Articles | Volume 26, issue 7
https://doi.org/10.5194/nhess-26-3185-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-3185-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Representation of meteotsunamis in km-scale regional simulations coupled at high frequency
Nefeli Makrygianni
CORRESPONDING AUTHOR
Met Office, Fitzroy Road, EX1 3PB, Exeter, UK
Ségolène Berthou
Met Office, Fitzroy Road, EX1 3PB, Exeter, UK
David L. A. Flack
Met Office, Fitzroy Road, EX1 3PB, Exeter, UK
Cindy Lebeaupin Brossier
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Jonathan Beuvier
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Juan Manuel Castillo
Met Office, Fitzroy Road, EX1 3PB, Exeter, UK
Emiliano Renzi
Mathematics of Complex and Nonlinear Phenomena, School of Engineering, Physics and Mathematics, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK
Clare O'Neill
Met Office, Fitzroy Road, EX1 3PB, Exeter, UK
Daniel Peláez-Zapata
Centre Borelli, École normale supérieure Paris-Saclay, Gif-sur-Yvette, France
Frederic Dias
Centre Borelli, École normale supérieure Paris-Saclay, Gif-sur-Yvette, France
Huw Lewis
Met Office, Fitzroy Road, EX1 3PB, Exeter, UK
Diego Bruciaferri
Met Office, Fitzroy Road, EX1 3PB, Exeter, UK
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Samira Khodayar, Silvio Davolio, Paolo Di Girolamo, Cindy Lebeaupin Brossier, Emmanouil Flaounas, Nadia Fourrie, Keun-Ok Lee, Didier Ricard, Benoit Vie, Francois Bouttier, Alberto Caldas-Alvarez, and Veronique Ducrocq
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High coastal total water levels (TWLs) can lead to flooding and hazardous conditions for coastal communities and environment. In this research we are using numerical models to study the interactions between the three main components of the TWL (waves, tides, and surges) on UK and Irish coasts during winter 2013/14. The main finding of this research is that extreme waves and surges can indeed happen together, even at high tide, but they often occurred simultaneously 2–3 h before high tide.
César Sauvage, Cindy Lebeaupin Brossier, and Marie-Noëlle Bouin
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Air–sea processes are key elements during Mediterranean heavy precipitation events. We aim to progress in their representation in high-resolution weather forecast. Using coupled ocean–air–wave simulations, we investigated air–sea mechanisms modulated by ocean and waves during a case that occurred in southern France. Results showed significant impact of the forecast on low-level dynamics and air–sea fluxes and illustrated potential benefits of coupled numerical weather prediction systems.
Jennifer Saxby, Julia Crook, Simon Peatman, Cathryn Birch, Juliane Schwendike, Maria Valdivieso da Costa, Juan Manuel Castillo Sanchez, Chris Holloway, Nicholas P. Klingaman, Ashis Mitra, and Huw Lewis
Weather Clim. Dynam. Discuss., https://doi.org/10.5194/wcd-2021-46, https://doi.org/10.5194/wcd-2021-46, 2021
Preprint withdrawn
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This study assesses the ability of the new Met Office IND1 numerical model to simulate tropical cyclones and their associated hazards, such as high winds and heavy rainfall. The new system consists of both atmospheric and oceanic models coupled together, allowing us to explore the sensitivity of cyclones to important air–sea feedbacks. We find that the model can accurately simulate tropical cyclone position, structure, and intensity, which are crucial for predicting and mitigating hazards.
