the southern North Sea to the increased number of extreme marine heat waves. The study further identified the role of the cold spells at the early stage of a year to the intensity of thermal stratification in summer. In a broader context, the research will have fundamental significance for further discussion of the secondary effects of heat wave events, such as in ecosystems, fisheries, and sediment dynamics.
Johannes Pein, Annika Eisele, Richard Hofmeister, Tina Sanders, Ute Daewel, Emil V. Stanev, Justus van Beusekom, Joanna Staneva, and Corinna Schrum
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-265, https://doi.org/10.5194/bg-2019-265, 2019
Revised manuscript not accepted
Short summary
Short summary
The Elbe estuary is subject to vigorous tidal forcing from the sea side and considerable biological inputs from the land side. Our 3D numerical coupled physical-biogeochemical integrates these forcing signals and provides highly realistic hindcasts of the associated dynamics. Model simulations show that the freshwater part of Elbe estuary is inhabited by plankton. According to simulations these organism play a key role in converting organic inputs into nitrate, the major inorganic nutrient.
Huw W. Lewis, Juan Manuel Castillo Sanchez, John Siddorn, Robert R. King, Marina Tonani, Andrew Saulter, Peter Sykes, Anne-Christine Pequignet, Graham P. Weedon, Tamzin Palmer, Joanna Staneva, and Lucy Bricheno
Ocean Sci., 15, 669–690, https://doi.org/10.5194/os-15-669-2019, https://doi.org/10.5194/os-15-669-2019, 2019
Short summary
Short summary
Forecasts of ocean temperature, salinity, currents, and sea height can be improved by linking state-of-the-art ocean and wave models, so that they can interact to better represent the real world. We test this approach in an ocean model of north-west Europe which can simulate small-scale details of the ocean state. The intention is to implement the system described in this study for operational use so that improved information can be provided to users of ocean forecast data.
Johannes Schulz-Stellenfleth and Joanna Staneva
Ocean Sci., 15, 249–268, https://doi.org/10.5194/os-15-249-2019, https://doi.org/10.5194/os-15-249-2019, 2019
Short summary
Short summary
Errors of observations and numerical model data are analysed with a focus on heterogeneous coastal areas. An extension of the triple collocation method is proposed, which takes into account gradients in the collocation of datasets separated by distances which may not be acceptable for a nearest-neigbour approximation, but still be feasible for linear or higher order interpolations. The technique is applied to wave height data from in situ stations, models, and the Sentinel-3A altimeter.
Anne Wiese, Joanna Staneva, Johannes Schulz-Stellenfleth, Arno Behrens, Luciana Fenoglio-Marc, and Jean-Raymond Bidlot
Ocean Sci., 14, 1503–1521, https://doi.org/10.5194/os-14-1503-2018, https://doi.org/10.5194/os-14-1503-2018, 2018
Short summary
Short summary
The increase of data quality of wind and wave measurements provided by the new Sentinel-3A satellite in coastal areas is demonstrated compared to measurements of older satellites with in situ data and spectral wave model simulations. Furthermore, the sensitivity of the wave model to wind forcing is evaluated using data with different temporal and spatial resolution, where an hourly temporal resolution is necessary to represent the peak of extreme events better.
Burkard Baschek, Friedhelm Schroeder, Holger Brix, Rolf Riethmüller, Thomas H. Badewien, Gisbert Breitbach, Bernd Brügge, Franciscus Colijn, Roland Doerffer, Christiane Eschenbach, Jana Friedrich, Philipp Fischer, Stefan Garthe, Jochen Horstmann, Hajo Krasemann, Katja Metfies, Lucas Merckelbach, Nino Ohle, Wilhelm Petersen, Daniel Pröfrock, Rüdiger Röttgers, Michael Schlüter, Jan Schulz, Johannes Schulz-Stellenfleth, Emil Stanev, Joanna Staneva, Christian Winter, Kai Wirtz, Jochen Wollschläger, Oliver Zielinski, and Friedwart Ziemer
Ocean Sci., 13, 379–410, https://doi.org/10.5194/os-13-379-2017, https://doi.org/10.5194/os-13-379-2017, 2017
Short summary
Short summary
The Coastal Observing System for Northern and Arctic Seas (COSYNA) was established in order to better understand the complex interdisciplinary processes of northern seas and the Arctic coasts in a changing environment. Particular focus is given to the heavily used German Bight in the North Sea. The automated observing and modelling system is designed to monitor real-time conditions, to provide short-term forecasts and data products, and to assess the impact of anthropogenically induced change.
Kathrin Wahle, Joanna Staneva, Wolfgang Koch, Luciana Fenoglio-Marc, Ha T. M. Ho-Hagemann, and Emil V. Stanev
Ocean Sci., 13, 289–301, https://doi.org/10.5194/os-13-289-2017, https://doi.org/10.5194/os-13-289-2017, 2017
Short summary
Short summary
Reduction of wave forecasting errors is a challenge, especially in dynamically complicated coastal ocean areas such as the southern part of the North Sea area. We study the effects of coupling between an atmospheric and two nested-grid wind wave models. Comparisons with data from in situ and satellite altimeter observations indicate that two-way coupling improves the simulation of wind and wave parameters of the model and justifies its implementation for both operational and climate simulation.
Joanna Staneva, Kathrin Wahle, Wolfgang Koch, Arno Behrens, Luciana Fenoglio-Marc, and Emil V. Stanev
Nat. Hazards Earth Syst. Sci., 16, 2373–2389, https://doi.org/10.5194/nhess-16-2373-2016, https://doi.org/10.5194/nhess-16-2373-2016, 2016
Short summary
Short summary
This study addresses the impact of wind, waves, tidal forcing and baroclinicity on the sea level of the German Bight during extreme storm events. The role of wave-induced processes, tides and baroclinicity is quantified, and the results are compared with in situ measurements and satellite data. Considering a wave-dependent approach and baroclinicity, the surge is significantly enhanced in the coastal areas and the model results are closer to observations, especially during the extreme storm.
Emil V. Stanev, Johannes Schulz-Stellenfleth, Joanna Staneva, Sebastian Grayek, Sebastian Grashorn, Arno Behrens, Wolfgang Koch, and Johannes Pein
Ocean Sci., 12, 1105–1136, https://doi.org/10.5194/os-12-1105-2016, https://doi.org/10.5194/os-12-1105-2016, 2016
Short summary
Short summary
This paper describes coastal ocean forecasting practices exemplified for the North Sea and Baltic Sea. It identifies new challenges, most of which are associated with the nonlinear behavior of coastal oceans. It describes the assimilation of remote sensing, in situ and HF radar data, prediction of wind waves and storm surges, as well as applications to search and rescue operations. Seamless applications to coastal and estuarine modeling are also presented.
Joanna Staneva, Kathrin Wahle, Heinz Günther, and Emil Stanev
Ocean Sci., 12, 797–806, https://doi.org/10.5194/os-12-797-2016, https://doi.org/10.5194/os-12-797-2016, 2016
Short summary
Short summary
This study addresses the impact of coupling between wind wave and circulation models on the quality of coastal ocean predicting systems. This topic reflects the increased interest in operational oceanography to reduce prediction errors of state estimates at coastal scales. The improved skill of the coupled forecasts compared to the non-coupled ones, in particular during extreme events, justifies the further enhancements of coastal operational systems by including wind wave models.
Pascal Matte, John Wilkin, and Joanna Staneva
State Planet, 5-opsr, 19, https://doi.org/10.5194/sp-5-opsr-19-2025, https://doi.org/10.5194/sp-5-opsr-19-2025, 2025
Short summary
Short summary
Rivers, vital to the Earth's system, connect the ocean with the land, governing hydrological and biogeochemical contributions and influencing processes like upwelling and mixing. This paper reviews methods to represent river runoff in operational ocean forecasting systems, from coarse-resolution models to coastal coupling approaches. It discusses river data sources and examines how river forcing is treated in global to coastal operational systems, highlighting challenges and future directions.
