Articles | Volume 20, issue 6
https://doi.org/10.5194/os-20-1657-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-20-1657-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing the impact of future altimeter constellations in the Met Office global ocean forecasting system
Robert R. King
CORRESPONDING AUTHOR
Met Office, Exeter, UK
Matthew J. Martin
Met Office, Exeter, UK
Lucile Gaultier
OceanDataLab, Plouzané, France
Jennifer Waters
Met Office, Exeter, UK
Clément Ubelmann
DATLAS, Grenoble, France
Craig Donlon
ESTEC, ESA, Noordwijk, Netherlands
Related authors
Robert R. King and Matthew J. Martin
Ocean Sci., 17, 1791–1813, https://doi.org/10.5194/os-17-1791-2021, https://doi.org/10.5194/os-17-1791-2021, 2021
Short summary
Short summary
The SWOT satellite will provide a step change in our ability to measure the sea surface height over large areas, and so improve operational ocean forecasts, but will be affected by large correlated errors. We found that while SWOT observations without these errors significantly improved our system, including correlated errors degraded most variables. To realise the full benefits offered by the SWOT mission, we must develop methods to account for correlated errors in ocean forecasting systems.
Marina Tonani, Peter Sykes, Robert R. King, Niall McConnell, Anne-Christine Péquignet, Enda O'Dea, Jennifer A. Graham, Jeff Polton, and John Siddorn
Ocean Sci., 15, 1133–1158, https://doi.org/10.5194/os-15-1133-2019, https://doi.org/10.5194/os-15-1133-2019, 2019
Short summary
Short summary
A new high-resolution ocean model at 1.5 km has replaced the 7 km system for delivering short-term forecasts of the North-West European Shelf seas. The products (temperature, salinity, currents, and sea surface height) are available on the Copernicus Marine Service catalogue. This study focuses on the high-resolution impact on the quality of the products delivered to the users. Results show that the high-resolution model is better at resolving the variability of the physical variables.
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.
Enda O'Dea, Rachel Furner, Sarah Wakelin, John Siddorn, James While, Peter Sykes, Robert King, Jason Holt, and Helene Hewitt
Geosci. Model Dev., 10, 2947–2969, https://doi.org/10.5194/gmd-10-2947-2017, https://doi.org/10.5194/gmd-10-2947-2017, 2017
Short summary
Short summary
An update to an ocean modelling configuration for the European North West Shelf is described. It is assessed against observations and climatologies for 1981–2012. Sensitivities in the model configuration updates are assessed to understand changes in the model system. The model improves upon an existing model of the region, although there remain some areas with significant biases. The paper highlights the dependence upon the quality of the river inputs.
Guisella Gacitúa, Jacob Lorentsen Høyer, Sten Schmidl Søbjærg, Hoyeon Shi, Sotirios Skarpalezos, Ioanna Karagali, Emy Alerskans, and Craig Donlon
Geosci. Instrum. Method. Data Syst., 13, 373–391, https://doi.org/10.5194/gi-13-373-2024, https://doi.org/10.5194/gi-13-373-2024, 2024
Short summary
Short summary
In spring 2021, a study compared sea surface temperature (SST) measurements from thermal infrared (IR) and passive microwave (PMW) radiometers on a ferry between Denmark and Iceland. The goal was to reduce atmospheric effects and directly compare IR and PMW measurements. A method was developed to convert PMW data to match IR data, with uncertainties analysed in the process. The findings provide insights to improve SST inter-comparisons and enhance the synergy between IR and PMW observations.
Yann Drillet, Matthew Martin, Yasumasa Miyazawa, Mike Bell, Eric Chassignet, and Stefania Ciliberti
State Planet Discuss., https://doi.org/10.5194/sp-2024-38, https://doi.org/10.5194/sp-2024-38, 2024
Preprint under review for SP
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.
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.
Michael J. Bell, Yann Drillet, Matthew Martin, Andreas Schiller, and Stefania Ciliberti
State Planet Discuss., https://doi.org/10.5194/sp-2024-41, https://doi.org/10.5194/sp-2024-41, 2024
Revised manuscript accepted for SP
Short summary
Short summary
We describe, at an elementary level, the spatially varying properties of the ocean that physical ocean models represent, the principles they use to evolve these properties with time, the physical phenomena that they simulate, and some of the roles these phenomena play within the Earth system. We also describe, in some technical detail, the methods and approximations that the models use and the difficulties that limit their accuracy or reliability.
