Stochastic Surrogate Model for Meteotsunami Early Warning System in the Eastern Adriatic Sea

The meteotsunami early warning system prototype using stochastic surrogate approach and running operationally in the eastern Adriatic Sea is presented. First, the atmospheric internal gravity waves (IGWs) driving the meteotsunamis are either forecasted with stateâ ofâ theâ art deterministic models a...

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Published in:Journal of Geophysical Research: Oceans
Main Authors: Denamiel, Cléa, Šepić, Jadranka, Huan, Xun, Bolzer, Célia, Vilibić, Ivica
Format: Article in Journal/Newspaper
Language:unknown
Published: Coastal Processes Research Group, School of Marine Science and Engineering, Plymouth University 2019
Subjects:
eastern Adriatic
extreme seaâ level hazard assessment
meteotsunami early warning system
Atmospheric and Oceanic Sciences
Geological Sciences
Science
The Cryosphere
Online Access:https://hdl.handle.net/2027.42/152998
https://doi.org/10.1029/2019JC015574
id ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/152998
record_format openpolar
institution Open Polar
collection University of Michigan: Deep Blue
op_collection_id ftumdeepblue
language unknown
topic eastern Adriatic
extreme seaâ level hazard assessment
meteotsunami early warning system
Atmospheric and Oceanic Sciences
Geological Sciences
Science
spellingShingle eastern Adriatic
extreme seaâ level hazard assessment
meteotsunami early warning system
Atmospheric and Oceanic Sciences
Geological Sciences
Science
Denamiel, Cléa
Šepić, Jadranka
Huan, Xun
Bolzer, Célia
Vilibić, Ivica
Stochastic Surrogate Model for Meteotsunami Early Warning System in the Eastern Adriatic Sea
topic_facet eastern Adriatic
extreme seaâ level hazard assessment
meteotsunami early warning system
Atmospheric and Oceanic Sciences
Geological Sciences
Science
description The meteotsunami early warning system prototype using stochastic surrogate approach and running operationally in the eastern Adriatic Sea is presented. First, the atmospheric internal gravity waves (IGWs) driving the meteotsunamis are either forecasted with stateâ ofâ theâ art deterministic models at least a day in advance or detected through measurements at least 2 hr before the meteotsunami reaches sensitive locations. The extreme seaâ level hazard forecast at endangered locations is then derived with an innovative stochastic surrogate modelâ implemented with generalized polynomial chaos expansion (gPCE) method and synthetic IGWs forcing a barotropic ocean modelâ used with the input parameters extracted from deterministic model results and/or measurements. The evaluation of the system, both against five historical events and for all the detected potential meteotsunamis since late 2018 when the early warning system prototype became operational, reveals that the meteotsunami hazard is conservatively assessed but often overestimated at some locations. Despite some needed improvements and developments, this study demonstrates that gPCEâ based methods can be used for atmospherically driven extreme seaâ level hazard assessment and in geosciences in wide.Plain Language SummaryAtmospherically driven extreme seaâ level events are one of the major threats to people and assets in the coastal regions. Assessing the hazard associated with such events together with uncertainty quantification in a precise and timely manner is thus of primary importance in modern societies. In this study, an early warning system for the eastern Adriatic meteotsunamis, destructive long waves with periods from few minutes up to an hour generated by traveling atmospheric disturbances, is presented and evaluated. The system is based on stateâ ofâ theâ art deterministic atmospheric and ocean models as well as an innovative statistical model developed to forecast the meteotsunami hazard. The evaluation reveals that the meteotsunami hazard is ...