Jennifer Veitch, Enrique Alvarez-Fanjul, Arthur Capet, Stefania Ciliberti, Mauro Cirano, Emanuela Clementi, Fraser Davidson, Ghada el Serafy, Guilherme Franz, Patrick Hogan, Sudheer Joseph, Svitlana Liubartseva, Yasumasa Miyazawa, Heather Regan, and Katerina Spanoudaki
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
Short summary
Short summary
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.
Mauro Cirano, Enrique Alvarez-Fanjul, Arthur Capet, Stefania Ciliberti, Emanuela Clementi, Boris Dewitte, Matias Dinápoli, Ghada El Serafy, Patrick Hogan, Sudheer Joseph, Yasumasa Miyazawa, Ivonne Montes, Diego A. Narvaez, Heather Regan, Claudia G. Simionato, Gregory C. Smith, Joanna Staneva, Clemente A. S. Tanajura, Pramod Thupaki, Claudia Urbano-Latorre, and Jennifer Veitch
State Planet, 5-opsr, 5, https://doi.org/10.5194/sp-5-opsr-5-2025, https://doi.org/10.5194/sp-5-opsr-5-2025, 2025
Short summary
Short summary
Operational ocean forecasting systems (OOFSs) are crucial for human activities, environmental monitoring, and policymaking. An assessment across eight key regions highlights strengths and gaps, particularly in coastal and biogeochemical forecasting. AI offers improvements, but collaboration, knowledge sharing, and initiatives like the OceanPrediction Decade Collaborative Centre (DCC) are key to enhancing accuracy, accessibility, and global forecasting capabilities.
Angelique Melet, Begoña Pérez Gómez, and Pascal Matte
State Planet, 5-opsr, 11, https://doi.org/10.5194/sp-5-opsr-11-2025, https://doi.org/10.5194/sp-5-opsr-11-2025, 2025
Short summary
Short summary
Forecasting the sea level is crucial for supporting coastal management through early warning systems and for adopting adaptation strategies to mitigate climate change impacts. We provide here an overview on models commonly used for sea level forecasting, which can be based on storm surge models or ocean circulation ones, integrated on structured or unstructured grids, including an outlook on new approaches based on ensemble methods.
Fabrice Hernandez, Marcos Garcia Sotillo, and Angélique Melet
State Planet, 5-opsr, 17, https://doi.org/10.5194/sp-5-opsr-17-2025, https://doi.org/10.5194/sp-5-opsr-17-2025, 2025
Short summary
Short summary
An historical review over the last 3 decades on intercomparison projects of ocean numerical reanalysis or forecast is first proposed. From this, main issues and lessons learned are discussed in order to propose an overview of best practices and key considerations to facilitate intercomparison activities in operational oceanography.
Alisée A. Chaigneau, Angélique Melet, Aurore Voldoire, Maialen Irazoqui Apecechea, Guillaume Reffray, Stéphane Law-Chune, and Lotfi Aouf
Nat. Hazards Earth Syst. Sci., 24, 4031–4048, https://doi.org/10.5194/nhess-24-4031-2024, https://doi.org/10.5194/nhess-24-4031-2024, 2024
Short summary
Short summary
Climate-change-induced sea level rise increases the frequency of extreme sea levels. We analyze projected changes in extreme sea levels for western European coasts produced with high-resolution models (∼ 6 km). Unlike commonly used coarse-scale global climate models, this approach allows us to simulate key processes driving coastal sea level variations, such as long-term sea level rise, tides, storm surges induced by low atmospheric surface pressure and winds, waves, and their interactions.
Angélique Melet, Roderik van de Wal, Angel Amores, Arne Arns, Alisée A. Chaigneau, Irina Dinu, Ivan D. Haigh, Tim H. J. Hermans, Piero Lionello, Marta Marcos, H. E. Markus Meier, Benoit Meyssignac, Matthew D. Palmer, Ronja Reese, Matthew J. R. Simpson, and Aimée B. A. Slangen
State Planet, 3-slre1, 4, https://doi.org/10.5194/sp-3-slre1-4-2024, https://doi.org/10.5194/sp-3-slre1-4-2024, 2024
Short summary
Short summary
The EU Knowledge Hub on Sea Level Rise’s Assessment Report strives to synthesize the current scientific knowledge on sea level rise and its impacts across local, national, and EU scales to support evidence-based policy and decision-making, primarily targeting coastal areas. This paper complements IPCC reports by documenting the state of knowledge of observed and 21st century projected changes in mean and extreme sea levels with more regional information for EU seas as scoped with stakeholders.
Roderik van de Wal, Angélique Melet, Debora Bellafiore, Paula Camus, Christian Ferrarin, Gualbert Oude Essink, Ivan D. Haigh, Piero Lionello, Arjen Luijendijk, Alexandra Toimil, Joanna Staneva, and Michalis Vousdoukas
State Planet, 3-slre1, 5, https://doi.org/10.5194/sp-3-slre1-5-2024, https://doi.org/10.5194/sp-3-slre1-5-2024, 2024
Short summary
Short summary
Sea level rise has major impacts in Europe, which vary from place to place and in time, depending on the source of the impacts. Flooding, erosion, and saltwater intrusion lead, via different pathways, to various consequences for coastal regions across Europe. This causes damage to assets, the environment, and people for all three categories of impacts discussed in this paper. The paper provides an overview of the various impacts in Europe.
Bart van den Hurk, Nadia Pinardi, Alexander Bisaro, Giulia Galluccio, José A. Jiménez, Kate Larkin, Angélique Melet, Lavinia Giulia Pomarico, Kristin Richter, Kanika Singh, Roderik van de Wal, and Gundula Winter
State Planet, 3-slre1, 1, https://doi.org/10.5194/sp-3-slre1-1-2024, https://doi.org/10.5194/sp-3-slre1-1-2024, 2024
Short summary
Short summary
The Summary for Policymakers compiles findings from “Sea Level Rise in Europe: 1st Assessment Report of the Knowledge Hub on Sea Level Rise”. It covers knowledge gaps, observations, projections, impacts, adaptation measures, decision-making principles, and governance challenges. It provides information for each European basin (Mediterranean, Black Sea, North Sea, Baltic Sea, Atlantic, and Arctic) and aims to assist policymakers in enhancing the preparedness of European coasts for sea level rise.
Wei Chen and Joanna Staneva
State Planet, 4-osr8, 7, https://doi.org/10.5194/sp-4-osr8-7-2024, https://doi.org/10.5194/sp-4-osr8-7-2024, 2024
Short summary
Short summary
Marine heatwaves (MHWs), which are the unusually warm periods in the ocean, are becoming more frequent and lasting longer in the northwest European Shelf (NWES), particularly near the coast, from 1993 to 2023. However, thermal stratification is weakening, implying that the sea surface warming caused by MHWs is insufficient to counteract the overall stratification decline due to global warming. Moreover, the varying salinity has a notable impact on the trend of density stratification change.
Carolina B. Gramcianinov, Joanna Staneva, Celia R. G. Souza, Priscila Linhares, Ricardo de Camargo, and Pedro L. da Silva Dias
State Planet, 1-osr7, 12, https://doi.org/10.5194/sp-1-osr7-12-2023, https://doi.org/10.5194/sp-1-osr7-12-2023, 2023
Short summary
Short summary
We analyse extreme wave event trends in the south-western South Atlantic in the last 29 years using wave products and coastal hazard records. The results show important regional changes associated with increased mean sea wave height, wave period, and wave power. We also find a rise in the number of coastal hazards related to waves affecting the state of São Paulo, Brazil, which partially agrees with the increase in extreme waves in the adjacent ocean sector but is also driven by local factors.
