Articles | Volume 5-opsr
https://doi.org/10.5194/sp-5-opsr-21-2025
© 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-21-2025
© 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 8.2
| 02 Jun 2025 | OPSR | Chapter 8.2
Improving accuracy and providing uncertainty estimations: ensemble methodologies for ocean forecasting
Ibrahim Hoteit
CORRESPONDING AUTHOR
Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Eric Chassignet
Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, Florida, United States
Mike Bell
MetOffice, Exeter, UK
Related authors
Andreas Schiller, Simon A. Josey, John Siddorn, and Ibrahim Hoteit
State Planet, 5-opsr, 18, https://doi.org/10.5194/sp-5-opsr-18-2025, https://doi.org/10.5194/sp-5-opsr-18-2025, 2025
Short summary
Short summary
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.
Matthew J. Martin, Ibrahim Hoteit, Laurent Bertino, and Andrew M. Moore
State Planet, 5-opsr, 9, https://doi.org/10.5194/sp-5-opsr-9-2025, https://doi.org/10.5194/sp-5-opsr-9-2025, 2025
Short summary
Short summary
Observations of the ocean from satellites and platforms in the ocean are combined with information from computer models to produce predictions of how the ocean temperature, salinity, and currents will evolve over the coming days and weeks and to describe how the ocean has evolved in the past. This paper summarises the methods used to produce these ocean forecasts at various centres around the world and outlines the practical considerations for implementing such forecasting systems.
Ségolène Berthou, John Siddorn, Vivian Fraser-Leonhardt, Pierre-Yves Le Traon, and Ibrahim Hoteit
State Planet, 5-opsr, 20, https://doi.org/10.5194/sp-5-opsr-20-2025, https://doi.org/10.5194/sp-5-opsr-20-2025, 2025
Short summary
Short summary
Ocean forecasting is traditionally done independently from atmospheric, wave, or river modelling. We discuss the benefits and challenges of bringing all these modelling systems together for ocean forecasting.
Manal Hamdeno, Aida Alvera-Azcárate, George Krokos, and Ibrahim Hoteit
Ocean Sci., 20, 1087–1107, https://doi.org/10.5194/os-20-1087-2024, https://doi.org/10.5194/os-20-1087-2024, 2024
Short summary
Short summary
Our study focuses on the characteristics of MHWs in the Red Sea during the last 4 decades. Using satellite-derived sea surface temperatures (SSTs), we found a clear warming trend in the Red Sea since 1994, which has intensified significantly since 2016. This SST rise was associated with an increase in the frequency and days of MHWs. In addition, a correlation was found between the frequency of MHWs and some climate modes, which was more pronounced in some years of the study period.
Yasser O. Abualnaja, Alexandra Pavlidou, James H. Churchill, Ioannis Hatzianestis, Dimitris Velaoras, Harilaos Kontoyiannis, Vassilis P. Papadopoulos, Aristomenis P. Karageorgis, Georgia Assimakopoulou, Helen Kaberi, Theodoros Kannelopoulos, Constantine Parinos, Christina Zeri, Dionysios Ballas, Elli Pitta, Vassiliki Paraskevopoulou, Afroditi Androni, Styliani Chourdaki, Vassileia Fioraki, Stylianos Iliakis, Georgia Kabouri, Angeliki Konstantinopoulou, Georgios Krokos, Dimitra Papageorgiou, Alkiviadis Papageorgiou, Georgios Pappas, Elvira Plakidi, Eleni Rousselaki, Ioanna Stavrakaki, Eleni Tzempelikou, Panagiota Zachioti, Anthi Yfanti, Theodore Zoulias, Abdulah Al Amoudi, Yasser Alshehri, Ahmad Alharbi, Hammad Al Sulami, Taha Boksmati, Rayan Mutwalli, and Ibrahim Hoteit
Earth Syst. Sci. Data, 16, 1703–1731, https://doi.org/10.5194/essd-16-1703-2024, https://doi.org/10.5194/essd-16-1703-2024, 2024
Short summary
Short summary
We present oceanographic measurements obtained during two surveillance cruises conducted in June and September 2021 in the Red Sea and the Arabian Gulf. It is the first multidisciplinary survey within the Saudi Arabian coastal zone, extending from near the Saudi–Jordanian border in the north of the Red Sea to the south close to the Saudi--Yemen border and in the Arabian Gulf. The objective was to record the pollution status along the coastal zone of the kingdom related to specific pressures.
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
Rui Sun, Alison Cobb, Ana B. Villas Bôas, Sabique Langodan, Aneesh C. Subramanian, Matthew R. Mazloff, Bruce D. Cornuelle, Arthur J. Miller, Raju Pathak, and Ibrahim Hoteit
Geosci. Model Dev., 16, 3435–3458, https://doi.org/10.5194/gmd-16-3435-2023, https://doi.org/10.5194/gmd-16-3435-2023, 2023
Short summary
Short summary
In this work, we integrated the WAVEWATCH III model into the regional coupled model SKRIPS. We then performed a case study using the newly implemented model to study Tropical Cyclone Mekunu, which occurred in the Arabian Sea. We found that the coupled model better simulates the cyclone than the uncoupled model, but the impact of waves on the cyclone is not significant. However, the waves change the sea surface temperature and mixed layer, especially in the cold waves produced due to the cyclone.
M. G. Ziliani, M. U. Altaf, B. Aragon, R. Houborg, T. E. Franz, Y. Lu, J. Sheffield, I. Hoteit, and M. F. McCabe
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1045–1052, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1045-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1045-2022, 2022
Oliver Miguel López Valencia, Kasper Johansen, Bruno José Luis Aragón Solorio, Ting Li, Rasmus Houborg, Yoann Malbeteau, Samer AlMashharawi, Muhammad Umer Altaf, Essam Mohammed Fallatah, Hari Prasad Dasari, Ibrahim Hoteit, and Matthew Francis McCabe
Hydrol. Earth Syst. Sci., 24, 5251–5277, https://doi.org/10.5194/hess-24-5251-2020, https://doi.org/10.5194/hess-24-5251-2020, 2020
Short summary
Short summary
The agricultural sector in Saudi Arabia has expanded rapidly over the last few decades, supported by non-renewable groundwater abstraction. This study describes a novel data–model fusion approach to compile national-scale groundwater abstractions and demonstrates its use over 5000 individual center-pivot fields. This method will allow both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales.