format Article in Journal/Newspaper
author Denamiel, Cléa
Šepić, Jadranka
Huan, Xun
Bolzer, Célia
Vilibić, Ivica
author_facet Denamiel, Cléa
Šepić, Jadranka
Huan, Xun
Bolzer, Célia
Vilibić, Ivica
author_sort Denamiel, Cléa
title Stochastic Surrogate Model for Meteotsunami Early Warning System in the Eastern Adriatic Sea
title_short Stochastic Surrogate Model for Meteotsunami Early Warning System in the Eastern Adriatic Sea
title_full Stochastic Surrogate Model for Meteotsunami Early Warning System in the Eastern Adriatic Sea
title_fullStr Stochastic Surrogate Model for Meteotsunami Early Warning System in the Eastern Adriatic Sea
title_full_unstemmed Stochastic Surrogate Model for Meteotsunami Early Warning System in the Eastern Adriatic Sea
title_sort stochastic surrogate model for meteotsunami early warning system in the eastern adriatic sea
publisher Coastal Processes Research Group, School of Marine Science and Engineering, Plymouth University
publishDate 2019
url https://hdl.handle.net/2027.42/152998
https://doi.org/10.1029/2019JC015574
genre The Cryosphere
genre_facet The Cryosphere
op_relation Denamiel, Cléa
Šepić, Jadranka
Huan, Xun; Bolzer, Célia
Vilibić, Ivica (2019). "Stochastic Surrogate Model for Meteotsunami Early Warning System in the Eastern Adriatic Sea." Journal of Geophysical Research: Oceans 124(11): 8485-8499.
2169-9275
2169-9291
https://hdl.handle.net/2027.42/152998
doi:10.1029/2019JC015574
Journal of Geophysical Research: Oceans
Sivakumar, B. ( 2008 ). Undermining the science or undermining Nature? Hydrological Processes, 22 ( 6 ), 893 â 897. https://doi.org/10.1002/hyp.7004
OrliÄ , M., BeluÅ¡iÄ , D., JanekoviÄ , I., & PasariÄ , M. ( 2010 ). Fresh evidence relating the great Adriatic surge of 21 June 1978 to mesoscale atmospheric forcing. Journal of Geophysical Research, 115, C06011. https://doi.org/10.1029/2009JC005777
Pattiaratchi, C., & Wijeratne, E. M. S. ( 2014 ). Observations of meteorological tsunamis along the southâ west Australia. Natural Hazards, 74 ( 1 ), 281 â 303. https://doi.org/10.1007/s11069â 014â 1263â 8
Pellikka, H., Rauhala, J., Kahma, K. K., Tapani, S., Boman, H., & Kangas, A. ( 2014 ). Recent observations of meteotsunamis on the Finnish coast. Natural Hazards, 74 ( 1 ), 197 â 215. https://doi.org/10.1007/s11069â 014â 1150â 3
Renault, L., Vizoso, G., Jansà , A., Wilkin, J., & Tintoré, J. ( 2011 ). Toward the predictability of meteotsunamis in the Balearic Sea using regional nested atmosphere and ocean models. Geophysical Research Letters, 38, L10601. https://doi.org/10.1029/2011gl047361
Rupert, C., & Miller, C. ( 2007 ). An analysis of polynomial chaos approximations for modeling singleâ fluidâ phase flow in porous medium systems. Journal of Computational Physics, 226 ( 2 ), 2175 â 2205. https://doi.org/10.1016/j.jcp.2007.07.001
Salaree, A., Mansouri, R., & Okal, E. A. ( 2018 ). The intriguing tsunami of 19 March 2017 at Bandar Dayyer, Iran: Field survey and simulations. Natural Hazards, 90 ( 3 ), 1277 â 1307. https://doi.org/10.1007/s11069â 017â 3119â 5
Å epiÄ , J., MeÄ ugorac, I., JanekoviÄ , I., DuniÄ , N., & VilibiÄ , I. ( 2016 ). Multiâ meteotsunami event in the Adriatic Sea generated by atmospheric disturbances of 25â 26 June 2014. Pure and Applied Geophysics, 173 ( 12 ), 4117 â 4138. https://doi.org/10.1007/s00024â 016â 1249â 4