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
Alisée A. Chaigneau, Stéphane Law-Chune, Angélique Melet, Aurore Voldoire, Guillaume Reffray, and Lotfi Aouf
Ocean Sci., 19, 1123–1143, https://doi.org/10.5194/os-19-1123-2023, https://doi.org/10.5194/os-19-1123-2023, 2023
Short summary
Short summary
Wind waves and swells are major drivers of coastal environment changes and can drive coastal marine hazards such as coastal flooding. In this paper, by using numerical modeling along the European Atlantic coastline, we assess how present and future wave characteristics are impacted by sea level changes. For example, at the end of the century under the SSP5-8.5 climate change scenario, extreme significant wave heights are higher by up to +40 % due to the effect of tides and mean sea level rise.
Kathrin Wahle, Emil V. Stanev, and Joanna Staneva
Nat. Hazards Earth Syst. Sci., 23, 415–428, https://doi.org/10.5194/nhess-23-415-2023, https://doi.org/10.5194/nhess-23-415-2023, 2023
Short summary
Short summary
Knowledge of what causes maximum water levels is often key in coastal management. Processes, such as storm surge and atmospheric forcing, alter the predicted tide. Whilst most of these processes are modeled in present-day ocean forecasting, there is still a need for a better understanding of situations where modeled and observed water levels deviate from each other. Here, we will use machine learning to detect such anomalies within a network of sea-level observations in the North Sea.
Wei Chen, Joanna Staneva, Sebastian Grayek, Johannes Schulz-Stellenfleth, and Jens Greinert
Nat. Hazards Earth Syst. Sci., 22, 1683–1698, https://doi.org/10.5194/nhess-22-1683-2022, https://doi.org/10.5194/nhess-22-1683-2022, 2022
Short summary
Short summary
This study links the occurrence and persistence of density stratification in the southern North Sea to the increased number of extreme marine heat waves. The study further identified the role of the cold spells at the early stage of a year to the intensity of thermal stratification in summer. In a broader context, the research will have fundamental significance for further discussion of the secondary effects of heat wave events, such as in ecosystems, fisheries, and sediment dynamics.
Alisée A. Chaigneau, Guillaume Reffray, Aurore Voldoire, and Angélique Melet
Geosci. Model Dev., 15, 2035–2062, https://doi.org/10.5194/gmd-15-2035-2022, https://doi.org/10.5194/gmd-15-2035-2022, 2022
Short summary
Short summary
Climate-change-induced sea level rise is a major threat for coastal and low-lying regions. Projections of coastal sea level changes are thus of great interest for coastal risk assessment and have significantly developed in recent years. In this paper, the objective is to provide high-resolution (6 km) projections of sea level changes in the northeastern Atlantic region bordering western Europe. For that purpose, a regional model is used to refine existing coarse global projections.
Huayang Cai, Ping Zhang, Erwan Garel, Pascal Matte, Shuai Hu, Feng Liu, and Qingshu Yang
Hydrol. Earth Syst. Sci., 24, 1871–1889, https://doi.org/10.5194/hess-24-1871-2020, https://doi.org/10.5194/hess-24-1871-2020, 2020
Short summary
Short summary
Understanding the morphological changes in estuaries due to natural processes and human interventions is especially important with regard to sustainable water management and ecological impacts on the estuarine environment. In this contribution, we explore the morphological evolution in tide-dominated estuaries by means of a novel analytical approach using the observed water levels along the channel. The method could serve as a useful tool to understand the evolution of estuarine morphology.
Johannes Pein, Annika Eisele, Richard Hofmeister, Tina Sanders, Ute Daewel, Emil V. Stanev, Justus van Beusekom, Joanna Staneva, and Corinna Schrum
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-265, https://doi.org/10.5194/bg-2019-265, 2019
Revised manuscript not accepted
Short summary
Short summary
The Elbe estuary is subject to vigorous tidal forcing from the sea side and considerable biological inputs from the land side. Our 3D numerical coupled physical-biogeochemical integrates these forcing signals and provides highly realistic hindcasts of the associated dynamics. Model simulations show that the freshwater part of Elbe estuary is inhabited by plankton. According to simulations these organism play a key role in converting organic inputs into nitrate, the major inorganic nutrient.
Huw W. Lewis, Juan Manuel Castillo Sanchez, John Siddorn, Robert R. King, Marina Tonani, Andrew Saulter, Peter Sykes, Anne-Christine Pequignet, Graham P. Weedon, Tamzin Palmer, Joanna Staneva, and Lucy Bricheno
Ocean Sci., 15, 669–690, https://doi.org/10.5194/os-15-669-2019, https://doi.org/10.5194/os-15-669-2019, 2019
Short summary
Short summary
Forecasts of ocean temperature, salinity, currents, and sea height can be improved by linking state-of-the-art ocean and wave models, so that they can interact to better represent the real world. We test this approach in an ocean model of north-west Europe which can simulate small-scale details of the ocean state. The intention is to implement the system described in this study for operational use so that improved information can be provided to users of ocean forecast data.
Johannes Schulz-Stellenfleth and Joanna Staneva
Ocean Sci., 15, 249–268, https://doi.org/10.5194/os-15-249-2019, https://doi.org/10.5194/os-15-249-2019, 2019
Short summary
Short summary
Errors of observations and numerical model data are analysed with a focus on heterogeneous coastal areas. An extension of the triple collocation method is proposed, which takes into account gradients in the collocation of datasets separated by distances which may not be acceptable for a nearest-neigbour approximation, but still be feasible for linear or higher order interpolations. The technique is applied to wave height data from in situ stations, models, and the Sentinel-3A altimeter.
Anne Wiese, Joanna Staneva, Johannes Schulz-Stellenfleth, Arno Behrens, Luciana Fenoglio-Marc, and Jean-Raymond Bidlot
Ocean Sci., 14, 1503–1521, https://doi.org/10.5194/os-14-1503-2018, https://doi.org/10.5194/os-14-1503-2018, 2018
Short summary
Short summary
The increase of data quality of wind and wave measurements provided by the new Sentinel-3A satellite in coastal areas is demonstrated compared to measurements of older satellites with in situ data and spectral wave model simulations. Furthermore, the sensitivity of the wave model to wind forcing is evaluated using data with different temporal and spatial resolution, where an hourly temporal resolution is necessary to represent the peak of extreme events better.
Burkard Baschek, Friedhelm Schroeder, Holger Brix, Rolf Riethmüller, Thomas H. Badewien, Gisbert Breitbach, Bernd Brügge, Franciscus Colijn, Roland Doerffer, Christiane Eschenbach, Jana Friedrich, Philipp Fischer, Stefan Garthe, Jochen Horstmann, Hajo Krasemann, Katja Metfies, Lucas Merckelbach, Nino Ohle, Wilhelm Petersen, Daniel Pröfrock, Rüdiger Röttgers, Michael Schlüter, Jan Schulz, Johannes Schulz-Stellenfleth, Emil Stanev, Joanna Staneva, Christian Winter, Kai Wirtz, Jochen Wollschläger, Oliver Zielinski, and Friedwart Ziemer
Ocean Sci., 13, 379–410, https://doi.org/10.5194/os-13-379-2017, https://doi.org/10.5194/os-13-379-2017, 2017
Short summary
Short summary
The Coastal Observing System for Northern and Arctic Seas (COSYNA) was established in order to better understand the complex interdisciplinary processes of northern seas and the Arctic coasts in a changing environment. Particular focus is given to the heavily used German Bight in the North Sea. The automated observing and modelling system is designed to monitor real-time conditions, to provide short-term forecasts and data products, and to assess the impact of anthropogenically induced change.
Kathrin Wahle, Joanna Staneva, Wolfgang Koch, Luciana Fenoglio-Marc, Ha T. M. Ho-Hagemann, and Emil V. Stanev
Ocean Sci., 13, 289–301, https://doi.org/10.5194/os-13-289-2017, https://doi.org/10.5194/os-13-289-2017, 2017
Short summary
Short summary
Reduction of wave forecasting errors is a challenge, especially in dynamically complicated coastal ocean areas such as the southern part of the North Sea area. We study the effects of coupling between an atmospheric and two nested-grid wind wave models. Comparisons with data from in situ and satellite altimeter observations indicate that two-way coupling improves the simulation of wind and wave parameters of the model and justifies its implementation for both operational and climate simulation.