Matthew J. Martin, Ibrahim Hoteit, Laurent Bertino, and Andrew M. Moore
State Planet Discuss., https://doi.org/10.5194/sp-2024-20, https://doi.org/10.5194/sp-2024-20, 2024
Preprint under review for SP
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, as well as 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.
Michaël Ablain, Noémie Lalau, Benoit Meyssignac, Robin Fraudeau, Anne Barnoud, Gérald Dibarboure, Alejandro Egido, and Craig James Donlon
EGUsphere, https://doi.org/10.5194/egusphere-2024-1802, https://doi.org/10.5194/egusphere-2024-1802, 2024
Short summary
Short summary
This study proposes a novel cross-validation method to assess the instrumental stability in sea level trends. The method involves implementing a second tandem flight phase between two successive altimeter missions a few years after the first. The trend in systematic instrumental differences made during the two tandem phases can be estimated below ±0.1 mm/yr (16–84 % confidence level) on a global scale, for time intervals between the tandem phases of four years or more.
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.
Florian Le Guillou, Lucile Gaultier, Maxime Ballarotta, Sammy Metref, Clément Ubelmann, Emmanuel Cosme, and Marie-Helène Rio
Ocean Sci., 19, 1517–1527, https://doi.org/10.5194/os-19-1517-2023, https://doi.org/10.5194/os-19-1517-2023, 2023
Short summary
Short summary
Altimetry provides sea surface height (SSH) data along one-dimensional tracks. For many applications, the tracks are interpolated in space and time to provide gridded SSH maps. The operational SSH gridded products filter out the small-scale signals measured on the tracks. This paper evaluates the performances of a recently implemented dynamical method to retrieve the small-scale signals from real SSH data. We show a net improvement in the quality of SSH maps when compared to independent data.
Emma K. Fiedler, Matthew J. Martin, Ed Blockley, Davi Mignac, Nicolas Fournier, Andy Ridout, Andrew Shepherd, and Rachel Tilling
The Cryosphere, 16, 61–85, https://doi.org/10.5194/tc-16-61-2022, https://doi.org/10.5194/tc-16-61-2022, 2022
Short summary
Short summary
Sea ice thickness (SIT) observations derived from CryoSat-2 satellite measurements have been successfully used to initialise an ocean and sea ice forecasting model (FOAM). Other centres have previously used gridded and averaged SIT observations for this purpose, but we demonstrate here for the first time that SIT measurements along the satellite orbit track can be used. Validation of the resulting modelled SIT demonstrates improvements in the model performance compared to a control.
Robert R. King and Matthew J. Martin
Ocean Sci., 17, 1791–1813, https://doi.org/10.5194/os-17-1791-2021, https://doi.org/10.5194/os-17-1791-2021, 2021
Short summary
Short summary
The SWOT satellite will provide a step change in our ability to measure the sea surface height over large areas, and so improve operational ocean forecasts, but will be affected by large correlated errors. We found that while SWOT observations without these errors significantly improved our system, including correlated errors degraded most variables. To realise the full benefits offered by the SWOT mission, we must develop methods to account for correlated errors in ocean forecasting systems.
Thomas Lavergne, Montserrat Piñol Solé, Emily Down, and Craig Donlon
The Cryosphere, 15, 3681–3698, https://doi.org/10.5194/tc-15-3681-2021, https://doi.org/10.5194/tc-15-3681-2021, 2021
Short summary
Short summary
Pushed by winds and ocean currents, polar sea ice is on the move. We use passive microwave satellites to observe this motion. The images from their orbits are often put together into daily images before motion is measured. In our study, we measure motion from the individual orbits directly and not from the daily images. We obtain many more motion vectors, and they are more accurate. This can be used for current and future satellites, e.g. the Copernicus Imaging Microwave Radiometer (CIMR).
Malcolm McMillan, Alan Muir, and Craig Donlon
The Cryosphere, 15, 3129–3134, https://doi.org/10.5194/tc-15-3129-2021, https://doi.org/10.5194/tc-15-3129-2021, 2021
Short summary
Short summary
We evaluate the consistency of ice sheet elevation measurements made by two satellites: Sentinel-3A and Sentinel-3B. We analysed data from the unique
tandemphase of the mission, where the two satellites flew 30 s apart to provide near-instantaneous measurements of Earth's surface. Analysing these data over Antarctica, we find no significant difference between the satellites, which is important for demonstrating that they can be used interchangeably for long-term ice sheet monitoring.