Cited articles
Angove, M., Kozlosky, L., Chu, P., Dusek, G., Mann, G., Anderson, E., Gridley, J., Arcas, D., Titov, V., Eble, M., McMahon, K., Hirsch, B., and Zaleski, W.: Addressing the meteotsunami risk in the United States, Nat. Hazards, 106, 1467–1487, https://doi.org/10.1007/s11069-020-04499-3, 2021. a
Ardhuin, F., Rogers, E., Babanin, A., Filipot, J.-F., Magne, R., Roland, A., Westhuysen, A. V. D., Queffeulou, P., Lefevre, J.-M., Aouf, L., and Collard, F.: Semiempirical Dissipation Source Functions for Ocean Waves. Part I: Definition, Calibration, and Validation, J. Phys. Oceanogr., 40, 1917–1941, https://doi.org/10.1175/2010JPO4324.1, 2010. a
Battjes, J. A. and Janssen, J. P. F. M.: Energy loss and set-up due to breaking random waves, in: Proceedings of the 16th International Conference on Coastal Engineering, pp. 569–587, American Society of Civil Engineers, Hamburg, Germany, https://repository.tudelft.nl/record/uuid:2fba43fe-f8bd-42ac-85ee-848312d2e27e (last access: 7 July 2026), 1978. a
Berthou, S., Siddorn, J., Fraser-Leonhardt, V., Le Traon, P.-Y., and Hoteit, I.: Towards Earth system modelling: coupled ocean forecasting, in: Ocean prediction: present status and state of the art (OPSR), edited by: Álvarez Fanjul, E., Ciliberti, S. A., Pearlman, J., Wilmer-Becker, K., and Behera, S., Copernicus Publications, State Planet, 5-opsr, 20, https://doi.org/10.5194/sp-5-opsr-20-2025, 2025. a, b, c, d, e
Berthou, S., Castillo, J. M., Fraser-Leonhardt, V., Mahmood, S., Makrygianni, N., Arnold, A., Sanchez, C., Lewis, H. W., Partridge, D., Best, M., Bricheno, L., Davies, H., Clark, D., Clark, J. R., Polton, J., Saulter, A., Short, C. J., Tinker, J., and Tucker, S.: Km-scale regional coupled system for the Northwest European shelf for weather and climate applications: RCS-UKC4, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-6216, 2026. a, b, c, d
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Blayo, E. and Debreu, L.: Revisiting open boundary conditions from the point of view of characteristic variables, Ocean Modell., 9, 231–252, https://doi.org/10.1016/j.ocemod.2004.07.001, 2005. a
Bouin, M.-N. and Lebeaupin Brossier, C.: Surface processes in the 7 November 2014 medicane from air–sea coupled high-resolution numerical modelling, Atmos. Chem. Phys., 20, 6861–6881, https://doi.org/10.5194/acp-20-6861-2020, 2020. a, b
Brousseau, P., Seity, Y., Ricard, D., and Léger, J.: Improvement of the forecast of convective activity from the AROME-France system, Q. J. Roy. Meteorol. Soc., 142, 2231–2243, https://doi.org/10.1002/qj.2822, 2016. a
Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J., and Shelly, A.: Unified Modeling and Prediction of Weather and Climate: A 25-Year Journey, B. Am. Meteorol. Soc., 93, 1865–1877, https://doi.org/10.1175/BAMS-D-12-00018.1, 2012. a
Bush, M., Allen, T., Bain, C., Boutle, I., Edwards, J., Finnenkoetter, A., Franklin, C., Hanley, K., Lean, H., Lock, A., Manners, J., Mittermaier, M., Morcrette, C., North, R., Petch, J., Short, C., Vosper, S., Walters, D., Webster, S., Weeks, M., Wilkinson, J., Wood, N., and Zerroukat, M.: The first Met Office Unified Model–JULES Regional Atmosphere and Land configuration, RAL1, Geosci. Model Dev., 13, 1999–2029, https://doi.org/10.5194/gmd-13-1999-2020, 2020. a