Joanna Staneva, Kathrin Wahle, Wolfgang Koch, Arno Behrens, Luciana Fenoglio-Marc, and Emil V. Stanev
Nat. Hazards Earth Syst. Sci., 16, 2373–2389, https://doi.org/10.5194/nhess-16-2373-2016, https://doi.org/10.5194/nhess-16-2373-2016, 2016
Short summary
Short summary
This study addresses the impact of wind, waves, tidal forcing and baroclinicity on the sea level of the German Bight during extreme storm events. The role of wave-induced processes, tides and baroclinicity is quantified, and the results are compared with in situ measurements and satellite data. Considering a wave-dependent approach and baroclinicity, the surge is significantly enhanced in the coastal areas and the model results are closer to observations, especially during the extreme storm.
Emil V. Stanev, Johannes Schulz-Stellenfleth, Joanna Staneva, Sebastian Grayek, Sebastian Grashorn, Arno Behrens, Wolfgang Koch, and Johannes Pein
Ocean Sci., 12, 1105–1136, https://doi.org/10.5194/os-12-1105-2016, https://doi.org/10.5194/os-12-1105-2016, 2016
Short summary
Short summary
This paper describes coastal ocean forecasting practices exemplified for the North Sea and Baltic Sea. It identifies new challenges, most of which are associated with the nonlinear behavior of coastal oceans. It describes the assimilation of remote sensing, in situ and HF radar data, prediction of wind waves and storm surges, as well as applications to search and rescue operations. Seamless applications to coastal and estuarine modeling are also presented.
Joanna Staneva, Kathrin Wahle, Heinz Günther, and Emil Stanev
Ocean Sci., 12, 797–806, https://doi.org/10.5194/os-12-797-2016, https://doi.org/10.5194/os-12-797-2016, 2016
Short summary
Short summary
This study addresses the impact of coupling between wind wave and circulation models on the quality of coastal ocean predicting systems. This topic reflects the increased interest in operational oceanography to reduce prediction errors of state estimates at coastal scales. The improved skill of the coupled forecasts compared to the non-coupled ones, in particular during extreme events, justifies the further enhancements of coastal operational systems by including wind wave models.
Cited articles
Benkiran, M., Le Traon, P.-Y., Rémy, E., and Drillet, Y.: Impact of two high-resolution altimetry mission concepts on ocean forecasting, Front. Mar. Sci., 11, 1465065, https://doi.org/10.3389/fmars.2024.1465065, 2024.
Beucler, T., Gentine, P., Yuval, J., Gupta, A., Peng, L., Lin, J., Yu, S., Rasp, S., Ahmed, F., O'Gorman, P. A., Neelin, J. D., Lutsko, N. J., and Pritchard, M.: Climate-invariant machine learning, Science Advances, 10, eadj7250, https://doi.org/10.1126/sciadv.adj7250, 2024.
Bire, S., Mittal, T., Kang, W., Ramadhan, A., Tuckman, P. J., German, C. R., Thurnherr, A. M., and Marshall, J.: Divergent Behavior of Hydrothermal Plumes in Fresh Versus Salty Icy Ocean Worlds, J. Geophys. Res.-Planet., 128, e2023JE007740, https://doi.org/10.1029/2023JE007740, 2023.
Blumberg, A. F. and Mellor, G. L.: A description of a three-dimensional coastal ocean circulation model, in: Three-dimensional coastal ocean models, edited by: Heaps, N. S., American Geophysical Union, vol. 4, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/CO004p0001 (last access: 27 May, 2025), 1987.
Bolton, T. and Zanna, L.: Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization, J. Adv. Model. Earth Sy., 11, 376–399, https://doi.org/10.1029/2018MS001472, 2019.
Bonavita, M.: On Some Limitations of Current Machine Learning Weather Prediction Models, Geophys. Res. Lett., 51, e2023GL107377, https://doi.org/10.1029/2023GL107377, 2024.
Brajard, J., Carrassi, A., Bocquet, M., and Bartino, L.: Combining data assimilation and machine learning to infer unresolved scale parametrization, Philos. T. R. Soc. A, 379, 20200086, https://doi.org/10.1098/rsta.2020.0086, 2021.
Burchard, H. and Bolding, K.: Comparative Analysis of Four Second-Moment Turbulence Closure Models for the Oceanic Mixed Layer, J. Phys. Oceanogr., 31, 1943–1968, https://doi.org/10.1175/1520-0485(2001)031<1943:CAOFSM>2.0.CO;2, 2001.
Campuzano, F., Santos, F., Simionesei, L., Oliveira, A. R., Olmedo, E., Turiel, A., Fernandes, R., Brito, D., Alba, M., Novellino, A., and Neves, R.: Framework for Improving Land Boundary Conditions in Ocean Regional Products, Journal of Marine Science and Engineering, 10, 852, https://doi.org/10.3390/jmse10070852, 2022.
Candy, A. S. and Pietrzak, J. D.: Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models, Geosci. Model Dev., 11, 213–234, https://doi.org/10.5194/gmd-11-213-2018, 2018.
Casulli, V.: A semi-implicit finite difference method for nonhydrostatic free surface flows, Int. J. Numer. Meth. Fl., 30, 425–440, https://doi.org/10.1002/(SICI)1097-0363(19990630)30:4<425::AID-FLD847>3.0.CO;2-D, 1999.
Casulli, V. and Zanolli, P.: High resolution methods for multidimensional advection diffusion problems in free-surface hydrodynamics, Ocean Model., 10, 137–151, https://doi.org/10.1016/j.ocemod.2004.06.007, 2005.
Casulli, V. and Zanolli, P.: Semi-implicit numerical modelling of non-hydrostatic free-surface flows for environmental problems, Math. Comput. Model., 36, 1131–1149, https://doi.org/10.1016/S0895-7177(02)00264-9, 2002.
Chen, C., Liu, H., and Beardsley, R. C.:An unstructured grid, finite-volume, three dimensional, primitive equations ocean model: Application to coastal ocean and estuaries, J. Atmos. Ocean. Tech., 20, 159–186, https://doi.org/10.1175/1520-0426(2003)020<0159:AUGFVT>2.0.CO;2, 2003.
Chen, W., Jacob, B., Valle-Levinson, A., Stanev, E., Staneva, J., and Badewien, T. H.: Subtidal secondary circulation induced by eddy viscosity-velocity shear covariance in a predominantly well-mixed tidal inlet, Front. Mar. Sci., 10, 1105626,https://doi.org/10.3389/fmars.2023.1105626, 2023.
Davidson, F., Alvera-Azcárate, A., Barth, A., Brassington, G. B., Chassignet, E. P., Clementi, E., De Mey-Frémaux, P., Divakaran, P., Harris, C., Hernandez, F., Hogan, P., Hole, L. R., Holt, J., Liu, G., Lu, Y., Lorente, P., Maksymczuk, J., Martin, M., Mehra, A., Melsom, A., Mo, H., Moore, A., Oddo, P., Pascual, A,, Pequignet, A.-C., Kourafalou, V., Ryan, A., Siddorn, J., Smith, G., Spindler, D., Spindler, T., Stanev, E. V., Staneva, J., Storto, A., Tanajura, C., Vinayachandran, P. N., Wan, L., Wang, H., Zhang, Y., Zhu, X., and Zu., Z.: Synergies in Operational Oceanography: The Intrinsic Need for Sustained Ocean Observations, Front. Mar. Sci., 6, 450, https://doi.org/10.3389/fmars.2019.00450, 2019.
Debreu, L., Vouland, C., and Blayo, E.: AGRIF: Adaptive grid refinement in Fortran, Comput. Geosci., 34, 8–13, https://doi.org/10.1016/j.cageo.2007.01.009, 2008.
Debreu, L., Marchesiello, P., Penven, P., and Cambon, G.: Two-way nesting in split-explicit ocean models: Algorithms, implementation and validation, Ocean Model., 49–50, 1–21, https://doi.org/10.1016/j.ocemod.2012.03.003, 2012.