Lise Kilic, Catherine Prigent, Carlos Jimenez, and Craig Donlon
Ocean Sci., 17, 455–461, https://doi.org/10.5194/os-17-455-2021, https://doi.org/10.5194/os-17-455-2021, 2021
Short summary
Short summary
The Copernicus Imaging Microwave Radiometer (CIMR) is one of the high-priority satellite missions of the Copernicus program within the European Space Agency. It is designed to respond to the European Union Arctic policy. Its channels, incidence angle, precisions, and spatial resolutions have been selected to observe the Arctic Ocean with the recommendations expressed by the user communities.
In this note, we present the sensitivity analysis that has led to the choice of the CIMR channels.
Louis Marié, Fabrice Collard, Frédéric Nouguier, Lucia Pineau-Guillou, Danièle Hauser, François Boy, Stéphane Méric, Peter Sutherland, Charles Peureux, Goulven Monnier, Bertrand Chapron, Adrien Martin, Pierre Dubois, Craig Donlon, Tania Casal, and Fabrice Ardhuin
Ocean Sci., 16, 1399–1429, https://doi.org/10.5194/os-16-1399-2020, https://doi.org/10.5194/os-16-1399-2020, 2020
Short summary
Short summary
With present-day techniques, ocean surface currents are poorly known near the Equator and globally for spatial scales under 200 km and timescales under 30 d. Wide-swath radar Doppler measurements are an alternative technique. Such direct surface current measurements are, however, affected by platform motions and waves. These contributions are analyzed in data collected during the DRIFT4SKIM airborne and in situ experiment, demonstrating the possibility of measuring currents from space globally.
Guillaume Dodet, Jean-François Piolle, Yves Quilfen, Saleh Abdalla, Mickaël Accensi, Fabrice Ardhuin, Ellis Ash, Jean-Raymond Bidlot, Christine Gommenginger, Gwendal Marechal, Marcello Passaro, Graham Quartly, Justin Stopa, Ben Timmermans, Ian Young, Paolo Cipollini, and Craig Donlon
Earth Syst. Sci. Data, 12, 1929–1951, https://doi.org/10.5194/essd-12-1929-2020, https://doi.org/10.5194/essd-12-1929-2020, 2020
Short summary
Short summary
Sea state data are of major importance for climate studies, marine engineering, safety at sea and coastal management. However, long-term sea state datasets are sparse and not always consistent. The CCI is a program of the European Space Agency, whose objective is to realize the full potential of global Earth Observation archives in order to contribute to the ECV database. This paper presents the implementation of the first release of the Sea State CCI dataset.
Thomas Holding, Ian G. Ashton, Jamie D. Shutler, Peter E. Land, Philip D. Nightingale, Andrew P. Rees, Ian Brown, Jean-Francois Piolle, Annette Kock, Hermann W. Bange, David K. Woolf, Lonneke Goddijn-Murphy, Ryan Pereira, Frederic Paul, Fanny Girard-Ardhuin, Bertrand Chapron, Gregor Rehder, Fabrice Ardhuin, and Craig J. Donlon
Ocean Sci., 15, 1707–1728, https://doi.org/10.5194/os-15-1707-2019, https://doi.org/10.5194/os-15-1707-2019, 2019
Short summary
Short summary
FluxEngine is an open-source software toolbox designed to allow for the easy and accurate calculation of air–sea gas fluxes. This article describes new functionality and capabilities, which include the ability to calculate fluxes for nitrous oxide and methane, optimisation for running FluxEngine on a stand-alone desktop computer, and extensive new features to support the in situ measurement community. Four research case studies are used to demonstrate these new features.
Anne Braakmann-Folgmann and Craig Donlon
The Cryosphere, 13, 2421–2438, https://doi.org/10.5194/tc-13-2421-2019, https://doi.org/10.5194/tc-13-2421-2019, 2019
Short summary
Short summary
Snow on sea ice is a fundamental climate variable. We propose a novel approach to estimate snow depth on sea ice from satellite microwave radiometer measurements at several frequencies using neural networks (NNs). We evaluate our results with airborne snow depth measurements and compare them to three other established snow depth algorithms. We show that our NN results agree better with the airborne data than the other algorithms. This is also advantageous for sea ice thickness calculation.
Marina Tonani, Peter Sykes, Robert R. King, Niall McConnell, Anne-Christine Péquignet, Enda O'Dea, Jennifer A. Graham, Jeff Polton, and John Siddorn
Ocean Sci., 15, 1133–1158, https://doi.org/10.5194/os-15-1133-2019, https://doi.org/10.5194/os-15-1133-2019, 2019
Short summary
Short summary
A new high-resolution ocean model at 1.5 km has replaced the 7 km system for delivering short-term forecasts of the North-West European Shelf seas. The products (temperature, salinity, currents, and sea surface height) are available on the Copernicus Marine Service catalogue. This study focuses on the high-resolution impact on the quality of the products delivered to the users. Results show that the high-resolution model is better at resolving the variability of the physical variables.