Bush, M., Boutle, I., Edwards, J., Finnenkoetter, A., Franklin, C., Hanley, K., Jayakumar, A., Lewis, H., Lock, A., Mittermaier, M., Mohandas, S., North, R., Porson, A., Roux, B., Webster, S., and Weeks, M.: The second Met Office Unified Model–JULES Regional Atmosphere and Land configuration, RAL2, Geosci. Model Dev., 16, 1713–1734, https://doi.org/10.5194/gmd-16-1713-2023, 2023. a
Bush, M., Flack, D. L. A., Lewis, H. W., Bohnenstengel, S. I., Short, C. J., Franklin, C., Lock, A. P., Best, M., Field, P., McCabe, A., Van Weverberg, K., Berthou, S., Boutle, I., Brooke, J. K., Cole, S., Cooper, S., Dow, G., Edwards, J., Finnenkoetter, A., Furtado, K., Halladay, K., Hanley, K., Hendry, M. A., Hill, A., Jayakumar, A., Jones, R. W., Lean, H., Lee, J. C. K., Malcolm, A., Mittermaier, M., Mohandas, S., Moore, S., Morcrette, C., North, R., Porson, A., Rennie, S., Roberts, N., Roux, B., Sanchez, C., Su, C.-H., Tucker, S., Vosper, S., Walters, D., Warner, J., Webster, S., Weeks, M., Wilkinson, J., Whitall, M., Williams, K. D., and Zhang, H.: The third Met Office Unified Model–JULES Regional Atmosphere and Land Configuration, RAL3, Geosci. Model Dev., 18, 3819–3855, https://doi.org/10.5194/gmd-18-3819-2025, 2025. a, b
Carrere, L., Lyard, F., Cancet, M., and Guillot, A.: FES 2014, a new tidal model on the global ocean with enhanced accuracy in shallow seas and in the Arctic region, in: EGU General Assembly Conference Abstracts, EGU General Assembly Conference Abstracts, p. 5481, https://meetingorganizer.copernicus.org/EGU2015/EGU2015-5481-1.pdf (last access: 7 July 2026), 2015. a
Chan, S. C., Kendon, E. J., Berthou, S., Fosser, G., Lewis, E., and Fowler, H. J.: Europe-wide precipitation projections at convection permitting scale with the Unified Model, Clim. Dynam., 55, 409–428, https://doi.org/10.1007/s00382-020-05192-8, 2020. a
Charnock, H.: Wind stress on a water surface, Q. J. Roy. Meteorol. Soc., 81, 639–640, https://doi.org/10.1002/qj.49708135027, 1955. a
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4, 701–722, https://doi.org/10.5194/gmd-4-701-2011, 2011. a
Courtier, P., Freydier, C., Geleyn, J.-F., Rabier, F., and Rochas, M.: The Arpege project at Meteo France, Ph.D. thesis, Shinfield Park, Reading, https://www.ecmwf.int/en/elibrary/74049-arpege-project-meteo-france (last access: 7 July 2026), 1991. a
Craig, A., Valcke, S., and Coquart, L.: Development and performance of a new version of the OASIS coupler, OASIS3-MCT_3.0, Geosci. Model Dev., 10, 3297–3308, https://doi.org/10.5194/gmd-10-3297-2017, 2017. a
Cuxart, J., Bougeault, P., and Redelsperger, J. L.: A turbulence scheme allowing for mesoscale and large-eddy simulations, Q. J. Roy. Meteorol. Soc., 126, 1–30, https://doi.org/10.1002/qj.49712656202, 2000. a
Edwards, J. M. and Slingo, A.: Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model, Q. J. Roy. Meteorol. Soc., 122, 689–719, 1996. a
Ferrarin, C., Orlić, M., Bajo, M., Davolio, S., Umgiesser, G., and Lionello, P.: The contribution of a mesoscale cyclone and associated meteotsunami to the exceptional flood in Venice on November 12, 2019, Q. J. Roy. Meteorol. Soc., 149, 2929–2942, https://doi.org/10.1002/qj.4539, 2023. a
Flather, R.: A tidal model of the northwest European continental shelf, Mém. Soc. Roy. Sci. Liège, 10, 141–164, 1976. a
Fouquart, Y. and Bonnel, B.: Computation of Solar Heating of the Earth's Atmosphere: A New Parameterization, Beitr. Phys. Atmos., 53, 35–62, http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=PASCAL8030435628 (last access: 7 July 2026), 1980. a