Deltares: Delft3D-Flow User Manual, https://content.oss.deltares.nl/delft3d4/Delft3D-FLOW_User_Manual.pdf (last access: 25 July 2024), 2012.
De Mey, P., Stanev, E., and Kourafalou, V.: Science in support of coastal ocean forecasting – part 1, Ocean Dynam., 67, 665–668, https://doi.org/10.1007/s10236-017-1048-1, 2017.
De Mey-Frémaux, P., Ayoub, N., Barth, A., Brewin, R., Charria, G., Campuzano, F., Ciavatta, S., Cirano, M., Edwards, C. A., Federico, I., Gao, S., Garcia Hermosa, I., Garcia Sotillo, M., Hewitt, H., Hole, L. R., Holt, J., King, R., Kourafalou, V., Lu, Y., Mourre, B., Pascual, A., Staneva, J., Stanev, E. V., Wang, H., and Zhu, X.: Model-Observations Synergy in the Coastal Ocean, Front. Mar. Sci., 6, 436, https://doi.org/10.3389/fmars.2019.00436, 2019.
Engwirda, D.: JIGSAW-GEO (1.0): locally orthogonal staggered unstructured grid generation for general circulation modelling on the sphere, Geosci. Model Dev., 10, 2117–2140, https://doi.org/10.5194/gmd-10-2117-2017, 2017.
Espino, M., Staneva, J., Alvarez-Fanjul, E., and Sanchez-Arcilla, A.: Coastal Extension of CMEMS Products. Models, Data and Applications, Front. Mar. Sci., 9, 903610, https://doi.org/10.3389/fmars.2022.903610, 2022.
Fablet, R., Febvre, Q., and Chapron, B.: Multimodal 4DVarNets for the reconstruction of sea surface dynamics from SST‐SSH synergies, IEEE T. Geosci., 61, 1–14, https://doi.org/10.1109/TGRS.2023.3268006, 2023.
Federico, I., Pinardi, N., Coppini, G., Oddo, P., Lecci, R., and Mossa, M.: Coastal ocean forecasting with an unstructured grid model in the southern Adriatic and northern Ionian seas, Nat. Hazards Earth Syst. Sci., 17, 45–59, https://doi.org/10.5194/nhess-17-45-2017, 2017.
Ferrarin, C., Bellafiore, D., Sannino, G., Bajo, M., and Umgiesser, G.: Tidal dynamics in the inter-connected Mediterranean, Marmara, Black and Azov seas, Prog. Oceanogr., 161, 102–115, https://doi.org/10.1016/j.pocean.2018.02.006, 2018.
FOCCUS: Forecasting and Observing the Open-to-Coastal Ocean for Copernicus Users Project, Grant agreement ID: 101133911, https://cordis.europa.eu/project/id/101133911/results, last access: 16 May 2025.
Fringer, O. B., Gerritsen, M., and Street, R. L.: An unstructured-grid, finite-volume, nonhydrostatic, parallel coastal ocean simulator, Ocean Model., 14, 139–173, https://doi.org/10.1016/j.ocemod.2006.03.006, 2006.
Fringer, O. B., Dawson, C. N., He, R., Ralston, D. K., and Zhang, Y. J.: The future of coastal and estuarine modelling: Findings from a workshop, Ocean Model., 143, 101458, https://doi.org/10.1016/j.ocemod.2019.101458, 2019.
Frishfelds, V., She, J., and Murawski, J.: On-demand coastal modelling with two-way nesting, Ocean Dynam., 75, 23, https://doi.org/10.1007/s10236-025-01670-x, 2025.
Fujii, Y., Remy, E., Zuo, H., Oke, P. R., Halliwell, G. R., Gasparin, F., Benkiran, M., Loose, N., Cummings, J., Xie, J., Xue, Y., Masuda, S., Smith, G.C., Balmaseda, M., Germineaud, C., Lea, D.J., Larnicol, G., Bertino, L., Bonaduce, A., Brasseur, P., Donlon, C., Heimbach, P., Kim, Y., Kourafalou, V., Le Traon, P.-Y., Martin, M., Paturi, S., Tranchant, B., and Usui, N.:. Observing system evaluation based on ocean data assimilation and prediction systems: on-going challenges and future vision for designing/supporting ocean observational networks, Front. Mar. Sci., 6, 417, https://doi.org/10.3389/fmars.2019.00417, 2019.
García-León, M., Sotillo, M. G., Mestres, M., Espino, M., and Alvarez Fanjul, E.: Improving Operational Ocean Models for the Spanish Port Authorities: Assessment of the SAMOA Coastal Forecasting Service Upgrades, Journal of Marine Science and Engineering, 10, 149, https://doi.org/10.3390/jmse10020149, 2022.
Gregory, W., Bushuk, M., Adcroft, A., Zhang, Y., and Zanna, L.: Deep Learning of Systematic Sea Ice Model Errors From Data Assimilation Increments, J. Adv. Model. Earth Sy., 15, e2023MS003757, https://doi.org/10.1029/2023MS003757, 2023.
Haid, V., Stanev, E.V., Pein, J., Staneva, J., and Chen, W.: Secondary circulation in shallow ocean straits: Observations and numerical modelling of the Danish Straits, Ocean Model., 148, 101585, https://doi.org/10.1016/j.ocemod.2020.101585, 2020.
Harrigan, S., Zsoter, E., Alfieri, L., Prudhomme, C., Salamon, P., Wetterhall, F., Barnard, C., Cloke, H., and Pappenberger, F.: GloFAS-ERA5 operational global river discharge reanalysis 1979–present, Earth Syst. Sci. Data, 12, 2043–2060, https://doi.org/10.5194/essd-12-2043-2020, 2020.
Heimbach, P., O'Donncha, F., Smith, T., Garcia-Valdecasas, J. M., Arnaud, A., and Wan, L.: Crafting the Future: Machine Learning for Ocean Forecasting, in: Ocean prediction: present status and state of the art (OPSR), edited by: Álvarez Fanjul, E., Ciliberti, S. A., Pearlman, J., Wilmer-Becker, K., and Behera, S., Copernicus Publications, State Planet, 5-opsr, 22, https://doi.org/10.5194/sp-5-opsr-22-2025, 2025.
Herzfeld, M.: Methods for freshwater riverine input into regional ocean models, Ocean Model., 90, 1–15, https://doi.org/10.1016/j.ocemod.2015.04.001, 2015.
Herzfeld, M. and Rizwi, F.: A two-way nesting framework for ocean models, Environ. Modell. Softw., 117, 200–213,, https://doi.org/10.1016/j.envsoft.2019.03.015, 2019.
Herzfeld, M., Engwirda, D., and Rizwi, F.: A coastal unstructured model using Voronoi meshes and C-grid staggering, Ocean Model., 148, 101599, https://doi.org/10.1016/j.ocemod.2020.101599, 2020.
Holt, J. T., Allen, J. I., Proctor, R., and Gilbert, F.: Error quantification of a high resolution coupled hydrodynamic-ecosystem coastal-ocean model – Part 1: Model overview and assessment of the hydrodynamics, J. Marine Sys., 57, 167–188, https://doi.org/10.1016/j.jmarsys.2005.04.008 , 2005.
Ilicak, M., Federico, I., Barletta, I., Mutlu, S., Karan, H., Ciliberti, S. A., Clementi, E., Coppini, G., and Pinardi, N.: Modeling of the Turkish Strait System Using a High Resolution Unstructured Grid Ocean Circulation Model, Journal of Marine Science and Engineering, 9, 769, https://doi.org/10.3390/jmse9070769, 2021.
Jeon, C.-H., Buijsman, M. C., Wallcraft, A. J., Shriver, J. F., Arbic, B. K., Richman, J. G., and Hogan, P. J.: Improving surface tidal accuracy through two-way nesting in a global ocean model, Ocean Model., 137, 98–113, https://doi.org/10.1016/j.ocemod.2019.03.007, 2019.