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.
Thomas Block, Sabine Embacher, Christopher J. Merchant, and Craig Donlon
Geosci. Model Dev., 11, 2419–2427, https://doi.org/10.5194/gmd-11-2419-2018, https://doi.org/10.5194/gmd-11-2419-2018, 2018
Short summary
Short summary
For calibration and validation purposes it is necessary to detect simultaneous data acquisitions from different spaceborne platforms. We present an algorithm and a software system which implements a general approach to resolve this problem. The multisensor matchup system (MMS) can detect simultaneous acquisitions in a large dataset (> 100 TB) and extract data for matching locations for further analysis. The MMS implements a flexible software infrastructure and allows for high parallelization.
Fabrice Ardhuin, Yevgueny Aksenov, Alvise Benetazzo, Laurent Bertino, Peter Brandt, Eric Caubet, Bertrand Chapron, Fabrice Collard, Sophie Cravatte, Jean-Marc Delouis, Frederic Dias, Gérald Dibarboure, Lucile Gaultier, Johnny Johannessen, Anton Korosov, Georgy Manucharyan, Dimitris Menemenlis, Melisa Menendez, Goulven Monnier, Alexis Mouche, Frédéric Nouguier, George Nurser, Pierre Rampal, Ad Reniers, Ernesto Rodriguez, Justin Stopa, Céline Tison, Clément Ubelmann, Erik van Sebille, and Jiping Xie
Ocean Sci., 14, 337–354, https://doi.org/10.5194/os-14-337-2018, https://doi.org/10.5194/os-14-337-2018, 2018
Short summary
Short summary
The Sea surface KInematics Multiscale (SKIM) monitoring mission is a proposal for a future satellite that is designed to measure ocean currents and waves. Using a Doppler radar, the accurate measurement of currents requires the removal of the mean velocity due to ocean wave motions. This paper describes the main processing steps needed to produce currents and wave data from the radar measurements. With this technique, SKIM can provide unprecedented coverage and resolution, over the global ocean.
Enda O'Dea, Rachel Furner, Sarah Wakelin, John Siddorn, James While, Peter Sykes, Robert King, Jason Holt, and Helene Hewitt
Geosci. Model Dev., 10, 2947–2969, https://doi.org/10.5194/gmd-10-2947-2017, https://doi.org/10.5194/gmd-10-2947-2017, 2017
Short summary
Short summary
An update to an ocean modelling configuration for the European North West Shelf is described. It is assessed against observations and climatologies for 1981–2012. Sensitivities in the model configuration updates are assessed to understand changes in the model system. The model improves upon an existing model of the region, although there remain some areas with significant biases. The paper highlights the dependence upon the quality of the river inputs.
J. R. Siddorn, S. A. Good, C. M. Harris, H. W. Lewis, J. Maksymczuk, M. J. Martin, and A. Saulter
Ocean Sci., 12, 217–231, https://doi.org/10.5194/os-12-217-2016, https://doi.org/10.5194/os-12-217-2016, 2016
Short summary
Short summary
The Met Office provides a range of services in the marine environment. To support these services, and to ensure they evolve to meet the demands of users and are based on the best available science, a number of scientific challenges need to be addressed. The paper summarises the key challenges, and highlights some priorities for the ocean monitoring and forecasting research group at the Met Office.
L. M. Goddijn-Murphy, D. K. Woolf, P. E. Land, J. D. Shutler, and C. Donlon
Ocean Sci., 11, 519–541, https://doi.org/10.5194/os-11-519-2015, https://doi.org/10.5194/os-11-519-2015, 2015
Short summary
Short summary
We describe the OceanFlux Greenhouse Gases methodology for creating an ocean surface CO2 climatology. In situ measurements valid for instantaneous sea surface temperature (SST) were recomputed using a more consistent and averaged SST. The results were normalised to year 2010, averaged by month, and interpolated onto a global 1°×1° grid. The 12 monthly distributions of ocean surface CO2 (see supplement) can be used in air-sea gas flux calculations together with climatologies of other variables.