Gentile, E. S., Gray, S. L., and Lewis, H. W.: The sensitivity of probabilistic convective-scale forecasts of an extratropical cyclone to atmosphere–ocean–wave coupling, Q. J. Roy. Meteorol. Soc., 148, 685–710, https://doi.org/10.1002/qj.4225, 2022. a
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, 2018. a
Greenspan, M.: Propagation of Sound in Five Monatomic Gases, Acoust. Soc. Am. J., 28, 644, https://doi.org/10.1121/1.1908432, 1956. a
Hagelin, S., Roberts, N., Thompson, S. A., Beare, R., and Bush, M.: The Met Office convective‐scale ensemble, MOGREPS‐UK, Q. J. Roy. Meteorol. Soc., 143, 2846–2861, https://doi.org/10.1002/qj.3135, 2017. a
Haslett, S. K., Mellor, H. E., and Bryant, E. A.: Meteo-tsunami hazard associated with summer thunderstorms in the United Kingdom, Phys. Chem. Earth, 34, 1016–1022, https://doi.org/10.1016/j.pce.2009.10.005, 2009. a
Hasselmann, K., Barnett, T., Bouws, E., Carlson, H., Cartwright, D., Enke, K., Ewing, J., Gienapp, H., Hasselmann, D., Kruseman, P., Meerburg, A., Müller, P., Olbers, D., Richter, K., Sell, W., and Walden, H.: Measurements of Wind-Wave Growth and Swell Decay during the Joint North Sea Wave Project (JONSWAP), Ergänzungsheft zur Deutschen Hydrographischen Zeitschrift, Reihe A, 12, https://pure.mpg.de/rest/items/item_3262854_4/component/file_3282032/content (last access: 7 July 2026), 1973. a
Huang, C., Anderson, E., Liu, Y., Ma, G., Mann, G., and Xue, P.: Evaluating essential processes and forecast requirements for meteotsunami-induced coastal flooding, Nat.Hazards, 110, 1693–1718, https://doi.org/10.1007/s11069-021-05007-x, 2021. a
Kahraman, A., Kendon, E. J., Chan, S. C., and Fowler, H. J.: Quasi-Stationary Intense Rainstorms Spread Across Europe Under Climate Change, Geophys. Res. Lett., 48, e92361, https://doi.org/10.1029/2020GL092361, 2021. a
Kain, J. S. and Fritsch, J. M.: A One-Dimensional Entraining/Detraining Plume Model and Its Application in Convective Parameterization, J. Atmos. Sci., 47, 2784–2802, https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2, 1990. a
Kim, J. and Omira, R.: Combined surge-meteotsunami dynamics: A numerical model for hurricane Leslie on the coast of Portugal, Ocean Modell., 189, 102368, https://doi.org/10.1016/j.ocemod.2024.102368, 2024. a
Kim, M.-S., Woo, S.-B., Eom, H., and You, S. H.: Occurrence of pressure-forced meteotsunami events in the eastern Yellow Sea during 2010–2019, Nat. Hazards Earth Syst. Sci., 21, 3323–3337, https://doi.org/10.5194/nhess-21-3323-2021, 2021. a
Leclair, M. and Madec, G.: A conservative leapfrog time stepping method, Ocean Modell., 30, 88–94, https://doi.org/10.1016/j.ocemod.2009.06.006, 2009. a
Lewis, H. W., Castillo Sanchez, J. M., Arnold, A., Fallmann, J., Saulter, A., Graham, J., Bush, M., Siddorn, J., Palmer, T., Lock, A., Edwards, J., Bricheno, L., Martínez-de la Torre, A., and Clark, J.: The UKC3 regional coupled environmental prediction system, Geosci. Model Dev., 12, 2357–2400, https://doi.org/10.5194/gmd-12-2357-2019, 2019. a, b, c
Levier, B., Reffray, G., Escudier, R., Gutknecht, E., Amo-Baladrón, A., Ciliberti, S., Aznar, R., and Sotillo, M. G.: Quality Information Document for IBI-MFC Ocean Reanalysis Product, Tech. Rep. CMEMS-IBI-QUID-005-001, Copernicus Marine Environment Monitoring Service (CMEMS), https://documentation.marine.copernicus.eu/QUID/CMEMS-IBI-QUID-005-001.pdf (last access: July 2025), 2021. a