Kourafalou, V. H., De Mey, P., Le Henaff, M., Charria, G., Edwards, C. A., He, R., Herzfeld, M., Pascual, A., Stanev, E.V., Tintore', J., Usui, N., van der Westhuysen, A. J., Wilkin, J., and Zhu, X.: Coastal Ocean Forecasting: system integration and evaluation, J. Oper. Oceanogr., 8, S127–S146, https://doi.org/10.1080/1755876X.2015.1022336, 2015a.
Kourafalou, V. H., De Mey, P., Staneva, J., Ayoub, N., Barth, A., Chao, Y., Cirano, M., Fiechter, J., Herzfeld, M., Kurapov, A., Moore, A. M., Oddo, P., Pullen, J., van der Westhuysen, A. J., and Weisberg, R. H.:. Coastal ocean forecasting: science foundation and user benefits, J. Oper. Oceanogr., 8, 147–167, https://doi.org/10.1080/1755876X.2015.1022348, 2015b.
Kurihara, Y., Murakami, H., and Kachi, M.: Sea surface temperature from the new Japanese geostationary meteorological Himawari-8 satellite, Geophys. Res. Lett., 43, 1234–1240, https://doi.org/10.1002/2015GL067159, 2016.
Lorente, P., Piedracoba, S., Sotillo, M. G., Aznar, R., Amo-Baladrón, A., Pascual, A., Soto-Navarro, J., and Alvarez-Fanjul, E.: Characterizing the surface circulation in Ebro Delta (NW Mediterranean) with HF radar and modeled current data, J. Marine Syst., 163, 61–79, https://doi.org/10.1016/j.jmarsys.2016.07.001, 2016.
Luettich Jr., R. A., Westerink, J. J., and Scheffner, N. W.: ADCIRC: an Advanced Three Dimensional Circulation Model for Shelves, Coasts and Estuaries. Report 1: Theory and methodology of ADCIRC-2DDI and ADCIRC-3DL, Dredging Research Program Technical Report DRP-92-6, U.S. Army Engineers Waterways Experiment Station, Vicksburg, MS, 137 pp., http://hdl.handle.net/11681/4618 (last access: 20 May 2025), 1992.
Madec, G. and the NEMO System Team: NEMO ocean engine, Institut Pierre-Simon Laplace (IPSL), France, Zenodo [data set], https://doi.org/10.5281/zenodo.1464816, 2016.
Madec, G. and the NEMO System Team: NEMO Ocean Engine Reference Manual, Zenodo, https://doi.org/10.5281/zenodo.6334656, 2024.
Maicu, F., De Pascalis, F., Ferrarin, C., and Umgiesser, G.: Hydrodynamics of the Po River-Delta-Sea system, J. Geophys. Res.-Oceans, 123, 6349–6372, https://doi.org/10.1029/2017JC013601, 2018.
Mani, S., Calzada, J. R., Moghimi, S., Melton, C., and Signell, R. P.: OCSMesh: a data-driven automated unstructured mesh generation software for coastal ocean modeling, NOAA Technical Memorandum NOS CS, 47. U.S. Department of Commerce, https://repository.library.noaa.gov/view/noaa/33879 (last access: 16 May 2025), 2021.
Marchesiello, P., McWilliams, J. C., and Shchepetkin, A.:. Open boundary conditions for long-term integration of regional oceanic models, Ocean Model., 3, 1–20, https://doi.org/10.1016/S1463-5003(00)00013-5, 2001.
Marchesiello, P., Auclair, F., Debreu, L, McWilliams, J. C., Almar, R., Benshila, R., and Dumas, F.:. Tridimensional nonhydrostatic transient rip currents in a wave-resolving model, Ocean Model., 163, 101816, https://doi.org/10.1016/j.ocemod.2021.101816, 2021.
Marshall, J., Adcroft, A., Hill, C., Perelman, L., and Heisey, C.: A finite volume, incompressible Navier–Stokes model for studies of the ocean on parallel computers, J. Geophys. Res., 102, 5753–5766, https://doi.org/10.1029/96JC02775, 1997.
Matte, P., Wilkin, J., and Staneva, J.: The Representation of Rivers in Operational Ocean Forecasting Systems: A Review, in: Ocean prediction: present status and state of the art (OPSR), edited by: Álvarez Fanjul, E., Ciliberti, S. A., Pearlman, J., Wilmer-Becker, K., and Behera, S., Copernicus Publications, State Planet, 5-opsr, 19, https://doi.org/10.5194/sp-5-opsr-19-2025, 2025.
McWilliams, J. C.: Submesoscale Currents in the Ocean, P. Roy. Soc. A-Math. Phy., 472, 20160117, https://doi.org/10.1098/rspa.2016.0117, 2016.
Melet, A., Teatini, P., Le Cozannet, G., Jamet, C., Conversi, A., Benveniste, J., and Almar, R.:. Earth Observations for Monitoring Marine Coastal Hazards and Their Drivers, Surv. Geophys., 41, 1489–1534, https://doi.org/10.1007/s10712-020-09594-5, 2020.
Mellor, G. L.:. Users guide for a three-dimensional, primitive equation, numerical ocean model, Princeton University, Program in Atmospheric and Oceanic Sciences, http://jes.apl.washington.edu/modsims_two/usersguide0604.pdf (last access: 16 May 2025), 2004.
Moore, A. M., Martin, M. J., Akella, S., Arango, H. G., Balmaseda, M., Bertino, L., Ciavatta, S., Cournuelle, B., Cummings, J., Frolov, S., Lermusiaux, P., Oddo, P., Oke, P. R., Storto, A., Teruzzi, A., Vidard, A., Weaver, A. T., and GODAE OceanView Data Assimilation Task Team: Synthesis of ocean observations using data assimilation: toward a more complete picture of the State of the Ocean, Front. Mar. Sci. 6, 90, https://doi.org/10.3389/fmars.2019.00090, 2019.
Munk, W. and Wunsch, C.:. Abyssal recipes II, Energetics of tidal and wind mixing, Deep-Sea Res. Pt. I, 45, 1977–2010, https://doi.org/10.1016/S0967-0637(98)00070-3, 1998.
Nguyen, T. T., Staneva, J., Grayek, S., Bonaduce, A., Hagemann, S., Pham, N. T., Kumar, R., and Rakovec, O.: Impacts of extreme river discharge on coastal dynamics and environment: Insights from high-resolution modeling in the German Bight, Regional Studies in Marine Science, 66, 103476, https://doi.org/10.1016/j.rsma.2024.103476, 2024.
Oke, P. R. and Sakov, P.: Assessing the footprint of a regional ocean observing system, J. Marine Syst., 105, 30–51, https://doi.org/10.1016/j.jmarsys.2012.05.009, 2012.
Oke, P. R., Allen, J. S., Miller, R. N., Egbert, G. D., and Kosro, P. M.: Assimilation of surface velocity data into a primitive equation coastal ocean model, J. Geophys. Res., 107, 3122, https://doi.org/10.1029/2000JC000511, 2002.
Oliveira, A., Fortunato, A. B., Rogeiro, J., Teixeira, J., Azevedo, A., Lavaud, L., Bertin, X., Gomes, J., David, M., Pina, J., Rodrigues, M., and Lopes, P.:. OPENCoastS: An open-access service for the automatic generation of coastal forecast systems, Environ. Modell. Softw., 124, 104585, https://doi.org/10.1016/j.envsoft.2019.104585, 2019.
Oliveira, A. Fortunato, A. B., Rodrigues, M., Azevedo, A., Rogeiro, J., Bernardo, S., Lavaud, L., Bertin, X., Nahon, A., Jesus, G., Rocha, M., and Lopes, P.: Forecasting contrasting coastal and estuarine hydrodynamics with OPENCoastS, Environ. Modell. Softw., 143, 105132, https://doi.org/10.1016/j.envsoft.2021.105132, 2021.
Pein, J. U., Grayek, S., Schulz-Stelleneth, J., and Stanev, E. V.: On the impact of salinity observations on state estimates in Ems Estuary, Ocean Dynam., 66, 243–262, https://doi.org/10.1007/s10236-015-0920-0, 2016.