Rui Sun, Aneesh C. Subramanian, Arthur J. Miller, Matthew R. Mazloff, Ibrahim Hoteit, and Bruce D. Cornuelle
Geosci. Model Dev., 12, 4221–4244, https://doi.org/10.5194/gmd-12-4221-2019, https://doi.org/10.5194/gmd-12-4221-2019, 2019
Short summary
Short summary
A new regional coupled ocean–atmosphere model, SKRIPS, is developed and presented. The oceanic component is the MITgcm and the atmospheric component is the WRF model. The coupler is implemented using ESMF according to NUOPC protocols. SKRIPS is demonstrated by simulating a series of extreme heat events occurring in the Red Sea region. We show that SKRIPS is capable of performing coupled ocean–atmosphere simulations. In addition, the scalability test shows SKRIPS is computationally efficient.
Hugo Cruz-Jiménez, Guotu Li, Paul Martin Mai, Ibrahim Hoteit, and Omar M. Knio
Geosci. Model Dev., 11, 3071–3088, https://doi.org/10.5194/gmd-11-3071-2018, https://doi.org/10.5194/gmd-11-3071-2018, 2018
Short summary
Short summary
One of the most important challenges seismologists and earthquake engineers face is reliably estimating ground motion in an area prone to large damaging earthquakes. This study aimed at better understanding the relationship between characteristics of geological faults (e.g., hypocenter location, rupture size/location, etc.) and resulting ground motion, via statistical analysis of a rupture simulation model. This study provides important insight on ground-motion responses to geological faults.
Khan Zaib Jadoon, Muhammad Umer Altaf, Matthew Francis McCabe, Ibrahim Hoteit, Nisar Muhammad, Davood Moghadas, and Lutz Weihermüller
Hydrol. Earth Syst. Sci., 21, 5375–5383, https://doi.org/10.5194/hess-21-5375-2017, https://doi.org/10.5194/hess-21-5375-2017, 2017
Short summary
Short summary
In this study electromagnetic induction (EMI) measurements were used to estimate soil salinity in an agriculture field irrigated with a drip irrigation system. Electromagnetic model parameters and uncertainty were estimated using adaptive Bayesian Markov chain Monte Carlo (MCMC). Application of the MCMC-based inversion to the synthetic and field measurements demonstrates that the parameters of the model can be well estimated for the saline soil as compared to the non-saline soil.
Mohamad E. Gharamti, Johan Valstar, Gijs Janssen, Annemieke Marsman, and Ibrahim Hoteit
Hydrol. Earth Syst. Sci., 20, 4561–4583, https://doi.org/10.5194/hess-20-4561-2016, https://doi.org/10.5194/hess-20-4561-2016, 2016
Short summary
Short summary
The paper addresses the issue of sampling errors when using the ensemble Kalman filter, in particular its hybrid and second-order formulations. The presented work is aimed at estimating concentration and biodegradation rates of subsurface contaminants at the port of Rotterdam in the Netherlands. Overall, we found that accounting for both forecast and observation sampling errors in the joint data assimilation system helps recover more accurate state and parameter estimates.
Boujemaa Ait-El-Fquih, Mohamad El Gharamti, and Ibrahim Hoteit
Hydrol. Earth Syst. Sci., 20, 3289–3307, https://doi.org/10.5194/hess-20-3289-2016, https://doi.org/10.5194/hess-20-3289-2016, 2016
Short summary
Short summary
We derive a new dual ensemble Kalman filter (EnKF) for state-parameter estimation. The derivation is based on the one-step-ahead smoothing formulation, and unlike the standard dual EnKF, it is consistent with the Bayesian formulation of the state-parameter estimation problem and uses the observations in both state smoothing and forecast. This is shown to enhance the performance and robustness of the dual EnKF in experiments conducted with a two-dimensional synthetic groundwater aquifer model.
Andreas Schiller, Simon A. Josey, John Siddorn, and Ibrahim Hoteit
State Planet, 5-opsr, 18, https://doi.org/10.5194/sp-5-opsr-18-2025, https://doi.org/10.5194/sp-5-opsr-18-2025, 2025
Short summary
Short summary
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.
Matthew J. Martin, Ibrahim Hoteit, Laurent Bertino, and Andrew M. Moore
State Planet, 5-opsr, 9, https://doi.org/10.5194/sp-5-opsr-9-2025, https://doi.org/10.5194/sp-5-opsr-9-2025, 2025
Short summary
Short summary
Observations of the ocean from satellites and platforms in the ocean are combined with information from computer models to produce predictions of how the ocean temperature, salinity, and currents will evolve over the coming days and weeks and to describe how the ocean has evolved in the past. This paper summarises the methods used to produce these ocean forecasts at various centres around the world and outlines the practical considerations for implementing such forecasting systems.
Liying Wan, Marcos Garcia Sotillo, Mike Bell, Yann Drillet, Roland Aznar, and Stefania Ciliberti
State Planet, 5-opsr, 15, https://doi.org/10.5194/sp-5-opsr-15-2025, https://doi.org/10.5194/sp-5-opsr-15-2025, 2025
Short summary
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.
Ségolène Berthou, John Siddorn, Vivian Fraser-Leonhardt, Pierre-Yves Le Traon, and Ibrahim Hoteit
State Planet, 5-opsr, 20, https://doi.org/10.5194/sp-5-opsr-20-2025, https://doi.org/10.5194/sp-5-opsr-20-2025, 2025
Short summary
Short summary
Ocean forecasting is traditionally done independently from atmospheric, wave, or river modelling. We discuss the benefits and challenges of bringing all these modelling systems together for ocean forecasting.