E. W. Blockley, M. J. Martin, A. J. McLaren, A. G. Ryan, J. Waters, D. J. Lea, I. Mirouze, K. A. Peterson, A. Sellar, and D. Storkey
Geosci. Model Dev., 7, 2613–2638, https://doi.org/10.5194/gmd-7-2613-2014, https://doi.org/10.5194/gmd-7-2613-2014, 2014
P. E. Land, J. D. Shutler, R. D. Cowling, D. K. Woolf, P. Walker, H. S. Findlay, R. C. Upstill-Goddard, and C. J. Donlon
Biogeosciences, 10, 8109–8128, https://doi.org/10.5194/bg-10-8109-2013, https://doi.org/10.5194/bg-10-8109-2013, 2013
Related subject area
Approach: Operational Oceanography | Properties and processes: Mesoscale to submesoscale dynamics
Transient Attracting Profiles in the Great Pacific Garbage Patch
Persistence and Robustness of Lagrangian Coherent Structures
Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea
Luca Kunz, Alexa Griesel, Carsten Eden, Rodrigo Duran, and Bruno Sainte-Rose
Ocean Sci., 20, 1611–1630, https://doi.org/10.5194/os-20-1611-2024, https://doi.org/10.5194/os-20-1611-2024, 2024
Short summary
Short summary
Transient Attracting Profiles (TRAPs) indicate the most attracting regions of the flow and have the potential to facilitate offshore cleanups in the Great Pacific Garbage Patch. We study the characteristics of TRAPs and the prospects for predicting debris transport from a mesoscale-permitting dataset. Our findings show the relevance of TRAP lifetime estimations to an operational application, and our TRAP tracking algorithm may even benefit other challenges that are related to search at sea.
Mateusz Matuszak, Johannes Röhrs, Pål Erik Isachsen, and Martina Idžanović
EGUsphere, https://doi.org/10.5194/egusphere-2024-1171, https://doi.org/10.5194/egusphere-2024-1171, 2024
Short summary
Short summary
Lagrangian coherent structures (LCS) describe material transport in ocean flow by describing transport barriers and accumulation regions. Noting that circulation fields from models are prone to uncertainties, we discuss the implications for LCS analysis. LCSs add value to forecasting when these are certain and long-lived. Averaging LCS reveals where these are more certain and long-lived, often influenced by bottom topography. Large scale LCSs show a higher degree of certainty and longevity.
Giulia Bonino, Giuliano Galimberti, Simona Masina, Ronan McAdam, and Emanuela Clementi
Ocean Sci., 20, 417–432, https://doi.org/10.5194/os-20-417-2024, https://doi.org/10.5194/os-20-417-2024, 2024
Short summary
Short summary
This study employs machine learning to predict marine heatwaves (MHWs) in the Mediterranean Sea. MHWs have far-reaching impacts on society and ecosystems. Using data from ESA and ECMWF, the research develops accurate prediction models for sea surface temperature (SST) and MHWs across the region. Notably, machine learning methods outperform existing forecasting systems, showing promise in early MHW predictions. The study also highlights the importance of solar radiation as a predictor of SST.
Cited articles
Aijaz, S., Brassington, G. B., Divakaran, P., Regnier, C., Drevillon, M., Maksymczuk, J., and Peterson, K. A.: Verification and intercomparison of global ocean Eulerian near-surface currents, Ocean Model., 186, 102241, https://doi.org/10.1016/j.ocemod.2023.102241, 2023. a
Ballarotta, M., Ubelmann, C., Pujol, M.-I., Taburet, G., Fournier, F., Legeais, J.-F., Faugère, Y., Delepoulle, A., Chelton, D., Dibarboure, G., and Picot, N.: On the resolutions of ocean altimetry maps, Ocean Sci., 15, 1091–1109, https://doi.org/10.5194/os-15-1091-2019, 2019. a, b, c
Ballarotta, M., Albert, A., Ajayi, A., Beauchamp, M., Cosme, E., Le Sommer, J., and Metref, S.: Ocean Data Challenge 2020a_SSH_mapping_NATL60, GitHub [code], https://github.com/ocean-data-challenges/2020a_SSH_mapping_NATL60 (last access: 9 December 2024), 2020.