Å epiÄ , J., VilibiÄ , I., Beg Paklar, G., DadiÄ , V., Denamiel, C., DuniÄ , N., et al. ( 2017 ). Towards understanding and operational early warning of the Adriatic meteotsunamis: Project MESSI. Revue Paralia, 10, 177 â 182. https://doi.org/10.5150/cmcm.2017.033
Å epiÄ , J., VilibiÄ , I., & Strelec MahoviÄ , N. ( 2012 ). Northern Adriatic meteorological tsunamis: Observations, link to the atmosphere, and predictability. Journal of Geophysical Research, 117, C02002. https://doi.org/10.1029/2011JC007608
Shchepetkin, A. F., & McWilliams, J. C. ( 2005 ). The regional oceanic modeling system: A splitâ explicit, freeâ surface, topographyâ followingâ coordinate ocean model. Ocean Modelling, 9 ( 4 ), 347 â 404. https://doi.org/10.1016/j.ocemod.2004.08.002
Shchepetkin, A. F., & McWilliams, J. C. ( 2009 ). Correction and commentary for â Ocean forecasting in terrainâ following coordinates: Formulation and skill assessment of the regional ocean modeling systemâ by Haidvogel et al., J. Comput. Phys., 227, pp. 3595â 3624. Journal of Computational Physics, 228 ( 24 ), 8985 â 9000. https://doi.org/10.1016/j.jcp.2009.09.002
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang, W., & Powers, J. G. ( 2005 ). A description of the advanced research WRF Version 2. NCAR Technical Note NCAR/TNâ 468+STR. https://doi.org/10.5065/D6DZ069T
Smolyak, S. A. ( 1963 ). Quadrature and interpolation formulas for tensor products of certain classes of functions. Doklady Akademii Nauk SSSR, 148 ( 5 ), 1042 â 1045.
Soize, C., & Ghanem, R. G. ( 2004 ). Physical systems with random uncertainties: Chaos representations with arbitrary probability measure. SIAM Journal on Scientific Computing, 26 ( 2 ), 395 â 410. https://doi.org/10.1137/S1064827503424505
Sraj, I., Mandli, K., Knio, O., Dawson, C. N., & Hoteit, I. ( 2014 ). Uncertainty quantification and inference of Manning’s friction coefficients using DART buoy during the Tohoku tsunami. Ocean Modelling, 83, 82 â 97. https://doi.org/10.1016/j.ocemod.2014.09.001
Tanaka, K. ( 2010 ). Atmospheric pressureâ wave bands around a cold front resulted in a meteoâ tsunami in the East China Sea in February 2009. Natural Hazards and Earth System Sciences, 10 ( 12 ), 2599 â 2610. https://doi.org/10.5194/nhessâ 10â 2599â 2010
VilibiÄ , I., & Å epiÄ , J. ( 2009 ). Destructive meteotsunamis along the eastern Adriatic coast: Overview. Physics and Chemistry of the Earth, 34 ( 17â 18 ), 904 â 917. https://doi.org/10.1016/j.pce.2009.08.004
VilibiÄ , I., Å epiÄ , J., Rabinovich, A. B., & Monserrat, S. ( 2016 ). Modern approaches in meteotsunami research and early warning. Frontiers in Marine Science, 3 ( 57 ). https://doi.org/10.3389/fmars.2016.00057
VuÄ etiÄ , T., VilibiÄ , I., Tinti, S., & Maramai, A. ( 2009 ). The Great Adriatic flood of 21 June 1978 revisited: An overview of the reports. Physics and Chemistry of the Earth, 34 ( 17â 18 ), 894 â 903. https://doi.org/10.1016/j.pce.2009.08.005
Wang, M., Wan, Z., & Huang, Q. ( 2016 ). A new uncertain analysis method for the prediction of acoustic field with random and interval parameters. Shock and Vibration, 16 pp, 2016, 1 â 16. https://doi.org/10.1155/2016/3693262