Marina Tonani, Eric Chassignet, Mauro Cirano, Yasumasa Miyazawa, and Begoña Pérez Gómez
State Planet, 5-opsr, 3, https://doi.org/10.5194/sp-5-opsr-3-2025, https://doi.org/10.5194/sp-5-opsr-3-2025, 2025
Short summary
Short summary
This article provides an overview of the main characteristics of ocean forecast systems covering a limited region of the ocean. Their main components are described, as well as the spatial and temporal scales they resolve. The oceanic variables that these systems are able to predict are also explained. An overview of the main forecasting systems currently in operation is also provided.
Yann Drillet, Matthew Martin, Yosuke Fujii, Eric Chassignet, and Stefania Ciliberti
State Planet, 5-opsr, 2, https://doi.org/10.5194/sp-5-opsr-2-2025, https://doi.org/10.5194/sp-5-opsr-2-2025, 2025
Short summary
Short summary
This article describes the various stages of research and development that have been carried out over the last few decades to produce an operational reference service for global ocean monitoring and forecasting.
Michael J. Bell, Andreas Schiller, and Stefania Ciliberti
State Planet, 5-opsr, 10, https://doi.org/10.5194/sp-5-opsr-10-2025, https://doi.org/10.5194/sp-5-opsr-10-2025, 2025
Short summary
Short summary
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.
David Storkey, Pierre Mathiot, Michael J. Bell, Dan Copsey, Catherine Guiavarc'h, Helene T. Hewitt, Jeff Ridley, and Malcolm J. Roberts
Geosci. Model Dev., 18, 2725–2745, https://doi.org/10.5194/gmd-18-2725-2025, https://doi.org/10.5194/gmd-18-2725-2025, 2025
Short summary
Short summary
The Southern Ocean is a key region of the world ocean in the context of climate change studies. We show that the Met Office Hadley Centre coupled model with intermediate ocean resolution struggles to accurately simulate the Southern Ocean. Increasing the frictional drag that the seafloor exerts on ocean currents and introducing a representation of unresolved ocean eddies both appear to reduce the large-scale biases in this model.
Olmo Zavala-Romero, Alexandra Bozec, Eric P. Chassignet, and Jose R. Miranda
Ocean Sci., 21, 113–132, https://doi.org/10.5194/os-21-113-2025, https://doi.org/10.5194/os-21-113-2025, 2025
Short summary
Short summary
This study shows AI can speed up data assimilation in ocean models. Researchers used convolutional neural networks (CNNs) to assimilate sea surface temperature and height observations in the Gulf of Mexico, learning to replicate corrections made by traditional, computationally expensive methods. CNN design and training window size significantly impacted accuracy, but the percentage of ocean pixels did not. These findings suggest CNNs may accelerate data assimilation in realistic settings.
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
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
Short summary
Short summary
The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Davi Mignac, Jennifer Waters, Daniel J. Lea, Matthew J. Martin, James While, Anthony T. Weaver, Arthur Vidard, Catherine Guiavarc’h, Dave Storkey, David Ford, Edward W. Blockley, Jonathan Baker, Keith Haines, Martin R. Price, Michael J. Bell, and Richard Renshaw
EGUsphere, https://doi.org/10.5194/egusphere-2024-3143, https://doi.org/10.5194/egusphere-2024-3143, 2024
Short summary
Short summary
We describe major improvements of the Met Office's global ocean-sea ice forecasting system. The models and the way observations are used to improve the forecasts were changed, which led to a significant error reduction of 1-day forecasts. The new system performance in past conditions, where sub-surface observations are scarce, was improved with more consistent ocean heat content estimates. The new system will be of better use for climate studies and will provide improved forecasts for end users.
Manal Hamdeno, Aida Alvera-Azcárate, George Krokos, and Ibrahim Hoteit
Ocean Sci., 20, 1087–1107, https://doi.org/10.5194/os-20-1087-2024, https://doi.org/10.5194/os-20-1087-2024, 2024
Short summary
Short summary
Our study focuses on the characteristics of MHWs in the Red Sea during the last 4 decades. Using satellite-derived sea surface temperatures (SSTs), we found a clear warming trend in the Red Sea since 1994, which has intensified significantly since 2016. This SST rise was associated with an increase in the frequency and days of MHWs. In addition, a correlation was found between the frequency of MHWs and some climate modes, which was more pronounced in some years of the study period.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-1737, https://doi.org/10.5194/egusphere-2024-1737, 2024
Short summary
Short summary
In this paper we review marine data assimilation (MDA) in the UK, its stakeholders, needs, past and present developments in different areas of UK MDA, and offer a vision for their longer future. The specific areas covered are ocean physics and sea ice, marine biogeochemistry, coupled MDA, MDA informing observing network design and MDA theory. We also discuss future vision for MDA resources: observations, software, hardware and people skills.