Barbosa Aguiar, A., Bell, M. J., Blockley, E., Calvert, D., Crocker, R., Inverarity, G., King, R., Lea, D. J., Maksymczuk, J., Martin, M. J., Price, M. R., Siddorn, J., Smout-Day, K., Waters, J., and While, J.: The Met Office Forecast Ocean Assimilation Model (FOAM) using a 1/12-degree grid for global forecasts, Q. J. Roy. Meteorol. Soc., 150, 3827–3852, https://doi.org/10.1002/qj.4798, 2024. a
Benkiran, M., Ruggiero, G., Greiner, E., Le Traon, P.-Y., Remy, E., Lellouche, J. M., Bourdalle-Badie, R., Drillet, Y., and Tchonang, B.: Assessing the Impact of the Assimilation of SWOT Observations in a Global High-Resolution Analysis and Forecasting System – Part 1: Methods, Front. Mar. Sci., 8, 691955, https://doi.org/10.3389/fmars.2021.691955, 2021. a
Benkiran, M., Le Traon, P.-Y., Rémy, E., and Drillet, Y.: Impact of two high resolution altimetry mission concepts for ocean forecasting, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-420, 2024. a, b
Bloom, S. C., Takacs, L. L., da Silva, A. M., and Ledvina, D.: Data Assimilation Using Incremental Analysis Updates, Mon. Wea. Rev., 124, 1256–1271, https://doi.org/10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2, 1996. a
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. a
Dibarboure, G., Ubelmann, C., Flamant, B., Briol, F., Peral, E., Bracher, G., Vergara, O., Faugère, Y., Soulat, F., and Picot, N.: Data-driven calibration algorithm and pre-launch performance simulations for the swot mission, Remote Sens., 14, 6070, https://doi.org/10.3390/rs14236070, 2022. a
Esteban-Fernandez, D.: Swot Mission Performance and Error Budget, IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018, 8625–8628, https://doi.org/10.1109/IGARSS.2018.8517385, 2018. a
Fujii, Y., Rémy, E., Zuo, H., Oke, P., Halliwell, G., Gasparin, F., Benkiran, M., Loose, N., Cummings, J., Xie, J., Xue, Y., Masuda, S., Smith, G., Balmaseda, M., Germineaud, C., Lea, D., 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 a future vision for designing and supporting ocean observational networks, Front. Mar. Sci., 6, 417, https://doi.org/10.3389/fmars.2019.00417, 2019. a, b
Gasparin, F., Greiner, E., Lellouche, J.-M., Legalloudec, O., Garric, G., Drillet, Y., Bourdallé-Badie, R., Traon, P.-Y. L., Rémy, E., and Drévillon, M.: A large-scale view of oceanic variability from 2007 to 2015 in the global high resolution monitoring and forecasting system at Mercator Océan, J. Mar. Syst., 187, 260–276, https://doi.org/10.1016/j.jmarsys.2018.06.015, 2018. a
Gasparin, F., Guinehut, S., Mao, C., Mirouze, I., Rémy, E., King, R. R., Hamon, M., Reid, R., Storto, A., Le Traon, P.-Y., and Martin, M.: Requirements for an integrated in situ Atlantic Ocean observing system from coordinated observing system simulation experiments, Front. Mar. Sci., 6, 83, https://doi.org/10.3389/fmars.2019.00083, 2019. a, b, c, d
Gaultier, L., Ubelmann, C., and Fu, L.-L.: The challenge of using future SWOT data for oceanic field reconstruction, J. Atmos. Ocean. Technol., 33, 119–126, https://doi.org/10.1175/JTECH-D-15-0160.1, 2016. a, b, c
Good, S. A., Martin, M. J., and Rayner, N. A.: EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, J. Geophys. Res.-Ocean., 118, 6704–6716, https://doi.org/10.1002/2013JC009067, 2013. a
Guiavarc'h, C., Roberts-Jones, J., Harris, C., Lea, D. J., Ryan, A., and Ascione, I.: Assessment of ocean analysis and forecast from an atmosphere–ocean coupled data assimilation operational system, Ocean Sci., 15, 1307–1326, https://doi.org/10.5194/os-15-1307-2019, 2019. a
Guillet, O., Weaver, A. T., Vasseur, X., Michel, Y., Gratton, S., and Gürol, S.: Modelling spatially correlated observation errors in variational data assimilation using a diffusion operator on an unstructured mesh, Q. J. Roy. Meteorol. Soc., 145, 1947–1967, https://doi.org/10.1002/qj.3537, 2019. a, b
