Articles | Volume 15, issue 4
https://doi.org/10.5194/os-15-1133-2019
© Author(s) 2019. 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-15-1133-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Aug 2019
Research article |
| 27 Aug 2019
| 27 Aug 2019
The impact of a new high-resolution ocean model on the Met Office North-West European Shelf forecasting system
Marina Tonani
CORRESPONDING AUTHOR
Met Office, Exeter, EX1 3PB, UK
Peter Sykes
Met Office, Exeter, EX1 3PB, UK
Robert R. King
Met Office, Exeter, EX1 3PB, UK
Niall McConnell
Met Office, Exeter, EX1 3PB, UK
Anne-Christine Péquignet
Met Office, Exeter, EX1 3PB, UK
Enda O'Dea
Met Office, Exeter, EX1 3PB, UK
Jennifer A. Graham
Centre for Environment, Fisheries and Aquaculture Science, Lowestoft, NR33 0HT, UK
Jeff Polton
National Oceanography Centre, Liverpool, L3 5DA, UK
John Siddorn
Met Office, Exeter, EX1 3PB, UK
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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
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Huw W. Lewis, Juan Manuel Castillo Sanchez, Jennifer Graham, Andrew Saulter, Jorge Bornemann, Alex Arnold, Joachim Fallmann, Chris Harris, David Pearson, Steven Ramsdale, Alberto Martínez-de la Torre, Lucy Bricheno, Eleanor Blyth, Victoria A. Bell, Helen Davies, Toby R. Marthews, Clare O'Neill, Heather Rumbold, Enda O'Dea, Ashley Brereton, Karen Guihou, Adrian Hines, Momme Butenschon, Simon J. Dadson, Tamzin Palmer, Jason Holt, Nick Reynard, Martin Best, John Edwards, and John Siddorn
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In the real world the atmosphere, oceans and land surface are closely interconnected, and yet prediction systems tend to treat them in isolation. Those feedbacks are often illustrated in natural hazards, such as when strong winds lead to large waves and coastal damage, or when prolonged rainfall leads to saturated ground and high flowing rivers. For the first time, we have attempted to represent some of the feedbacks between sky, sea and land within a high-resolution forecast system for the UK.
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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
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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.
Giovanni Coppini, Palmalisa Marra, Rita Lecci, Nadia Pinardi, Sergio Cretì, Mario Scalas, Luca Tedesco, Alessandro D'Anca, Leopoldo Fazioli, Antonio Olita, Giuseppe Turrisi, Cosimo Palazzo, Giovanni Aloisio, Sandro Fiore, Antonio Bonaduce, Yogesh Vittal Kumkar, Stefania Angela Ciliberti, Ivan Federico, Gianandrea Mannarini, Paola Agostini, Roberto Bonarelli, Sara Martinelli, Giorgia Verri, Letizia Lusito, Davide Rollo, Arturo Cavallo, Antonio Tumolo, Tony Monacizzo, Marco Spagnulo, Rorberto Sorgente, Andrea Cucco, Giovanni Quattrocchi, Marina Tonani, Massimiliano Drudi, Paola Nassisi, Laura Conte, Laura Panzera, Antonio Navarra, and Giancarlo Negro
Nat. Hazards Earth Syst. Sci., 17, 533–547, https://doi.org/10.5194/nhess-17-533-2017, https://doi.org/10.5194/nhess-17-533-2017, 2017
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SeaConditions aims to support the users by providing the environmental information in due time and with adequate accuracy in the marine and coastal environments, enforcing users' sea situational awareness. SeaConditions consists of a web and mobile application for the provision of meteorological and oceanographic observation and forecasting products. The iOS/Android apps were downloaded by more than 105 000 users and more than 100 000 users have visited the web version (www.sea-conditions.com).