Yasser O. Abualnaja, Alexandra Pavlidou, James H. Churchill, Ioannis Hatzianestis, Dimitris Velaoras, Harilaos Kontoyiannis, Vassilis P. Papadopoulos, Aristomenis P. Karageorgis, Georgia Assimakopoulou, Helen Kaberi, Theodoros Kannelopoulos, Constantine Parinos, Christina Zeri, Dionysios Ballas, Elli Pitta, Vassiliki Paraskevopoulou, Afroditi Androni, Styliani Chourdaki, Vassileia Fioraki, Stylianos Iliakis, Georgia Kabouri, Angeliki Konstantinopoulou, Georgios Krokos, Dimitra Papageorgiou, Alkiviadis Papageorgiou, Georgios Pappas, Elvira Plakidi, Eleni Rousselaki, Ioanna Stavrakaki, Eleni Tzempelikou, Panagiota Zachioti, Anthi Yfanti, Theodore Zoulias, Abdulah Al Amoudi, Yasser Alshehri, Ahmad Alharbi, Hammad Al Sulami, Taha Boksmati, Rayan Mutwalli, and Ibrahim Hoteit
Earth Syst. Sci. Data, 16, 1703–1731, https://doi.org/10.5194/essd-16-1703-2024, https://doi.org/10.5194/essd-16-1703-2024, 2024
Short summary
Short summary
We present oceanographic measurements obtained during two surveillance cruises conducted in June and September 2021 in the Red Sea and the Arabian Gulf. It is the first multidisciplinary survey within the Saudi Arabian coastal zone, extending from near the Saudi–Jordanian border in the north of the Red Sea to the south close to the Saudi--Yemen border and in the Arabian Gulf. The objective was to record the pollution status along the coastal zone of the kingdom related to specific pressures.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
Short summary
Short summary
Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
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
Rui Sun, Alison Cobb, Ana B. Villas Bôas, Sabique Langodan, Aneesh C. Subramanian, Matthew R. Mazloff, Bruce D. Cornuelle, Arthur J. Miller, Raju Pathak, and Ibrahim Hoteit
Geosci. Model Dev., 16, 3435–3458, https://doi.org/10.5194/gmd-16-3435-2023, https://doi.org/10.5194/gmd-16-3435-2023, 2023
Short summary
Short summary
In this work, we integrated the WAVEWATCH III model into the regional coupled model SKRIPS. We then performed a case study using the newly implemented model to study Tropical Cyclone Mekunu, which occurred in the Arabian Sea. We found that the coupled model better simulates the cyclone than the uncoupled model, but the impact of waves on the cyclone is not significant. However, the waves change the sea surface temperature and mixed layer, especially in the cold waves produced due to the cyclone.
M. G. Ziliani, M. U. Altaf, B. Aragon, R. Houborg, T. E. Franz, Y. Lu, J. Sheffield, I. Hoteit, and M. F. McCabe
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1045–1052, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1045-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1045-2022, 2022
Oliver Miguel López Valencia, Kasper Johansen, Bruno José Luis Aragón Solorio, Ting Li, Rasmus Houborg, Yoann Malbeteau, Samer AlMashharawi, Muhammad Umer Altaf, Essam Mohammed Fallatah, Hari Prasad Dasari, Ibrahim Hoteit, and Matthew Francis McCabe
Hydrol. Earth Syst. Sci., 24, 5251–5277, https://doi.org/10.5194/hess-24-5251-2020, https://doi.org/10.5194/hess-24-5251-2020, 2020
Short summary
Short summary
The agricultural sector in Saudi Arabia has expanded rapidly over the last few decades, supported by non-renewable groundwater abstraction. This study describes a novel data–model fusion approach to compile national-scale groundwater abstractions and demonstrates its use over 5000 individual center-pivot fields. This method will allow both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales.
Eric P. Chassignet, Stephen G. Yeager, Baylor Fox-Kemper, Alexandra Bozec, Frederic Castruccio, Gokhan Danabasoglu, Christopher Horvat, Who M. Kim, Nikolay Koldunov, Yiwen Li, Pengfei Lin, Hailong Liu, Dmitry V. Sein, Dmitry Sidorenko, Qiang Wang, and Xiaobiao Xu
Geosci. Model Dev., 13, 4595–4637, https://doi.org/10.5194/gmd-13-4595-2020, https://doi.org/10.5194/gmd-13-4595-2020, 2020
Short summary
Short summary
This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea ice simulations to assess the robustness of climate-relevant improvements in ocean simulations associated with moving from coarse (∼1°) to eddy-resolving (∼0.1°) horizontal resolutions. Despite significant improvements, greatly enhanced horizontal resolution does not deliver unambiguous bias reduction in all regions for all models.
Hiroyuki Tsujino, L. Shogo Urakawa, Stephen M. Griffies, Gokhan Danabasoglu, Alistair J. Adcroft, Arthur E. Amaral, Thomas Arsouze, Mats Bentsen, Raffaele Bernardello, Claus W. Böning, Alexandra Bozec, Eric P. Chassignet, Sergey Danilov, Raphael Dussin, Eleftheria Exarchou, Pier Giuseppe Fogli, Baylor Fox-Kemper, Chuncheng Guo, Mehmet Ilicak, Doroteaciro Iovino, Who M. Kim, Nikolay Koldunov, Vladimir Lapin, Yiwen Li, Pengfei Lin, Keith Lindsay, Hailong Liu, Matthew C. Long, Yoshiki Komuro, Simon J. Marsland, Simona Masina, Aleksi Nummelin, Jan Klaus Rieck, Yohan Ruprich-Robert, Markus Scheinert, Valentina Sicardi, Dmitry Sidorenko, Tatsuo Suzuki, Hiroaki Tatebe, Qiang Wang, Stephen G. Yeager, and Zipeng Yu
Geosci. Model Dev., 13, 3643–3708, https://doi.org/10.5194/gmd-13-3643-2020, https://doi.org/10.5194/gmd-13-3643-2020, 2020
Short summary
Short summary
The OMIP-2 framework for global ocean–sea-ice model simulations is assessed by comparing multi-model means from 11 CMIP6-class global ocean–sea-ice models calculated separately for the OMIP-1 and OMIP-2 simulations. Many features are very similar between OMIP-1 and OMIP-2 simulations, and yet key improvements in transitioning from OMIP-1 to OMIP-2 are also identified. Thus, the present assessment justifies that future ocean–sea-ice model development and analysis studies use the OMIP-2 framework.
Taylor A. Shropshire, Steven L. Morey, Eric P. Chassignet, Alexandra Bozec, Victoria J. Coles, Michael R. Landry, Rasmus Swalethorp, Glenn Zapfe, and Michael R. Stukel
Biogeosciences, 17, 3385–3407, https://doi.org/10.5194/bg-17-3385-2020, https://doi.org/10.5194/bg-17-3385-2020, 2020
Short summary
Short summary
Zooplankton are the smallest animals in the ocean and important food for fish. Despite their importance, zooplankton have been relatively undersampled. To better understand the zooplankton community in the Gulf of Mexico (GoM), we developed a model to simulate their dynamics. We found that heterotrophic protists are important for supporting mesozooplankton, which are the primary prey of larval fish. The model developed in this study has the potential to improve fisheries management in the GoM.