Warner, J. C., Armstrong, B., He, R., & Zambon, J. B. ( 2010 ). Development of a Coupled Oceanâ Atmosphereâ Waveâ Sediment Transport (COAWST) modeling system. Ocean Modelling, 35 ( 3 ), 230 â 244. https://doi.org/10.1016/j.ocemod.2010.07.010
Whitmore, P., & White, B. ( 2014 ). Meteotsunami forecasting: Sensitivities demonstrated by the 2008 Boothbay, Maine, event. Natural Hazards, 74 ( 1 ), 11 â 23. https://doi.org/10.1007/s11069â 014â 1056â 0
Xiu, D., & Karniadakis, G. E. ( 2002 ). The Wienerâ Askey polynomial chaos for stochastic differential equations. SIAM Journal on Scientific Computing, 24 ( 2 ), 619 â 644. https://doi.org/10.1137/S1064827501387826
Le Maître, O. P., & Knio, O. M. ( 2010 ). Spectral methods for uncertainty quantification: With applications to computational fluid dynamics. Springer Science & Business Media. https://doi.org/10.1007/978â 90â 481â 3520â 2
Linares, A., Wu, C. H., Bechle, A. J., Anderson, E. J., & Kristovich, D. A. R. ( 2019 ). Scientific Reports, 9 ( 1 ), 2105. https://doi.org/10.1038/s41598â 019â 38716â 2
Luettich, R. A., Birkhahn, R. H., & Westerink, J. J. ( 1991 ). Application of ADCIRCâ 2DDI to Masonboro Inlet. A brief numerical modeling study. Contractors Report to the US Army Engineer Waterways Experiment Station: North Carolina. August, 1991
Arnst, M., & Ponthot, J.â P. ( 2014 ). An overview of nonintrusive characterization, propagation, and sensitivity analysis of uncertainties in computational mechanics. International Journal for Uncertainty Quantification, 4 ( 5 ), 387 â 421. https://doi.org/10.1615/int.j.uncertaintyquantification.2014006990
Beven, K. J. ( 2006 ). On undermining the science? Hydrological Processes, 20 ( 3 ), 141 â 146.
Bulthuis, K., Arnst, M., Sun, S., & Pattyn, F. ( 2019 ). Uncertainty quantification of the multiâ centennial response of the Antarctic ice sheet to climate change. The Cryosphere, 13 ( 4 ), 1349 â 1380. https://doi.org/10.5194/tcâ 13â 1349â 2019
Burkardt J. ( 2014 ). Slow exponential growth for Gauss Patterson sparse grids, Proc. 12th Int. Coastal Symposium. http://people.sc.fsu.edu/~jburkardt/presentations/sgmga_gps.pdf
Cho, K.â H., Choi, J.â Y., Park K.â S., Hyun S.â K., & Park J.â Y. ( 2013 ). A synoptic study on tsunamiâ like sea level oscillations along the west coast of Korea using an unstructuredâ grid ocean model. In Conley, D. C., Masselink, G., Russell, P. E., & O’Hare, T. J. (Eds.), Proc. 12th Int. Coastal Symposium (Vol. 1, pp. 678 â 683 ). Plymouth, MN: Coastal Processes Research Group, School of Marine Science and Engineering, Plymouth University.
Denamiel, C., Å epiÄ , J., IvankoviÄ , D., & VilibiÄ , I. ( 2019 ). The Adriatic Sea and Coast modelling suite: Evaluation of the meteotsunami forecast component. Ocean Modelling, 135, 71 â 93. https://doi.org/10.1016/j.ocemod.2019.02.003
Denamiel, C., Å epiÄ , J., & VilibiÄ , I. ( 2018 ). Impact of geomorphological changes to harbor resonance during meteotsunamis: The Vela Luka Bay Test Case. Pure and Applied Geophysics, 175 ( 11 ), 3839 â 3859. https://doi.org/10.1007/s00024â 018â 1862â 5
Dusek, G., DiVeglio, C., Licate, L., Heilman, L., Kirk, K., Paternostro, C., & Miller, A. ( 2019 ). A meteotsunami climatology along the U.S. East Coast. Bulletin of the American Meteorological Society, 100 ( 7 ), 1329 â 1345. https://doi.org/10.1175/BAMSâ Dâ 18â 0206.1