Jason Holt, Patrick Hyder, Mike Ashworth, James Harle, Helene T. Hewitt, Hedong Liu, Adrian L. New, Stephen Pickles, Andrew Porter, Ekaterina Popova, J. Icarus Allen, John Siddorn, and Richard Wood
Geosci. Model Dev., 10, 499–523, https://doi.org/10.5194/gmd-10-499-2017, https://doi.org/10.5194/gmd-10-499-2017, 2017
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Accurately representing coastal and shelf seas in global ocean models is one of the grand challenges of Earth system science. Here, we explore what the options are for improving this by exploring what the important physical processes are that need to be represented. We use a simple scale analysis to investigate how large the resulting models would need to be. We then compare this with how computer power is increasing to provide estimates of when this might be feasible in the future.
Jun She, Icarus Allen, Erik Buch, Alessandro Crise, Johnny A. Johannessen, Pierre-Yves Le Traon, Urmas Lips, Glenn Nolan, Nadia Pinardi, Jan H. Reißmann, John Siddorn, Emil Stanev, and Henning Wehde
Ocean Sci., 12, 953–976, https://doi.org/10.5194/os-12-953-2016, https://doi.org/10.5194/os-12-953-2016, 2016
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This white paper addresses key scientific challenges and research priorities for the development of operational oceanography in Europe for the next 5–10 years. Knowledge gaps and deficiencies are identified in relation to common scientific challenges in four EuroGOOS knowledge areas: European ocean observations, modelling and forecasting technology, coastal operational oceanography, and operational ecology.
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
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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.
M. Haller, F. Janssen, J. Siddorn, W. Petersen, and S. Dick
Ocean Sci., 11, 879–896, https://doi.org/10.5194/os-11-879-2015, https://doi.org/10.5194/os-11-879-2015, 2015
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Automated measurement systems called FerryBox are installed on cargo ships in the North Sea. Operational model forecasts have been compared to FerryBox data of water temperature and salinity. We wanted to know how well the simulations agree with the observations. We found out that water temperature simulation gives satisfying results, while salinity simulation still could be improved. It turned out that assimilation of observational data into operational models gives strong benefits.
A. Megann, D. Storkey, Y. Aksenov, S. Alderson, D. Calvert, T. Graham, P. Hyder, J. Siddorn, and B. Sinha
Geosci. Model Dev., 7, 1069–1092, https://doi.org/10.5194/gmd-7-1069-2014, https://doi.org/10.5194/gmd-7-1069-2014, 2014
Cited articles
Berg, P. and Weismann Poulsen, J.: Implementation details for HBM, DMI
Technical Report No. 12-11, available at:
https://www.dmi.dk/fileadmin/Rapporter/TR/tr12-11.pdf (last access: 15 August 2019), 2012.
Bloom, S. C., Takacs, L. L., da Silva, A. M., and Ledvina, D.: Data Assimilation Using Incremental Analysis Updates, Mon. Weather Rev., 124, 1256–1271, https://doi.org/10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2, 1996.
Brassington, G.: Forecast Errors, Goodness, and Verification in Ocean
Forecasting, J. Mar. Res., 75, 403–433, https://doi.org/10.1357/002224017821836851, 2017.
Burchard, H. and Bolding, K.: GETM, A General Estuarine Transport Model:
Scientific Documentation, Tech. Rep. EUR 20253 EN, Eur. Comm., Ispra (VA), Italy, available at:
https://publications.europa.eu/en/publication-detail/-/publication/5506bf19-e076-4d4b-8648-dedd06efbb38
(last access: 15 August 2019), 2002.
Burrows, M. and Thorpe, S. A.: Drifter observations of the Hebrides slope
current and nearby circulation patterns, Ann. Geophys., 17, 280–302, 1999.
Butterworth, S.: On the theory of Filter Amplifiers, in: Experimental Wireless and the Wireless Engineer, Vol. 7, Ilife & Sons Limited, London, 536–541, September 1930.