Rui Sun, Aneesh C. Subramanian, Arthur J. Miller, Matthew R. Mazloff, Ibrahim Hoteit, and Bruce D. Cornuelle
Geosci. Model Dev., 12, 4221–4244, https://doi.org/10.5194/gmd-12-4221-2019, https://doi.org/10.5194/gmd-12-4221-2019, 2019
Short summary
Short summary
A new regional coupled ocean–atmosphere model, SKRIPS, is developed and presented. The oceanic component is the MITgcm and the atmospheric component is the WRF model. The coupler is implemented using ESMF according to NUOPC protocols. SKRIPS is demonstrated by simulating a series of extreme heat events occurring in the Red Sea region. We show that SKRIPS is capable of performing coupled ocean–atmosphere simulations. In addition, the scalability test shows SKRIPS is computationally efficient.
Hugo Cruz-Jiménez, Guotu Li, Paul Martin Mai, Ibrahim Hoteit, and Omar M. Knio
Geosci. Model Dev., 11, 3071–3088, https://doi.org/10.5194/gmd-11-3071-2018, https://doi.org/10.5194/gmd-11-3071-2018, 2018
Short summary
Short summary
One of the most important challenges seismologists and earthquake engineers face is reliably estimating ground motion in an area prone to large damaging earthquakes. This study aimed at better understanding the relationship between characteristics of geological faults (e.g., hypocenter location, rupture size/location, etc.) and resulting ground motion, via statistical analysis of a rupture simulation model. This study provides important insight on ground-motion responses to geological faults.
Khan Zaib Jadoon, Muhammad Umer Altaf, Matthew Francis McCabe, Ibrahim Hoteit, Nisar Muhammad, Davood Moghadas, and Lutz Weihermüller
Hydrol. Earth Syst. Sci., 21, 5375–5383, https://doi.org/10.5194/hess-21-5375-2017, https://doi.org/10.5194/hess-21-5375-2017, 2017
Short summary
Short summary
In this study electromagnetic induction (EMI) measurements were used to estimate soil salinity in an agriculture field irrigated with a drip irrigation system. Electromagnetic model parameters and uncertainty were estimated using adaptive Bayesian Markov chain Monte Carlo (MCMC). Application of the MCMC-based inversion to the synthetic and field measurements demonstrates that the parameters of the model can be well estimated for the saline soil as compared to the non-saline soil.
Mohamad E. Gharamti, Johan Valstar, Gijs Janssen, Annemieke Marsman, and Ibrahim Hoteit
Hydrol. Earth Syst. Sci., 20, 4561–4583, https://doi.org/10.5194/hess-20-4561-2016, https://doi.org/10.5194/hess-20-4561-2016, 2016
Short summary
Short summary
The paper addresses the issue of sampling errors when using the ensemble Kalman filter, in particular its hybrid and second-order formulations. The presented work is aimed at estimating concentration and biodegradation rates of subsurface contaminants at the port of Rotterdam in the Netherlands. Overall, we found that accounting for both forecast and observation sampling errors in the joint data assimilation system helps recover more accurate state and parameter estimates.
Boujemaa Ait-El-Fquih, Mohamad El Gharamti, and Ibrahim Hoteit
Hydrol. Earth Syst. Sci., 20, 3289–3307, https://doi.org/10.5194/hess-20-3289-2016, https://doi.org/10.5194/hess-20-3289-2016, 2016
Short summary
Short summary
We derive a new dual ensemble Kalman filter (EnKF) for state-parameter estimation. The derivation is based on the one-step-ahead smoothing formulation, and unlike the standard dual EnKF, it is consistent with the Bayesian formulation of the state-parameter estimation problem and uses the observations in both state smoothing and forecast. This is shown to enhance the performance and robustness of the dual EnKF in experiments conducted with a two-dimensional synthetic groundwater aquifer model.
Cited articles
Albarakati, S., Lima, R., Theußl, T., Hoteit, I., and Knio, O.: Multi-objective risk-aware path planning in uncertain transient currents: an ensemble-based stochastic optimization approach, IEEE J. Oceanic Eng., 46, 1082–1098, https://doi.org/10.1109/JOE.2021.3063196, 2021.
Barker, C. H., Kourafalou, V. H., Beegle-Krause, C. J., Boufadel, M., Bourassa, M. A., Buschang, S. G., Androulidakis, Y., Chassignet, E. P., Dagestad, K.-F., Danmeier, D. G., Dissanayake, A. L., Galt, J. A., Jacobs, G., Marcotte, G., Özgökmen, T., Pinardi, N., Schiller, R. V., Socolofsky, S. A., Thrift-Viveros, D., Zelenke, B., Zhang, A., and Zheng, Y.: Progress in operational modeling in support of oil spill response, J. Mar. Sci. Eng., 8, 668, https://doi.org/10.3390/jmse8090668, 2020.
Barton, N., Metzger, E. J., Reynolds, C. A., Ruston, B., Rowley, C., Smedstad, O. M., Ridout, J. A., Wallcraft, A., Frolov, S., Hogan, P., Janiga, M. A., Shriver, J. F., McLay, J., Thoppil, P., Huang, A., Crawford, W., Whitcomb, T., Bishop, C. H., Zamudio, L., and Phelps, M.: The Navy's Earth System Prediction Capability: A New Global Coupled Atmosphere-Ocean-Sea Ice Prediction System Designed for Daily to Subseasonal Forecasting, Earth Space Sci., 8, e2020EA001199, https://doi.org/10.1029/2020EA001199, 2021.
Bauer, P., Dueben, P. D., Hoefler, T., Quintino, T., Schulthess, T. C., and Wedi, N. P.: The digital revolution of Earth-system science, Nat. Comput. Sci., 1, 104–113, https://doi.org/10.1038/s43588-021-00023-0, 2021.