Halliwell, G. R., Mehari, M. F., Le Hénaff, M., Kourafalou, V. H., Androulidakis, I. S., Kang, H. S., and Atlas, R.: North Atlantic Ocean OSSE system: Evaluation of operational ocean observing system components and supplemental seasonal observations for potentially improving tropical cyclone prediction in coupled systems, J. Oper. Oceanogr., 10, 154–175, https://doi.org/10.1080/1755876X.2017.1322770, 2017. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hoffman, R. N. and Atlas, R.: Future observing system simulation experiments, Bull. Am. Meteorol. Soc., 97, 1601–1616, https://doi.org/10.1175/BAMS-D-15-00200.1, 2016. a
Hunke, E. C. and Lipscombe, W. H.: CICE: The Los Alamos sea ice model, Documentation and software users manual, Version 4.1 (LA-CC-06012), T-3 Fluid Dynamics Group, Los Alamos National Laboratory, Los Alamos, US, https://csdms.colorado.edu/w/images/CICE_documentation_and_software_user's_manual.pdf (last access: 9 December 2024), 2010. a
King, R. R. and Martin, M. J.: Assimilating realistically simulated wide-swath altimeter observations in a high-resolution shelf-seas forecasting system, Ocean Sci., 17, 1791–1813, https://doi.org/10.5194/os-17-1791-2021, 2021. a, b, c
King, R. R., While, J., Martin, M. J., Lea, D. J., Lemieux-Dudon, B., Waters, J., and O'Dea, E.: Improving the initialisation of the Met Office operational shelf-seas model, Ocean Model., 130, 1–14, https://doi.org/10.1016/j.ocemod.2018.07.004, 2018. a
Le Guillou, F., Metref, S., Cosme, E., Ubelmann, C., Ballarotta, M., Le Sommer, J., and Verron, J.: Mapping altimetry in the forthcoming swot era by back-and-forth nudging a one-layer quasigeostrophic model, J. Atmos. Ocean. Technol., 38, 697–710, https://doi.org/10.1175/JTECH-D-20-0104.1, 2021. a
Le Traon, P.-Y., Dibarboure, G., Jacobs, G., Martin, M., Rémy, E., and Schiller, A.: Use of satellite altimetry for operational oceanography, in: Satellite altimetry over oceans and land surfaces, 581–608, CRC Press, https://doi.org/10.1201/9781315151779-18, 2017. a
Lea, D., Drecourt, J.-P., Haines, K., and Martin, M.: Ocean altimeter assimilation with observational-and model-bias correction, Quarterly J. Roy. Meteorol. Soc., 134, 1761–1774, https://doi.org/10.1002/qj.320, 2008. a
Lea, D. J., Mirouze, I., Martin, M. J., King, R. R., Hines, A., Walters, D., and Thurlow, M.: Assessing a New Coupled Data Assimilation System Based on the Met Office Coupled Atmosphere-Land-Ocean-Sea Ice Model, Mon. Weather Rev., 143, 4678–4694, https://doi.org/10.1175/MWR-D-15-0174.1, 2015. a
Lellouche, J.-M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., Benkiran, M., Testut, C.-E., Bourdalle-Badie, R., Gasparin, F., Olga Hernandez, O., Levier, B., Drillet, Y., Remy, E., and Le Traon, P.-Y.: Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time ° high-resolution system, Ocean Sci., 14, 1093–1126, https://doi.org/10.5194/os-14-1093-2018, 2018. a
MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P., and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. J. Roy. Meteorol. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2015. a
Madec, G., Bourdallé-Badie, R., Chanut, J., Clementi, E., Coward, A., Ethé, C., Iovino, D., Lea, D., Lévy, C., Lovato, T., Martin, N., Masson, S., Mocavero, S., Rousset, C., Storkey, D., Müeller, S., Nurser, G., Bell, M., Samson, G., Mathiot, P., Mele, F., and Moulin, A.: NEMO ocean engine, Zenodo, https://doi.org/10.5281/zenodo.6334656, 2022. a
Mao, C., King, R. R., Reid, R., Martin, M. J., and Good, S. A.: Assessing the Potential Impact of Changes to the Argo and Moored Buoy Arrays in an Operational Ocean Analysis System, Front. Mar. Sci., 7, 588267, https://doi.org/10.3389/fmars.2020.588267, 2020. a, b
Martin, M. J., Remy, E., Tranchant, B., King, R. R., Greiner, E., and Donlon, C.: Observation impact statement on satellite sea surface salinity data from two operational global ocean forecasting systems, J. Oper. Oceanogr., 15, 87–103, https://doi.org/10.1080/1755876X.2020.1771815, 2020. a
Mignac, D., Waters, J., Lea, D. J., Martin, M. J., While, J., Weaver, A. T., Vidard, A., Guiavarc’h, C., Storkey, D., Ford, D., Blockley, E. W., Baker, J., Haines, K., Price, M. R., Bell, M. J., and Renshaw, R.: Updates to the Met Office’s global ocean-sea ice forecasting system including model and data assimilation changes, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3143, 2024. a