Ewing, M., Press, F., & Donn, W. L. ( 1954 ). An explanation of the Lake Michigan wave of 26 June 1954. Science, 120 ( 3122 ), 684 â 686. https://doi.org/10.1126/science.120.3122.684
Foo, J., Yosibash, Z., & Karniadakis, G. E. ( 2007 ). Stochastic simulation of riserâ sections with uncertain measured pressure loads and/or uncertain material properties. Computer Methods in Applied Mechanics and Engineering, 196 ( 41â 44 ), 4250 â 4271. https://doi.org/10.1016/j.cma.2007.04.005
Formaggia, L., Guadagnini, A., Imperiali, I., Lever, V., Porta, G., Riva, M., Scotti, A., & Tamellini, L. ( 2013 ). Global sensitivity analysis through polynomial chaos expansion of a basinâ scale geochemical compaction model. Computational Geosciences, 17 ( 1 ), 25 â 42. https://doi.org/10.1007/s10596â 012â 9311â 5
Ghanem, R., Higdon, R., & Owhadi, H. ( 2017 ). Handbook of uncertainty quantification. Springer. https://doi.org/10.1007/978â 3â 319â 12385â 1
Giraldi, L., Le Maître, O. P., Mandli, K. T., Dawson, C. N., Hoteit, I., & Knio, O. M. ( 2017 ). Bayesian inference of earthquake parameters from buoy data using a polynomial chaosâ based surrogate. Computational Geosciences, 21 ( 4 ), 683 â 699. https://doi.org/10.1007/s10596â 017â 9646â z
Hibiya, T., & Kajiura, K. ( 1982 ). Origin of the Abiki phenomenon (a kind of seiche) in Nagasaki Bay. Journal of the Oceanographic Society of Japan, 38 ( 3 ), 172 â 182. https://doi.org/10.1007/BF02110288
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spelling ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/152998 2023-08-20T04:10:08+02:00 Stochastic Surrogate Model for Meteotsunami Early Warning System in the Eastern Adriatic Sea Denamiel, Cléa Šepić, Jadranka Huan, Xun Bolzer, Célia Vilibić, Ivica 2019-11 application/pdf https://hdl.handle.net/2027.42/152998 https://doi.org/10.1029/2019JC015574 unknown Coastal Processes Research Group, School of Marine Science and Engineering, Plymouth University Wiley Periodicals, Inc. Denamiel, Cléa Šepić, Jadranka Huan, Xun; Bolzer, Célia Vilibić, Ivica (2019). "Stochastic Surrogate Model for Meteotsunami Early Warning System in the Eastern Adriatic Sea." Journal of Geophysical Research: Oceans 124(11): 8485-8499. 2169-9275 2169-9291 https://hdl.handle.net/2027.42/152998 doi:10.1029/2019JC015574 Journal of Geophysical Research: Oceans Sivakumar, B. ( 2008 ). Undermining the science or undermining Nature? Hydrological Processes, 22 ( 6 ), 893 â 897. https://doi.org/10.1002/hyp.7004 OrliÄ , M., BeluÅ¡iÄ , D., JanekoviÄ , I., & PasariÄ , M. ( 2010 ). Fresh evidence relating the great Adriatic surge of 21 June 1978 to mesoscale atmospheric forcing. Journal of Geophysical Research, 115, C06011. https://doi.org/10.1029/2009JC005777 Pattiaratchi, C., & Wijeratne, E. M. S. ( 2014 ). Observations of meteorological tsunamis along the southâ west Australia. Natural Hazards, 74 ( 1 ), 281 â 303. https://doi.org/10.1007/s11069â 014â 1263â 8 Pellikka, H., Rauhala, J., Kahma, K. K., Tapani, S., Boman, H., & Kangas, A. ( 2014 ). Recent observations of meteotsunamis on the Finnish coast. Natural Hazards, 74 ( 1 ), 197 â 215. https://doi.org/10.1007/s11069â 014â 1150â 3 Renault, L., Vizoso, G., Jansà , A., Wilkin, J., & Tintoré, J. ( 2011 ). Toward the predictability of meteotsunamis in the Balearic Sea using regional nested atmosphere and ocean models. Geophysical