Brankart, J.-M., Candille, G., Garnier, F., Calone, C., Melet, A., Bouttier, P.-A., Brasseur, P., and Verron, J.: A generic approach to explicit simulation of uncertainty in the NEMO ocean model, Geosci. Model Dev., 8, 1285–1297, https://doi.org/10.5194/gmd-8-1285-2015, 2015.
Brassington, G. B., Sakov, P., Divakaran, P., Aijaz, S., Sweeney-Van Kinderen, J., Huang, X., and Allen, S.: OceanMAPS v4.0i: A global eddy resolving EnKF ocean forecasting system, in: OCEANS 2023-Limerick, Limerick, Ireland, 5–8 June 2023, IEEE, 1–8, https://doi.org/10.1109/OCEANSLimerick52467.2023.10244383, 2023.
Browne, P. A., De Rosnay, P., Zuo, H., Bennett, A., and Dawson, A.: Weakly coupled ocean–atmosphere data assimilation in the ECMWF NWP system, Remote Sens., 11, 234, https://doi.org/10.3390/rs11030234, 2019.
Bunney, C. and Saulter, A.: An ensemble forecast system for prediction of Atlantic-UK wind waves, Ocean Model., 96, 36–48, https://doi.org/10.1016/j.ocemod.2015.07.005, 2015.
Carrassi, A., Bocquet, M., Demaeyer, J., Grudzien, C., Raanes, P., and Vannitsem, S.: Data assimilation for chaotic dynamics, in: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, Vol. IV, Springer International Publishing, 1–42, ISBN 9783030777227, 2022.
D'Addezio, J. M., Smith, S., Jacobs, G. A., Helber, R. W., Rowley, C., Souopgui, I., and Carrier, M. J.: Quantifying wavelengths constrained by simulated SWOT observations in a submesoscale resolving ocean analysis/forecasting system, Ocean Model., 135, 40–55, https://doi.org/10.1016/j.ocemod.2019.02.001, 2019.
Davidson, M.: Forecasting coastal evolution on time-scales of days to decades, Coast. Eng., 168, 103928, https://doi.org/10.1016/j.coastaleng.2021.103928, 2021.
Du, J., Berner, J., Buizza, R., Charron, M., Houtekamer, P. L., Hou, D., Jankov, I., Mu, M., Wang, X., Wei, M., and Yuan, H.: Ensemble methods for meteorological predictions, in: Handbook of Hydrometeorological Ensemble Forecasting, edited by: Duan, Q., Pappenberger, F., Wood, A., Cloke, H. L., and Schaake, J., Springer, 1–52, https://doi.org/10.1007/978-3-642-40457-3_13-1, 2018.
Ebert, E. E.: Ability of a poor man's ensemble to predict the probability and distribution of precipitation, Mon. Weather Rev., 129, 2461–2480, https://doi.org/10.1175/1520-0493(2001)129<2461:AOAPMS>2.0.CO;2, 2001.
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res.-Oceans, 99, 10143–10162, https://doi.org/10.1029/94JC00572, 1994.
Francis, P. A., Jithin, A. K., Effy, J. B., Chatterjee, A., Chakraborty, K., Paul, A., Balaji, B., Shenoi, S. S. C., Biswamoy, P., Mukherjee, A., Singh, P., Deepsankar, B., Siva Reddy, S., Vinayachandran, P. N., Girish Kumar, M. S., Udaya Bhaskar, T. V. S., Ravichandran, M., Unnikrishnan, A. S., Shankar, D., Prakash, A., Aparna, S. G., Harikumar, R., Kaviyazhahu, K., Suprit, K., Shesu, R. V., Kiran Kumar, N., Srinivasa Rao, N., Annapurnaiah, K., Venkatesan, R., Rao, A. S., Rajagopal, E. N., Prasad, V. S., Gupta, M. D., Balakrishnan Nair, T. M., Rao, E. P. R., and Satyanarayana, B. V.: High-resolution operational ocean forecast and reanalysis system for the Indian ocean, B. Am. Meteorol. Soc., 101, E1340–E1356, 2020.
Fröhlich, K., Dobrynin, M., Isensee, K., Gessner, C., Paxian, A., Pohlmann, H., Haak, H., Brune, S., Früh, B., and Baehr, J.: The German climate forecast system: GCFS, J. Adv. Model. Earth Sys., 13, e2020MS002101, https://doi.org/10.1029/2020MS002101, 2021.
Fu, L. L. and Ubelmann, C.: On the transition from profile altimeter to swath altimeter for observing global ocean surface topography, J. Atmos. Ocean. Tech., 31, 560–568, https://doi.org/10.1175/JTECH-D-13-00109.1, 2014.
Fundel, V. J., Fleischhut, N., Herzog, S. M., Göber, M., and Hagedorn, R.: Promoting the use of probabilistic weather forecasts through a dialogue between scientists, developers and end-users, Q. J. Roy. Meteor. Soc., 145, 210–231, https://doi.org/10.1002/qj.3482, 2019.
Groeneveld, R. A. and Meeden, G.: Measuring skewness and kurtosis, J. Roy. Stat. Soc. D.-STA., 33, 391–399, https://doi.org/10.2307/2987742, 1984.
Gualdi, S., Borrelli, A., Cantelli, A., Davoli, G., del Mar Chaves Montero, M., Masina, S., Navarra, A., Sanna, A., and Tibaldi, S.: The new CMCC operational seasonal prediction system, CMCC Technial Note (RP0288), https://doi.org/10.25424/CMCC/SPS3.5, 2020.
Hamill, T. M.: Interpretation of rank histograms for verifying ensemble forecasts, Mon. Weather Rev., 129, 550–560, 2001.
Hoffman, R. N. and Kalnay, E.: Lagged average forecasting, an alternative to Monte Carlo forecasting, Tellus A, 35, 100–118, 1983.
Hoteit, I., Pham, D. T., Gharamti, M. E., and Luo, X.: Mitigating observation perturbation sampling errors in the stochastic EnKF, Mon. Weather Rev., 143, 2918–2936, 2015.