Dick, S., Eckard, K., Müller-Navarra, S., Klein, H., and Komo, H.: The
operational circulation model of BSH (BSHcmod) – model description and
validation, Berichte des Bundesamtes für Seeschifffahrt und Hydrographie (BSH) 29, Bundesamt für Seeschifffahrt und Hydrographie, Hamburg, Rostock, available at:
https://www.bsh.de/DE/PUBLIKATIONEN/_Anlagen/Downloads/Meer_und_Umwelt/Berichte-des-BSH/Berichte-des-BSH-56.html;jsessionid=A6966DF5C4E4B4E8DEE7DA32C7930E6A.live11292?nn=1907948
(last access: 15 August 2019), 2001.
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system, Remote Sens. Environ., 116, 140–158, 2012.
Egbert, G. D. and Erofeeva, S. Y.: Efficient Inverse Modeling of Barotropic Ocean Tides. J. Atmos. Ocean. Tech., 19, 183–204, https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2, 2002.
Fairall, C. W., Bradley, E. F., Hare, J. E., Grachev, A. A., and Edson, J. B.: Bulk Parameterization of Air–Sea Fluxes: Updates and Verification for the COARE Algorithm, J. Climate, 16, 571–591, https://doi.org/10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2, 2003.
Flather, R. A.: A tidal model of the north west European continental shelf,
Memoires de la Société Royale de Sciences de Liege, 6, 141–164, 1976.
Flather, R. A.: Results from a model of the north east Atlantic relating to
the Norwegian Coastal Current. The Norwegian Coastal Current (Proceedings
from the symposium, Geilo, 9–12 September 1980), Bergen University,
Bergen, 2, 427–458, 1981.
Good, S. A., Martin, M. A., and Rayner, N. A.: EN4: quality controlled ocean
temperature and salinity profiles and monthly objective analyses with
uncertainty estimates, J. Geophys. Res.-Oceans, 118, 6704–7616, 2013.
Graham, J. A., O'Dea, E., Holt, J., Polton, J., Hewitt, H. T., Furner, R., Guihou, K., Brereton, A., Arnold, A., Wakelin, S., Castillo Sanchez, J. M., and Mayorga Adame, C. G.: AMM15: a new high-resolution NEMO configuration for operational simulation of the European north-west shelf, Geosci. Model Dev., 11, 681–696, https://doi.org/10.5194/gmd-11-681-2018, 2018a.
Graham, J., Rosser, J. P., O'dea, E., and Hewitt, H. T.: Resolving the Shelf
Break Exchange Around the European Northwest Shelf, Geophys. Res. Lett., 45,
https://doi.org/10.1029/2018GL079399, 2018b.
Guihou, K., Polton, J., Wakelin, S., O'Dea, E., and Holt, J.: Kilometric Scale modeling of the North West European Shelf Seas: Exploring the spatial
and temporal variability of internal tides, J. Geophys. Res.-Oceans, 123,
688–707, https://doi.org/10.1002/2017JC012960, 2017.
Gurgel, K. W., Schlick, T., Voulgaris, G., Seemann, J., and Ziemer, F.: HF radar observations in the German Bight: Measurements and quality control, in: 2011 IEEE/OES 10th Current, Waves and Turbulence Measurements (CWTM), March 2011, Monterey, California, USA, 51–56, 2011.
Holt, J., Hyder, P., Ashworth, M., Harle, J., Hewitt, H. T., Liu, H., New, A. L., Pickles, S., Porter, A., Popova, E., Allen, J. I., Siddorn, J., and Wood, R.: Prospects for improving the representation of coastal and shelf seas in global ocean models, Geosci. Model Dev., 10, 499–523, https://doi.org/10.5194/gmd-10-499-2017, 2017.
Holt, J. T. and James, I. D.: An s coordinate density evolving model of the
northwest European continental shelf: 1. Model description and density
structure, J. Geophys. Res.-Oceans, 106, 14015–14034, https://doi.org/10.1029/2000JC000304, 2001.