Mirouze, I., Blockley, E. W., Lea, D. J., Martin, M. J., and Bell, M. J.: A multiple length scale correlation operator for ocean data assimilation, Tellus A, 68, 29744, https://doi.org/10.3402/tellusa.v68.29744, 2016. a, b
Morrow, R., Fu, L.-L., Ardhuin, F., Benkiran, M., Chapron, B., Cosme, E., d’Ovidio, F., Farrar, J. T., Gille, S. T., Lapeyre, G., Le Traon, P.-Y., Pascual, A., Ponte, A., Qiu, B., Rascle, N., Ubelmann, C., Wang, J., and Zaron, E. D.: Global Observations of Fine-Scale Ocean Surface Topography With the Surface Water and Ocean Topography (SWOT) Mission, Front. Mar. Sci., 6, 232, https://doi.org/10.3389/fmars.2019.00232, 2019. a
Oke, P. R. and O'Kane, T. J.: Observing system design and assessment, Operational Oceanography in the 21st Century, Springer, 123–151, https://doi.org/10.1007/978-94-007-0332-2_5, 2011. a
Peral, E., Rodríguez, E., and Esteban-Fernández, D.: Impact of surface waves on SWOT's projected ocean accuracy, Remote Sens., 7, 14509–14529, https://doi.org/10.3390/rs71114509, 2015. a
Pujol, M.-I., Dupuy, S., Vergara, O., Sánchez Román, A., Faugère, Y., Prandi, P., Dabat, M.-L., Dagneaux, Q., Lievin, M., Cadier, E., Dibarboure, G., and Picot, N.: Refining the Resolution of DUACS Along-Track Level-3 Sea Level Altimetry Products, Remote Sens., 15, 793, https://doi.org/10.3390/rs15030793, 2023. a
Ridley, J. K., Blockley, E. W., Keen, A. B., Rae, J. G., West, A. E., and Schroeder, D.: The sea ice model component of HadGEM3-GC3. 1, Geosci. Model Dev., 11, 713–723, https://doi.org/10.5194/gmd-11-713-2018, 2018. a
Rossa, A., Nurmi, P., and Ebert, E.: Overview of methods for the verification of quantitative precipitation forecasts, in: Precipitation: Advances in measurement, estimation and prediction, Springer, 419–452, https://doi.org/10.1007/978-3-540-77655-0_16, 2008. a
Storkey, D., Blaker, A. T., Mathiot, P., Megann, A., Aksenov, Y., Blockley, E. W., Calvert, D., Graham, T., Hewitt, H. T., Hyder, P., Kuhlbrodt, T., Rae, J. G. L., and Sinha, B.: UK Global Ocean GO6 and GO7: a traceable hierarchy of model resolutions, Geosci. Model Dev., 11, 3187–3213, https://doi.org/10.5194/gmd-11-3187-2018, 2018. a
Tchonang, B. C., Benkiran, M., Le Traon, P.-Y., Jan van Gennip, S., Lellouche, J. M., and Ruggiero, G.: Assessing the Impact of the Assimilation of SWOT Observations in a Global High-Resolution Analysis and Forecasting System 2̆013 Part 2: Results, Front. Mar. Sci., 8, 1208, https://doi.org/10.3389/fmars.2021.687414, 2021. a
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. a
Waters, J., Lea, D. J., Martin, M. J., Mirouze, I., Weaver, A., and While, J.: Implementing a variational data assimilation system in an operational ° global ocean model, Q. J. Roy. Meteorol. Soc., 141, 333–349, https://doi.org/10.1002/qj.2388, 2015. a, b
Weaver, A. T., Deltel, C., Machu, É., Ricci, S., and Daget, N.: A multivariate balance operator for variational ocean data assimilation, Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, Appl. Meteorol. Phys. Oceanogr., 131, 3605–3625, https://doi.org/10.1256/qj.05.119, 2005. a
Weaver, A. T., Tshimanga, J., and Piacentini, A.: Correlation operators based on an implicitly formulated diffusion equation solved with the Chebyshev iteration, Q. J. Roy. Meteorol. Soc., 142, 455–471, https://doi.org/10.1002/qj.2664, 2016. a
While, J. and Martin, M. J.: Variational bias correction of satellite sea-surface temperature data incorporating observations of the bias, Q. J. Roy. Meteorol. Soc., 145, 2733–2754, https://doi.org/10.1002/qj.3590, 2019. a
Short summary
We use simulations of our ocean forecasting system to compare the impact of additional altimeter observations from two proposed future satellite constellations. We found that, in our system, an altimeter constellation of 12 nadir altimeters produces improved predictions of sea surface height, surface currents, temperature, and salinity compared to a constellation of 2 wide-swath altimeters.
We use simulations of our ocean forecasting system to compare the impact of additional altimeter...