Hoteit, I., Luo, X., Bocquet, M., Kohl, A., and Ait-El-Fquih, B.: Data assimilation in oceanography: Current status and new directions, in: New Frontiers in Operational Oceanography, edited by: Chassignet, E. P., Pascual, A., Tintore, J., and Verron, J., GODAE Ocean View, 815 pp., https://doi.org/10.17125/gov2018.ch17, 2018.
Houtekamer, P. L. and Zhang, F.: Review of the ensemble Kalman filter for atmospheric data assimilation, Mon. Weather Rev., 144, 4489–4532, https://doi.org/10.1175/MWR-D-15-0440.1, 2016.
Johnson, C. and Bowler, N.: On the reliability and calibration of ensemble forecasts, Mon. Weather Rev., 137, 1717–1720, https://doi.org/10.1175/2009MWR2715.1, 2009.
Johnson, S. J., Stockdale, T. N., Ferranti, L., Balmaseda, M. A., Molteni, F., Magnusson, L., Tietsche, S., Decremer, D., Weisheimer, A., Balsamo, G., Keeley, S. P. E., Mogensen, K., Zuo, H., and Monge-Sanz, B. M.: SEAS5: the new ECMWF seasonal forecast system, Geosci. Model Dev., 12, 1087–1117, https://doi.org/10.5194/gmd-12-1087-2019, 2019.
Kristensen, N. M., Røed, L. P., and Sætra, Ø.: A forecasting and warning system of storm surge events along the Norwegian coast, Environ. Fluid Mech., 23, 307–329, https://doi.org/10.1007/s10652-022-09871-4, 2022.
Kwon, K. M., Choi, B.-J., Lee, S.-H., Kim, Y. H., Seo, G.-H., and Cho, Y.-K.: Effect of model error representation in the Yellow and East China Sea modeling system based on the ensemble Kalman filter, Ocean Dynam., 66, 263–283, https://doi.org/10.1007/s10236-015-0909-8, 2016.
Lea, D. J., While, J., Martin, M. J., Weaver, A., Storto, A., and Chrust, M.: A new global ocean ensemble system at the Met Office: Assessing the impact of hybrid data assimilation and inflation settings, Q. J. Roy. Meteor. Soc., 148, 1996–2030, https://doi.org/10.1002/qj.4292, 2022.
Leith, C. E.: Theoretical skill of Monte Carlo forecasts, Mon. Weather Rev., 102, 409–418, https://doi.org/10.1175/1520-0493(1974)102<0409:TSOMCF>2.0.CO;2, 1974.
Leutbecher, M. and Haiden, T.: Understanding changes of the continuous ranked probability score using a homogeneous Gaussian approximation, Q. J. Roy. Meteor. Soc., 147, 425–442, https://doi.org/10.1002/qj.3926, 2021.
Lima, L. N., Pezzi, L. P., Penny, S. G., and Tanajura, C. A. S.: An investigation of ocean model uncertainties through ensemble forecast experiments in the Southwest Atlantic Ocean, J. Geophys. Res.-Oceans, 124, 432–452, https://doi.org/10.1029/2018JC013919, 2019.
Lorenz, E. N.: The essence of chaos, Pure Appl. Geophys., 147, 598–599, 1996.
Martin, M. J., Balmaseda, M., Bertino, L., Brasseur, P., Brassington, G., Cummings, J., Fujii, Y., Lea, D. J., Lellouche, J.-M., Mogensen, K., Oke, P. R., Smith, G. C., Testut, C.-E., Waagbø, G. A., Waters, J., and Weaver, A. T.: Status and future of data assimilation in operational oceanography, J. Oper. Oceanogr., 8, s28–s48, https://doi.org/10.1080/1755876X.2015.1022055, 2015.
Melsom, A., Counillon, F., LaCasce, J. H., and Bertino, L.: Forecasting search areas using ensemble ocean circulation modeling, Ocean Dynam., 62, 1245–1257, https://doi.org/10.1007/s10236-012-0561-5, 2012.
Metzger, E. J., Smedstad, O. M., Thoppil, P. G., Hurlburt, H. E., Franklin, D. S., Peggion, G., Shriver, J. F., and Wallcraft, A. J.: Validation Test Report for the Global Ocean Forecast System V3. 0-1/12o HYCOM/NCODA: Phase II, Naval Research Lab. Memo. Rep. NRL/MR/7320-10-9236, Stennis Space Cent., Hancock, Miss., 70 pp., http://www7320.nrlssc.navy.mil/pubs/2010/metzger1-2010.pdf (last access: 20 February 2025), 2010.
Mullen, S. L. and Buizza, R.: The impact of horizontal resolution and ensemble size on probabilistic forecasts of precipitation by the ECMWF Ensemble Prediction System, Weather Forecast., 17, 173–191, 2002.
Nakanowatari, T., Xie, J., Bertino, L., Matsueda, M., Yamagami, A., and Inoue, J.: Ensemble forecast experiments of summertime sea ice in the Arctic Ocean using the TOPAZ4 ice-ocean data assimilation system, Environ. Res., 209, 112769, https://doi.org/10.1016/j.envres.2022.112769, 2022.
Penny, S. G., Behringer, D. W., Carton, J. A., and Kalnay, E.: A hybrid global ocean data assimilation system at NCEP, Mon. Weather Rev., 143, 4660–4677, 2015.
Pianezze, J., Beuvier, J., Lebeaupin Brossier, C., Samson, G., Faure, G., and Garric, G.: Development of a forecast-oriented kilometre-resolution ocean–atmosphere coupled system for western Europe and sensitivity study for a severe weather situation, Nat. Hazards Earth Syst. Sci., 22, 1301–1324, https://doi.org/10.5194/nhess-22-1301-2022, 2022.
Pinardi, N., Bonazzi, A., Scoccimarro, E., Dobricic, S., Navarra, A., Ghiselli, A., and Veronesi, P.: Very large ensemble ocean forecasting experiment using the grid computing infrastructure, B. Am. Meteorl. Soc., 89, 799–804, 2008.