Madec, G. and the NEMO team: NEMO Ocean Engine, Paris, France, version 3.6 stable, https://frouingroup.ucsd.edu/NEMO_ORCA2_LIM3_PISCES/Refs/NEMO_books/NEMO_book_3.6_STABLE-2016.pdf (last access: 7 July 2026), 2016. a
Madec, G., Bell, M., Blaker, A., Bricaud, C., Bruciaferri, D., Castrillo, M., Calvert, D., Chanut, J., Clementi, E., Coward, A., Epicoco, I., Éthé, C., Ganderton, J., Harle, J., Hutchinson, K., Iovino, D., Lea, D., Lovato, T., Martin, M., Martin, N., Mele, F., Martins, D., Masson, S., Mathiot, P., Mele, F., Mocavero, S., Müller, S., Nurser, A. G., Paronuzzi, S., Peltier, M., Person, R., Rousset, C., Rynders, S., Samson, G., Téchené, S., Vancoppenolle, M., and Wilson, C.: NEMO Ocean Engine Reference Manual, Zenodo, https://doi.org/10.5281/zenodo.8167700, 2023. a
Makrygianni, N.: Scripts and Data for Meteotsunami prediction in km-scale regional systems coupled at high frequency, Zenodo [data set and code], https://doi.org/10.5281/zenodo.16370112, 2025. a, b
Manners, J., Edwards, J. M., Hill, P., and Thelen, J.-C.: SOCRATES (Suite Of Community Radiative Transfer codes based on Edwards and Slingo) Technical Guide, Met Office, UK, https://github.com/MetOffice/socrates/ (last access: 7 July 2026), 2018. a
Masson, V.: A Physically-Based Scheme For The Urban Energy Budget In Atmospheric Models, Bound.-Lay. Meteorol., 94, 357–397, https://doi.org/10.1023/A:1002463829265, 2000. a
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geoscientific Model Development, 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, 2013. a
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997. a
NOC: UK National Tide Gauge Network | National Tidal and Sea Level Facility, https://ntslf.org/tides/uk-network, last access: 23 July 2025. a
Noilhan, J. and Planton, S.: A Simple Parameterization of Land Surface Processes for Meteorological Models, Mon. Weather Rev., 117, 536, https://doi.org/10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2, 1989. a
O'Brien, L., Dudley, J. M., and Dias, F.: Extreme wave events in Ireland: 14 680 BP–2012, Nat. Hazards Earth Syst. Sci., 13, 625–648, https://doi.org/10.5194/nhess-13-625-2013, 2013. a
O'Brien, L., Renzi, E., Dudley, J. M., Clancy, C., and Dias, F.: Catalogue of extreme wave events in Ireland: revised and updated for 14680 BP to 2017, Nat. Hazards Earth Syst. Sci., 18, 729–758, https://doi.org/10.5194/nhess-18-729-2018, 2018. a
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. a, b, c, d, e
Pinty, J.-P. and Jabouille, P.: A mixed-phase cloud parameterization for use in a mesoscale non-hydrostatic model: simulations of a squall line and of orographic precipitations, in: Proceedings of the Conference on Cloud Physics, American Meteorological Society, Everett, WA, USA, http://mesonh.aero.obs-mip.fr/mesonh/dir_publication/pinty_jabouille_ams_ccp1998.pdf (last access: 7 July 2026), 1998. a
Proudman, J.: The Effects on the Sea of Changes in Atmospheric Pressure, Geophys. J., 2, 197–209, https://doi.org/10.1111/j.1365-246X.1929.tb05408.x, 1929. a, b
Renault, L., Marchesiello, P., Masson, S., and McWilliams, J.: Remarkable Control of Western Boundary Currents by Eddy Killing, a Mechanical Air-Sea Coupling Process, Geophys. Res. Lett., 46, 2743–2751, https://doi.org/10.1029/2018GL081211, 2019. a