Holt, J. T. and Umlauf, L.: Modelling the tidal mixing fronts and seasonal stratification of the Northwest European Continental shelf, Cont. Shelf Res., 28, 887–903, https://doi.org/10.1016/j.csr.2008.01.012, 2008.
Ikeda, M., Johannessen, J. A., Lyge, K., and Sandven, S.: A Process Study of
Mesoscale Meanders and Eddies in the Norwegian Coastal Current, J. Phys.
Oceanogr., 19, 20–35, 1989.
Ingleby, B. and Huddlestone, M.: Quality control of ocean temperature and
salinity profiles – Historical and real-time data, J. Mar. Syst., 65, 158–175, https://doi.org/10.1016/j.jmarsys.2005.11.019, 2007.
Kara, A. B., Rochford, P. A., and Hurlburt, H. E.: An optimal definition for
ocean mixed layer depth, J. Geophys. Res., 105, 16803–16821,
https://doi.org/10.1029/2000JC900072, 2000.
King, 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, 2018.
Large, W. and Yeager, S.: The global climatology of an interannuallly varying air-sea data set, Clim. Dynam., 33, 341–364, 2009.
Lea, D. J., Drecourt, J. P., Haines, K., and Martin, M. J.: Ocean altimeter
assimilation with observational- and model-bias correction, Q. J. Roy.
Meteorol. Soc., 134, 1761–1774, https://doi.org/10.1002/qj.320.2008, 2008.
Le Traon, P. Y., Ali, A., Alvarez Fanjul, E., Aouf, L., Axell, L., Aznar, R.,
Ballarotta, M., Behrens, A., Benkiran, M., Bentamy, A., Bertino, L., Bowyer,
P., Brando, Breivik, L. A., Buongiorno Nardelli, B., Cailleau, S., Ciliberti,
S. A., Clementi, E., Colella, S., Mc Connell, N., Coppini, G., Cossarini, G.,
Dabrowski, T., de Alfonso Alonso-Muñoyerro, M., O'Dea, E., Desportes, C., Dinessen, F., Drevillon, M., Drillet, Y., Drudi, M., Dussurget, R., Faugère, Y., Forneris, V., Fratianni, C., Le Galloudec, O.,
García-Hermosa, M. I., García Sotillo, M., Garnesson, P., Garric, G., Golbeck, I., Gourrion, J., Grégoire, M. L., Guinehut, S., Gutknecht, E.,
Harris, C., Hernandez, F., Huess, V., Johannessen, J. A., Kay, S., Killick, R. King, R., de Kloe, J., Korres, G., Lagemaa, P., Lecci, R., Legeais, J. F.,
Lellouche, J. M., Levier, B., Lorente, P., Mangin, A., Martin, M., Melet, A.,
Murawski, J., Özsoy, E., Palazov, A., Pardo, S., Parent, L., Pascual, A., Paul, J., Peneva, E., Perruche, C.,, Peterson, D., Petit de la Villeon, L., Pinardi, N., Pouliquen, S., Pujol, M. I., Rainaud, R., Rampal, P., Reffray, G., Regnier, C., Reppucci, A., Ryan, A., Salon, S., Samuelsen, A., Santoleri, R., Saulter, A., She, J., Solidoro, C., Stanev, E., Staneva, J., Stoffelen, A., Storto, A., Sykes, P., Szekely, T., Taburet, G., Taylor, B., Tintore, J., Toledano, C., Tonani, M., Tuomi, L., Volpe, G., Wedhe, H., Williams, T., Vandendbulcke, L., van Zanten, D., von Schuckmann, K., Xie, J., Zacharioudaki, A., and Zuo, H.: The Copernicus Marine Environmental Monitoring Service: Main Scientific Achievements and Future Prospects, Special Issue Mercator Océan Journal, 56, 46–50, https://doi.org/10.25575/56, 2017.