Pein, J., Staneva, J., Daewel, U., and Schrum, C.: Channel curvature improves water quality and nutrient filtering in an artificially deepened mesotidal idealized estuary, Cont. Shelf Res., 231, 104582, https://doi.org/10.1016/j.csr.2021.104582, 2021.
Penny, A. B., Alaka, L., Taylor, A. A., Booth, W., DeMaria, M., Fritx, C., and Rhome, J.: Operational Storm Surge Forecasting at the National Hurricane Center: The Case for Probabilistic Guidance and the Evaluation of Improved Storm Size Forecasts Used to Define the Wind Forcing, Weather Forecast., 38, 2461–2479, https://doi.org/10.1175/WAF-D-22-0209.1, 2023.
Perezhogin, P., Zanna, L., and Fernandez-Granda, C.: Generative Data-Driven Approaches for Stochastic Subgrid Parameterizations in an Idealized Ocean Model, J. Adv. Model. Earth Sy., 15, e2023MS003681, https://doi.org/10.1029/2023MS003681, 2023.
Petton, S., Garnier, V., Caillaud, M., Debreu, L., and Dumas, F.: Using the two-way nesting technique AGRIF with MARS3D V11.2 to improve hydrodynamics and estimate environmental indicators, Geosci. Model Dev., 16, 1191–1211, https://doi.org/10.5194/gmd-16-1191-2023, 2023.
Ponte, R. M., Carson, M., Cirano, M., Domingues, C. M., Jevrejeva, S., Marcos, M., Mitchum, G., van de Wal, R. S. W., Woodworth, P. L., Ablain, M., Ardhuin, F., Ballu, V., Becker, M., Benveniste, J., Birol, F., Bradshaw, E., Cazenave, A., De Mey-Frémaux, P., Durand, F., Ezer, T., Fu, L.-L., Fukumori, I., Gordon, K., Gravelle, M., Griffies, S. M., Han, W., Hibbert, A., Hughes, C. W., Idier, D., Kourafalou, V. H., Little, C. M., Matthews, A., Melet, A., Merrifield, M., Meyssignac, B., Minobe, S., Penduff, T., Picot, N., Piecuch, C., Ray, R. D., Rickards, L., Santamaría-Gómez, A., Stammer, D., Staneva, J., Testut, L., Thompson, K., Thompson, P., Vignudelli, S., Williams, J., Williams, S. D. P., Wöppelmann, G., Zanna, L., and Zhang, X.: Towards comprehensive observing and modelling systems for monitoring and predicting regional to coastal sea level, Front. Mar. Sci., 6, 437, https://doi.org/10.3389/fmars.2019.00437, 2019.
Rao, Y. R., Murthy, C. R., and Sinha, P. C.: The coastal ocean, in: Modelling and Monitoring of Coastal Marine Processes, edited by: Murthy, C. R., Sinha, P. C., and Rao, Y. R., Springer Nature, 3–10, https://doi.org/10.1007/978-1-4020-8327-3, 2008.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabaht: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019.
Reyes, E., Aguiar, E., Bendoni, M., Berta, M., Brandini, C., Cáceres-Euse, A., Capodici, F., Cardin, V., Cianelli, D., Ciraolo, G., Corgnati, L., Dadić, V., Doronzo, B., Drago, A., Dumas, D., Falco, P., Fattorini, M., Fernandes, M. J., Gauci, A., Gómez, R., Griffa, A., Guérin, C.-A., Hernández-Carrasco, I., Hernández-Lasheras, J., Ličer, M., Lorente, P., Magaldi, M. G., Mantovani, C., Mihanović, H., Molcard, A., Mourre, B., Révelard, A., Reyes-Suárez, C., Saviano, S., Sciascia, R., Taddei, S., Tintoré, J., Toledo, Y., Uttieri, M., Vilibić, I., Zambianchi, E., and Orfila, A.: Coastal high-frequency radars in the Mediterranean – Part 2: Applications in support of science priorities and societal needs, Ocean Sci., 18, 797–837, https://doi.org/10.5194/os-18-797-2022, 2022.
Ringler, T., Petersen, M., Higdon, R. L., Jacobsen, D., Jones, P. W., and Maltrud, M.: A multi-resolution approach to global ocean modeling, Ocean Model., 69, 211–232, https://doi.org/10.1016/j.ocemod.2013.04.010 , 2013.
Roberts, K. J., Pringle, W. J., and Westerink, J. J.: OceanMesh2D 1.0: MATLAB-based software for two-dimensional unstructured mesh generation in coastal ocean modeling, Geosci. Model Dev., 12, 1847–1868, https://doi.org/10.5194/gmd-12-1847-2019, 2019.
Rubio, A. J., Mader, L., Corgnati, C., Mantovani, A., Griffa, A., Novellino, C., Quentin, C., Wyatt, L. Schulz-Stellenfleth, J., Horstmann, J., Loremte, P., Zambianchi, E., Hartnett, M., Fernandes, C., Zervakis, V., Gorringe, P., Mellet, A., and Puillat, I.: HF radar activity in European coastal seas: next steps towards a pan-european hf radar network, Front. Mar. Sci., 4, 8, https://doi.org/10.3389/fmars.2017.00008, 2017.
Rudnick, D. L.: Ocean research enabled by underwater gliders, Annu. Rev. Mar. Sci., 8, 519–541, https://doi.org/10.1146/annurev-marine-122414-033913, 2016.
Schulz-Stellenfleth, J. and Stanev, E. V.: Analysis of the upscaling problem – a case study for the barotropic dynamics in the North Sea and the German Bight, Ocean Model., 100, 109–124, https://doi.org/10.1016/j.ocemod.2016.02.002, 2016.
Shchepetkin, A. and McWilliams, J. C.: The regional oceanic modelling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model, Ocean Model., 9, 347–404, https://doi.org/10.1016/j.ocemod.2004.08.002, 2005.
Shulman, I. and Paduan, J. D.: Assimilation of HF radar-derived radials and total currents in the Monterey Bay area, Deep-Sea Res. Pt. II, 56, 149–160, https://doi.org/10.1016/j.dsr2.2008.08.004, 2009.
Siddorn, J. R., Good, S. A., Harris, C. M., Lewis, H. W., Maksymczuk, J., Martin, M. J., and Saulter, A.: Research priorities in support of ocean monitoring and forecasting at the Met Office, Ocean Sci., 12, 217–231, https://doi.org/10.5194/os-12-217-2016, 2016.
Sobrinho, J., de Pablo, H., Campuzano, F., and Neves, R.: Coupling Rivers and Estuaries with an Ocean Model, Water, 13, 2284, https://doi.org/10.3390/w13162284, 2021.
Sotillo, M. G., Campuzano, F., Guihou, K., Lorente, P., Olmedo, E., Matulka, A., Santos, F., Amo-Baladrón, M. A., and Novellino, A.: River Freshwater Contribution in Operational Ocean Models along the European Atlantic Façade: Impact of a New River Discharge Forcing Data on the CMEMS IBI Regional Model Solution, Journal of Marine Science and Engineering, 9, 401, https://doi.org/10.3390/jmse9040401, 2021a.
Sotillo, M. G., Mourre, B., Mestres, M., Lorente, P., Aznar, R., García-León, M., Liste, M., Santana, A., Espino, M., and Álvarez-Fanjul, E.: Evaluation of the Operational CMEMS and Coastal Downstream Ocean Forecasting Services During the Storm Gloria (January 2020), Front. Mar. Sci. 8, 644525, https://doi.org/10.3389/fmars.2021.644525, 2021b.
Sotillo, M. G: Ocean Modelling in Support of Operational Ocean and Coastal Services, Journal of Marine Science and Engineering, 10, 1482, https://doi.org/10.3390/jmse10101482, 2022.