Roehrig, R., Beau, I., Saint-Martin, D., Alias, A., Decharme, B., Guérémy, J.-F., Voldoire, A., Abdel-Lathif, A. Y., Bazile, E., Belamari, S., Blein, S., Bouniol, D., Bouteloup, Y., Cattiaux, J., Chauvin, F., Chevallier, M., Colin, J., Douville, H., Marquet, P., Michou, M., Nabat, P., Oudar, T., Peyrillé, P., Piriou, J.-M., Salas y Mélia, D., Séférian, R., and Sénési, S.: The CNRM Global Atmosphere Model ARPEGE-Climat 6.3: Description and Evaluation, J. Adv. Model. Earth Syst., 12, e2020MS002075, https://doi.org/10.1029/2020MS002075, 2020. a
Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P., Bouttier, F., Lac, C., and Masson, V.: The AROME-France Convective-Scale Operational Model, Mon. Weather Rev., 139, 976–991, https://doi.org/10.1175/2010MWR3425.1, 2011. a
Sibley, A.: Meteotsunamis reported around Britain and Ireland, and northern France, 18–19 June 2022, Weather, 77, 279–280, https://doi.org/10.1002/wea.4271, 2022. a, b, c
Swan, L. D., McCarthy, G. D., and Bergin, C.: Investigating the occurrence of an unusual tidal event along the north–west coast of Ireland, Nat. Hazards, 122, 517, https://doi.org/10.1007/s11069-026-08287-3, 2026. a
Tang, Y., Lean, H. W., and Bornemann, J.: The benefits of the Met Office variable resolution NWP model for forecasting convection, Meteorol. Appl., 20, 417–426, https://doi.org/10.1002/met.1300, 2013. a
Tappin, D. R., Sibley, A., Horsburgh, K., Daubord, C., Cox, D., and Long, D.: The English Channel “tsunami” of 27 June 2011 – a probable meteorological source, Weather, 68, 144–152, https://doi.org/10.1002/wea.2061, 2013. a
Taylor, J. P., Edwards, J. M., Glew, M. D., Hignett, P., and Slingo, A.: Studies with a flexible new radiation code. II: Comparisons with aircraft short-wave observations, Q. J. Roy. Meteorol. Soc., 122, 839–861, https://doi.org/10.1002/qj.49712253204, 1996. a
Titov, V. and Moore, C.: Meteotsunami model forecast: can coastal hazard be quantified in real time?, Nat. Hazards, 106, 1545–1561, https://doi.org/10.1007/s11069-020-04450-6, 2021. a
Tolman, H. L. and the WWIII development group: User Manual and System Documentation of WAVEWATCH III® Version 4.18, College Park, MD, USA, mMAB Contribution No. 316, https://polar.ncep.noaa.gov/waves/wavewatch/manual.v4.18.pdf (last access: 7 July 2026), 2014. 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, b, c, d, e
Valcke, S., Craig, T., and Coquart, L.: OASIS3-MCT User Guide, OASIS3-MCT 3.0, Toulouse, France, cERFACS TR/CMGC/15/38, https://cerfacs.fr/oa4web/oasis3-mct_3.0/oasis3mct_UserGuide.pdf (last access: 7 July 2026), 2015. a
Valcke, S., Craig, T., Maisonnave, E., and Coquart, L.: OASIS3-MCT User Guide, OASIS3-MCT 5.0, Toulouse, France, cERFACS TR/CMGC/21/161, https://cnrs.hal.science/hal-04739698v1/document (last access: 7 July 2026), 2021. a
Valiente, N. G., Saulter, A., Edwards, J. M., Lewis, H. W., Sanchez, J. M. C., Bruciaferri, D., Bunney, C., and Siddorn, J.: The Impact of Wave Model Source Terms and Coupling Strategies to Rapidly Developing Waves across the North-West European Shelf during Extreme Events, J. Mar. Sci. Eng., 9, 403, https://doi.org/10.3390/jmse9040403, 2021. a
Valiente, N. G., Saulter, A., Gomez, B., Bunney, C., Li, J.-G., Palmer, T., and Pequignet, C.: The Met Office operational wave forecasting system: the evolution of the regional and global models, Geosci. Model Dev., 16, 2515–2538, https://doi.org/10.5194/gmd-16-2515-2023, 2023. a
Vergados, P., Krishnamoorthy, S., Martire, L., Mrak, S., Komjáthy, A., Morton, Y. T. J., and Vilibić, I.: Prospects for meteotsunami detection in earth’s atmosphere using GNSS observations, GPS Sol., 27, 169, https://doi.org/10.1007/s10291-023-01492-8, 2023. a