Lewis, H., Castillo Sanchez, J. M., Siddorn, J., King, R., Tonani, M., Saulter, A., Sykes, P., Péquignet, A.-C., Weedon, G., Palmer, T., Staneva, J., and Bricheno, L.: Can wave coupling improve operational regional ocean forecasts for the North-West European Shelf, Ocean Sci., 15, 669–690,
https://doi.org/10.5194/os-15-669-2019, 2019a.
Lewis, H., Siddorn, J., Castillo Sanchez, J. M., Petch, J., Edwards, J., and
Smyth, T.: Evaluating the impact of atmospheric forcing resolution and
air–sea coupling on near-coastal regional ocean prediction, Ocean Sci., 15,
761–778, https://doi.org/10.5194/os-15-761-2019, 2019b.
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.
Madec, G. and the NEMO team: NEMO ocean engine, Note du Pôle de
modélisation, No. 27, Institut Pierre-Simon Laplace (IPSL), France,
ISSN 1288-1619, 2016.
Marsh, R., Haigh, I. D., Cunningham, S. A., Inall, M. E., Porter, M., and Moat, B. I.: Large-scale forcing of the European Slope Current and associated inflows to the North Sea, Ocean Sci., 13, 315–335, https://doi.org/10.5194/os-13-315-2017, 2017.
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.
Mogensen, K, Balmaseda, M. A., and Weaver, A.: The NEMOVAR ocean data
assimilation system as implemented in the ECMWF ocean analysis for System 4,
European Centre for Medium-Range Weather Forecasts, Reading, UK,
https://doi.org/10.21957/x5y9yrtm, 2012.
O'Dea, E. J., Arnold, A. K., Edwards, K. P., Furner, R., Hyder, P., Martin, M. J., Siddorn, J. R., Storkey, D., While, J., Holt, J. T., and Liu, H.: An
operational ocean forecast system incorporating NEMO and SST data
assimilation for the tidally driven European North-West shelf, J. Oper.
Oceanogr., 5, 3–17, https://doi.org/10.1080/1755876X.2012.11020128, 2012.
O'Dea, E., Furner, R., Wakelin, S., Siddorn, J., While, J., Sykes, P., King, R., Holt, J., and Hewitt, H.: The CO5 configuration of the 7 km Atlantic Margin Model: large-scale biases and sensitivity to forcing, physics options and vertical resolution, Geosci. Model Dev., 10, 2947–2969,
https://doi.org/10.5194/gmd-10-2947-2017, 2017.
Polton, J. A.: Tidally induced mean flow over bathymetric features: a
contemporary challenge for high-resolution wide-area models, Geophys. Astro.
Fluid, 109, 1–9, https://doi.org/10.1080/03091929.2014.952726, 2014.
Port, A., Gurgel, K. W., Staneva, J., Schulz-Stellenfleth, J., and Stanev, E.
V.: Tidal and wind-driven surface currents in the German Bight: HFR
observations versus model simulations, Ocean Dynam., 61, 1567–1585, 2011.
Rio, M. H., Guinehut, S., and Larnicol, G.: New CNES-CLS09 global mean dynamic topography computed from the combination of GRACE data, altimetry and in situ measurements, J. Geophys. Res., 116, C07018, https://doi.org/10.1029/2010JC006505, 2011.
Schrum, C.: Thermohaline stratification and instabilities at tidal mixing
fronts. Results of an eddy resolving model for the German Bight, Cont. Shelf
Res., 17, 689–716, 1997.
She, J., Allen, I., Buch, E., Crise, A., Johannessen, J. A., Le Traon, P.-Y., Lips, U., Nolan, G., Pinardi, N., Reissman, J. H., Siddorn, J., Stanev, E., and Wehde, H.: Developing European operational oceanography for Blue Growth, climate change adaptation and mitigation, and ecosystem-based management, Ocean Sci., 12 953–976, https://doi.org/10.5194/os-12-953-2016, 2016.