Ren, H.-L., Wu, Y., Bao, Q., Ma, J., Liu, C., Wan, J., Li, Q., Wu, X., Liu, Y., Tian, B., Fu, J.-X., and Sun, J.: The China multi-model ensemble prediction system and its application to flood-season prediction in 2018, J. Meteorol. Res., 33, 540–552, https://doi.org/10.1007/s13351-019-8154-6, 2019.
Richardson, D. S.: Skill and relative economic value of the ECMWF ensemble prediction system, Q. J. Roy. Meteor. Soc., 126, 649–667, https://doi.org/10.1002/qj.49712656313, 2000.
Ryan, A. G., Regnier, C., Divakaran, P., Spindler, T., Mehra, A., Smith, G. C., Davidson, F., Hernandez, F., Maksymczuk, J., and Liu, Y.: GODAE OceanView Class 4 forecast verification framework: global ocean inter-comparison, J. Oper. Oceanogr., 8, s98–s111, https://doi.org/10.1080/1755876X.2015.1022330, 2015.
Sandery, P. A. and Sakov, P.: Ocean forecasting of mesoscale features can deteriorate by increasing model resolution towards the submesoscale, Nat. Commun., 8, 1566, https://doi.org/10.1038/s41467-017-01595-0, 2017.
Sandery, P. A., Sakov, P., and Majewski, L.: The impact of open boundary forcing on forecasting the East Australian Current using ensemble data assimilation, Ocean Model., 84, 1–11, https://doi.org/10.1016/j.ocemod.2014.09.005, 2014.
Sanikommu, S., Toye, H., Zhan, P., Langodan, S., Krokos, G., Knio, O., and Hoteit, I.: Impact of atmospheric and model physics perturbations on a high-resolution ensemble data assimilation system of the Red Sea, J. Geophys. Res.-Oceans, 125, e2019JC015611, https://doi.org/10.1029/2019JC015611, 2020.
Schiller, A., Brassington, G. B., Oke, P., Cahill, M., Divakaran, P., Entel, M., Freeman, J., Griffin, D., Herzfeld, M., Hoeke, R., Huang, X., Jones, E., King, E., Parker, B., Pitman, T., Rosebrock, U., Sweeney, J., Taylor, A., Thatcher, M., Woodham, R., and Zhong, A.: Bluelink ocean forecasting Australia: 15 years of operational ocean service delivery with societal, economic and environmental benefits, J. Oper. Oceanogr., 13, 1–18, https://doi.org/10.1080/1755876X.2019.1685834, 2020.
Schwartz, C. S. and Sobash, R. A.: Generating probabilistic forecasts from convection-allowing ensembles using neighborhood approaches: A review and recommendations, Mon. Weather Rev., 145, 3397–3418, https://doi.org/10.1175/MWR-D-16-0400.1, 2017.
Song, H., Hoteit, I., Cornuelle, B., Luo, X. X., and Subramanian, A. C.: An Adjoint-Based Adaptive Ensemble Kalman Filter, Mon. Weather Rev., 141, 3343, https://doi.org/10.1175/MWR-D-12-00244.1, 2013.
Storto, A. and Andriopoulos, P.: A new stochastic ocean physics package and its application to hybrid-covariance data assimilation, Q. J. Roy. Meteor. Soc., 147, 1691–1725, https://doi.org/10.1002/qj.3990, 2021.
Storto, A. and Yang, C.: Stochastic schemes for the perturbation of the atmospheric boundary conditions in ocean general circulation models, Front. Mar. Sci., 10, 1155803, https://doi.org/10.3389/fmars.2023.1155803, 2023.
Strohmaier, E., Meuer, H. W., Dongarra, J., and Simon, H. D.: The TOP500 list and progress in high-performance computing, Computer, 48, 42–49, https://doi.org/10.1109/MC.2015.338, 2015.
Thoppil, P. G., Frolov, S., Rowley, C. D., Reynolds, C. A., Jacobs, G. A., Metzger, J. E., Hogan, P. J., Barton, N., Wallcraft, A. J., Smedstad, O. M., and Shriver, J. F.: Ensemble forecasting greatly expands the prediction horizon for ocean mesoscale variability, Commun. Earth Environ., 2, 89, https://doi.org/10.1038/s43247-021-00151-5, 2021.
Vandenbulcke, L. and Barth, A.: A stochastic operational forecasting system of the Black Sea: Technique and validation, Ocean Model., 93, 7–21, https://doi.org/10.1016/j.ocemod.2015.07.010, 2015.
Wedd, R., Alves, O., de Burgh-Day, C., Down, C., Griffiths, M., Hendon, H. H., Hudson, D., Li, S., Lim, E.-P., Marshall, A. G., Shi, L., Smith, P., Smith, G., Spillman, C. M., Wang, G., Wheeler, M. C., Yan, H., Yin, Y., Young, G., Zhao, M., Xiao, Y., and Zhou, X.: ACCESS-S2: the upgraded Bureau of Meteorology multi-week to seasonal prediction system, J. South. Hemisph. Earth Syst. Sci., 72, 218–242, https://doi.org/10.1071/ES22026, 2022.
Whitaker, J. S. and Hamill, T. M.: Ensemble data assimilation without perturbed observations, Mon. Weather Rev., 130, 1913–1924, 2002.
Whitaker, J. S. and Hamill, T. M.: Evaluating methods to account for system errors in ensemble data assimilation, Mon. Weather Rev., 140, 3078–3089, https://doi.org/10.1175/MWR-D-11-00276.1, 2012.
Zedler, S. E., Polton, J. A., King, R. R., and Wakelin, S. L.: The effect of uncertain river forcing on the thermohaline properties of the northwest European shelf seas, Ocean Model., 183, 102196, https://doi.org/10.1016/j.ocemod.2023.102196, 2023.
Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019.
Short summary
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.
This paper explores how using multiple predictions instead of just one can improve ocean...
Altmetrics
Final-revised paper
Preprint