Vilibić, I., Monserrat, S., Rabinovich, A., and Mihanović, H.: Numerical Modelling of the Destructive Meteotsunami of 15 June, 2006 on the Coast of the Balearic Islands, Pure Appl. Geophys., 165, 2169–2195, https://doi.org/10.1007/s00024-008-0426-5, 2008. a, b
Vilibić, I., Šepić, J., Rabinovich, A. B., and Monserrat, S.: Modern Approaches in Meteotsunami Research and Early Warning, Front. Mar. Sci., 3, https://doi.org/10.3389/fmars.2016.00057, 2016. a
Vilibić, I., Rabinovich, A. B., and Anderson, E. J.: Special issue on the global perspective on meteotsunami science: editorial, Nat. Hazards, 106, 1087–1104, https://doi.org/10.1007/s11069-021-04679-9, 2021. a, b
Villalonga, J., Monserrat, S., Gomis, D., and Jordà, G.: Observational Characterization of Atmospheric Disturbances Generating Meteotsunamis in the Balearic Islands, J. Geophys. Res.-Oceans, 129, e2024JC020910, https://doi.org/10.1029/2024JC020910, 2024. a
Voldoire, A., Decharme, B., Pianezze, J., Lebeaupin Brossier, C., Sevault, F., Seyfried, L., Garnier, V., Bielli, S., Valcke, S., Alias, A., Accensi, M., Ardhuin, F., Bouin, M.-N., Ducrocq, V., Faroux, S., Giordani, H., Léger, F., Marsaleix, P., Rainaud, R., Redelsperger, J.-L., Richard, E., and Riette, S.: SURFEX v8.0 interface with OASIS3-MCT to couple atmosphere with hydrology, ocean, waves and sea-ice models, from coastal to global scales, Geosci. Model Dev., 10, 4207–4227, https://doi.org/10.5194/gmd-10-4207-2017, 2017. a
Šepić, J., Vilibić, I., and Monserrat, S.: Quantifying the probability of meteotsunami occurrence from synoptic atmospheric patterns, Geophys. Res. Lett., 43, 10377–10384, https://doi.org/10.1002/2016GL070754, 2016. a
Walters, D. N., Best, M. J., Bushell, A. C., Copsey, D., Edwards, J. M., Falloon, P. D., Harris, C. M., Lock, A. P., Manners, J. C., Morcrette, C. J., Roberts, M. J., Stratton, R. A., Webster, S., Wilkinson, J. M., Willett, M. R., Boutle, I. A., Earnshaw, P. D., Hill, P. G., MacLachlan, C., Martin, G. M., Moufouma-Okia, W., Palmer, M. D., Petch, J. C., Rooney, G. G., Scaife, A. A., and Williams, K. D.: The Met Office Unified Model Global Atmosphere 3.0/3.1 and JULES Global Land 3.0/3.1 configurations, Geosci. Model Dev., 4, 919–941, https://doi.org/10.5194/gmd-4-919-2011, 2011. a
Wijeratne, E. M. S. and Pattiaratchi, C. B.: Meteotsunamis Generated by Thunderstorms, J. Geophys. Res.-Oceans, 129, e2023JC020662, https://doi.org/10.1029/2023JC020662, 2024. a, b, c
Williams, D. A., Schultz, D. M., Horsburgh, K. J., and Hughes, C. W.: An 8-yr Meteotsunami Climatology across Northwest Europe: 2010–17, J. Phys. Oceanogr., 51, 1145–1161, https://doi.org/10.1175/JPO-D-20-0175.1, 2021. a
Wunsch, C. and Stammer, D.: Atmospheric loading and the oceanic “inverted barometer” effect, Rev. Geophys., 35, 79–107, https://doi.org/10.1029/96RG03037, 1997. a
Short summary
Meteotsunamis are rare but dangerous anomalous waves triggered by atmospheric disturbances, they are not currently forecast in Northwest Europe. We analysed the strongest recorded event on June 18, 2022, which reached 1 m amplitude. We showed high-resolution, high-frequency coupled models can represent such events up to three days ahead and help better understand their atmospheric triggers. These models, together with improved observations, can enhance early warnings and coastal safety.
Meteotsunamis are rare but dangerous anomalous waves triggered by atmospheric disturbances, they...
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