Siddorn, J. R. and Furner, R: An analytical stretching function that combines the best attributes of geopotential and terrain-following vertical coordinates, Ocean Model., 66, 1–13, 2013.
Siddorn, J. R., Allen, J. I., Blackford, J. C., Gilbert, F. J., Holt, J. T.,
Holt, M. W., Osborne, J. P., Proctor, R., and Mills, D. K.: Modelling the
hydrodynamics and ecosystem of the North-West European continental shelf for
operational oceanography, J. Mar. Syst., 65, 417–429, https://doi.org/10.1016/j.jmarsys.2006.01.018, 2007.
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.
Simpson, J. H. and Hill, A. E.: The Scottish Coastal Current, in: The Role of Freshwater Outflow in Coastal Marine Ecosystems, NATO ASI Series (Series G: Ecological Sciences), vol. 7, edited by: Skreslet, S., Springer, Berlin, Heidelberg, 1986.
Storkey, D., Blockley, E. W., Furner, R., Guiavarc'h, C., Lea, D., Martin, M. J., Barciela, R. M., Hines, A., Hyder, P., and Siddorn, J. R.: Forecasting the ocean state using NEMO: The new FOAM system, J. Oper. Oceanogr., 3, 3–15, 2010.
Tinker, J., Krijnen, J., Wood, R., Barciela, R., and Dye, S. R.: What are the prospects for seasonal prediction of the marine environment of the North-west European Shelf?, Ocean Sci., 14, 887–909, https://doi.org/10.5194/os-14-887-2018, 2018.
Tonani, M., McConnell, N., King, R., O'Dea, E., Martin, M., Sykes, P., Ryan,
A., Saulter, A., and Kay, S.: North-West European Shelf Monitoring and
Forecasting Centre: system evolution since the beginning of CMEMS, in:
EurooGOOS-2017 Conference Proceedings, 3–4 October 2017, Bergen, Norway, 311–318, 2017.
Umlauf, L. and Burchard, H.: A generic length-scale equation for geophysical
turbulence models, J. Mar. Res., 61, 235–265, 2003.
Vörösmarty, J., Green, P., Salisbury, J., and Lammers, R. B.: Global Water Resources: Vulnerability from Climate Change and Population Growth, Science, 289, 284–288, https://doi.org/10.1126/science.289.5477.284, 2000.
Waters, J., Lea, D. J., Martin, M. J., Mirouze, I., Weaver, A., and While, J.: Implementing a variational data assimilation system in an operational
1∕4 degree global ocean model, Q. J. Roy. Meteorol. Soc., 141, 333–349, B
https://doi.org/10.1002/qj.2388, 2015.
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.
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., https://doi.org/10.1002/qj.3590, in press, 2019.
Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert, D., Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P., Ineson, S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S. F., Rae, J. G. L., Roberts, M. J., Scaife, A. A., Schiemann, R., Storkey, D., Thorpe, L., Watterson, I. G., Walters, D. N., West, A., Wood, R. A., Woolings, T., and Xavier, P. K.: The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations, J. Adv. Model Earth Syst., 10, 357–380,
https://doi.org/10.1002/2017MS001115, 2018.
WMO: Guide to meteorological instruments and methods of observation, 2008 Edn., available at: http://www.wmo.int/pages/prog/www/IMOP/CIMO-Guide.html (last access: 15 August 2019), 2010.
Young, E. F. and Holt, J. T.: Prediction and analysis of long-term variability of temperature and salinity in the Irish Sea, J. Geophys. Res.-Oceans, 112, 1–18, https://doi.org/10.1029/2005JC003386, 2007.
Zalesak, S. T.: Fully Multidimensional Flux-Corrected Transport Algorithms
for Fluids, J. Comput. Phys., 31, 335–362, 1979.
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.
A new high-resolution ocean model at 1.5 km has replaced the 7 km system for delivering...