Stanev, E. V., Ziemer, F., Schulz Stellenfleth, J., Seemann, J., Staneva, J., and Gurgel, K. W.: Blending surface currents from HF radar observations and numerical modelling: tidal hindcasts and forecasts, J. Atmos. Ocean. Technol., 32, 256–281, https://doi.org/10.1175/JTECH-D-13-00164.1, 2015.
Stanev, E. V., Schulz-Stellenfleth, J., Staneva, J., Grayek, S., Grashorn, S., Behrens, A., Koch, W., and Pein, J.: Ocean forecasting for the German Bight: from regional to coastal scales, Ocean Sci., 12, 1105–1136, https://doi.org/10.5194/os-12-1105-2016, 2016a.
Stanev, E. V., Ziemer, F., Schulz-Stellenfleth, J., Seemann, J., Staneva, J., and Gurgel, K. W.:. Blending surface currents from HF radar observations and numerical modelling: tidal hindcasts and forecasts, J. Atmos. Ocean. Tech. 32, 256–281, 2016b.
Stanev, E. V., Grashorn, S., and Zhang, Y. J.: Cascading ocean basins: numerical simulations of the circulation and interbasin exchange in the Azov-Black-Marmara-Mediterranean Seas system, Ocean Dynam., 67, 1003–1025, https://doi.org/10.1007/s10236-017-1071-2, 2017.
Staneva, J., Wahle, K., Günther, H., and Stanev, E.: Coupling of wave and circulation models in coastal–ocean predicting systems: a case study for the German Bight, Ocean Sci., 12, 797–806, https://doi.org/10.5194/os-12-797-2016, 2016a.
Staneva, J., Wahle, K., Koch, W., Behrens, A., Fenoglio-Marc, L., and Stanev, E. V.: Coastal flooding: impact of waves on storm surge during extremes – a case study for the German Bight, Nat. Hazards Earth Syst. Sci., 16, 2373–2389, https://doi.org/10.5194/nhess-16-2373-2016, 2016b.
Staneva, J., Alari, V., Breivik, O., Bidlot, J.-R., and Mogensen, K.: Effects of wave-induced forcing on a circulation model of the North Sea, Ocean Dynam., 67, 81–101, https://doi.org/10.1007/s10236-016-1009-0, 2017.
Sun, Q., Whitney, M. M., Bryan, F. O., and Tseng, Y.: A box model for representing estuarine physical processes in Earth system models, Ocean Model., 112, 139–153, https://doi.org/10.1016/j.ocemod.2017.03.004, 2017.
Tonani, M., Sykes, P., King, R. R., McConnell, N., Péquignet, A.-C., O'Dea, E., Graham, J. A., Polton, J., and Siddorn, J.: The impact of a new high-resolution ocean model on the Met Office North-West European Shelf forecasting system, Ocean Sci., 15, 1133–1158, https://doi.org/10.5194/os-15-1133-2019, 2019.
Trotta, F., Pinardi, N., Fenu, E., Grandi, A., and Lyubartsev, V.: Multinest high-resolution model of submesoscale circulation features in the Gulf of Taranto, Ocean Dynam., 67, 1609–1625, https://doi.org/10.1007/s10236-017-1110-z, 2017.
Umgiesser, G., Canu, D. M., Cucco, A., and Solidoro, C.: A finite element model for the Venice Lagoon. Development, set up, calibration and validation, J. Marine Syst., 51, 123–145, https://doi.org/10.1016/j.jmarsys.2004.05.009, 2004.
Verri, G., Pinardi, N., Bryan, F., Tseng, Y., Coppini, G., and Clementi, E.: Box model approaches to estuarine dynamics in coarse-resolution ocean models, Ocean Model., 148, 101587, https://doi.org/10.1016/j.ocemod.2020.101587, 2020.
Wahle, K., Staneva, J., Koch, W., Fenoglio-Marc, L., Ho-Hagemann, H. T. M., and Stanev, E. V.: An atmosphere–wave regional coupled model: improving predictions of wave heights and surface winds in the southern North Sea, Ocean Sci., 13, 289–301, https://doi.org/10.5194/os-13-289-2017, 2017.
Wang, C., Pritchard, M. S., Brenowitz, N., Cohen, Y., Bonev, B., Kurth, T., Durran, D., and Pathak, K.: Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model. Atmospheric and Ocean Physics, arXiv [preprint], https://doi.org/10.48550/arXiv.2406.08632, 2024.
Warner, J. C., Sherwood, C. R., Signell, R. P., Harris, C., and Arango, H. G.: Development of a three-dimensional, regional, coupled wave, current, and sediment-transport model, Comput. Geosci., 34, 1284–1306, https://doi.org/10.1016/j.cageo.2008.02.012, 2008.
Warner, J. C., Armstrong, B., He, R., and Zambon, J. B.: Development of a Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modelling system, Ocean Model., 35, 230–244, https://doi.org/10.1016/j.ocemod.2010.07.010, 2010.
Westerink, J. J., Luettich, R. A., Blain, C. A., and Scheffner, N. W.: ADCIRC: An Advanced Three-Dimensional Circulation Model for Shelves, Coasts and Estuaries. Report 2: Users' Manual for ADCIRC-2DDI, Department of the Army US Army Corps of Engineers, Washington, D.C, https://apps.dtic.mil/sti/citations/ADA276150 (last access: 25 July 2024), 1994.
Wilkin, J. L., Arango, H. G., Haidvogel, D. B., Lichtenwalner, C. S., Glenn, S. M., and Hedström, K. S.: A regional ocean modelling system for the Long-term Ecosystem Observatory, J. Geophys. Res.-Oceans, 110, C06S91, https://doi.org/10.1029/2003JC002218, 2005.
Xie W., Xu, G., Zhang, H., and Dong, C.: Developing a deep learning-based storm surge forecasting model, Ocean Model., 182, 102179, https://doi.org/10.1016/j.ocemod.2023.102179, 2023.
Yuan, B., Jacob, B., Chen, W., and Staneva, J.: Downscaling sea surface height and currents in coastal regions using convolutional neural network. Appl. Ocean Res., 151, 104153, https://doi.org/10.1016/j.apor.2024.104153, 2024.
Zanna, L. and Bolton, T.: Data-Driven Equation Discovrey of Ocean Mesoscale Closures, Geophys. Res. Lett., 47, e2020GL088376, https://doi.org/10.1029/2020GL088376, 2020.
Zeng, X., Atlas, R., Birk, R. J., Carr, F. H., Carrier, M. J., Cucurull, L., Hooke, W. H., Kalnay, E., Murtugudde, R., Posselt, D. J., Russell, J. L., Tyndall, D. P., Weller, R. A., and Zhang, F.: Title: Use of observing system simulation experiments in the United States, B. Am. Meteorol. Soc., 101, E1427–E1438, https://doi.org/10.1175/BAMS-D-19-0155.1, 2020.
Zhang, Y. and Baptista, A. M.: SELFE: a semi-implicit Eulerian–Lagrangian finite-element model for cross-scale ocean circulation, Ocean Model., 21, 71-96, https://doi.org/10.1016/j.ocemod.2007.11.005, 2008.
Zhang, Y. J., Stanev, E., and Grashorn, S.: Unstructured-grid model for the North Sea and Baltic Sea: validation against observations, Ocean Model., 97, 91–108, https://doi.org/10.1016/j.ocemod.2015.11.009, 2016a.
Zhang, Y. J., Ye, F., Stanev, E. V., and Grashorn, S.: Seamless cross-scale modelling with SCHISM, Ocean Model., 102, 64–81, https://doi.org/10.1016/j.ocemod.2016.05.002, 2016b.
Short summary
Coastal services are essential to society, requiring accurate prediction of ocean variables in complex, high-resolution environments. This paper outlines key aspects of coastal modelling and emphasizes the importance of capturing nonlinear interactions and feedbacks. Advances in coastal modelling, observational integration, and predictive skills are highlighted as being vital for supporting sustainability and strengthening climate resilience.
Coastal services are essential to society, requiring accurate prediction of ocean variables in...
Altmetrics
Final-revised paper
Preprint