Research Letters, 38, L10601. https://doi.org/10.1029/2011gl047361 Rupert, C., & Miller, C. ( 2007 ). An analysis of polynomial chaos approximations for modeling singleâ fluidâ phase flow in porous medium systems. Journal of Computational Physics, 226 ( 2 ), 2175 â 2205. https://doi.org/10.1016/j.jcp.2007.07.001 Salaree, A., Mansouri, R., & Okal, E. A. ( 2018 ). The intriguing tsunami of 19 March 2017 at Bandar Dayyer, Iran: Field survey and simulations. Natural Hazards, 90 ( 3 ), 1277 â 1307. https://doi.org/10.1007/s11069â 017â 3119â 5 Å epiÄ , J., MeÄ ugorac, I., JanekoviÄ , I., DuniÄ , N., & VilibiÄ , I. ( 2016 ). Multiâ meteotsunami event in the Adriatic Sea generated by atmospheric disturbances of 25â 26 June 2014. Pure and Applied Geophysics, 173 ( 12 ), 4117 â 4138. https://doi.org/10.1007/s00024â 016â 1249â 4 Å epiÄ , J., VilibiÄ , I., Beg Paklar, G., DadiÄ , V., Denamiel, C., DuniÄ , N., et al. ( 2017 ). Towards understanding and operational early warning of the Adriatic meteotsunamis: Project MESSI. Revue Paralia, 10, 177 â 182. https://doi.org/10.5150/cmcm.2017.033 Å epiÄ , J., VilibiÄ , I., & Strelec MahoviÄ , N. ( 2012 ). Northern Adriatic meteorological tsunamis: Observations, link to the atmosphere, and predictability. Journal of Geophysical Research, 117, C02002. https://doi.org/10.1029/2011JC007608 Shchepetkin, A. F., & McWilliams, J. C. ( 2005 ). The regional oceanic modeling system: A splitâ explicit, freeâ surface, topographyâ followingâ coordinate ocean model. Ocean Modelling, 9 ( 4 ), 347 â 404. https://doi.org/10.1016/j.ocemod.2004.08.002 Shchepetkin, A. F., & McWilliams, J. C. ( 2009 ). Correction and commentary for â Ocean forecasting in terrainâ following coordinates: Formulation and skill assessment of the regional ocean modeling systemâ by Haidvogel et al., J. Comput. Phys., 227, pp. 3595â 3624. Journal of Computational Physics, 228 ( 24 ), 8985 â 9000. https://doi.org/10.1016/j.jcp.2009.09.002 Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang, W., & Powers, J. G. ( 2005 ). A description of the advanced research WRF Version 2. NCAR Technical Note NCAR/TNâ 468+STR. https://doi.org/10.5065/D6DZ069T Smolyak, S. A. ( 1963 ). Quadrature and interpolation formulas for tensor products of certain classes of functions. Doklady Akademii Nauk SSSR, 148 ( 5 ), 1042 â 1045. Soize, C., & Ghanem, R. G. ( 2004 ). Physical systems with random uncertainties: Chaos representations with arbitrary probability measure. SIAM Journal on Scientific Computing, 26 ( 2 ), 395 â 410. https://doi.org/10.1137/S1064827503424505 Sraj, I., Mandli, K., Knio, O., Dawson, C. N., & Hoteit, I. ( 2014 ). Uncertainty quantification and inference of Manning’s friction coefficients using DART buoy during the Tohoku tsunami. Ocean Modelling, 83, 82 â 97. https://doi.org/10.1016/j.ocemod.2014.09.001 Tanaka, K. ( 2010 ). Atmospheric pressureâ wave bands around a cold front resulted in a meteoâ tsunami in the East China Sea in February 2009. Natural Hazards and Earth System Sciences, 10 ( 12 ), 2599 â 2610. https://doi.org/10.5194/nhessâ 10â 2599â 2010 VilibiÄ , I., & Å epiÄ , J. ( 2009 ). Destructive meteotsunamis along the eastern Adriatic coast: Overview. Physics and Chemistry of the Earth, 34 ( 17â 18 ), 904 â 917. https://doi.org/10.1016/j.pce.2009.08.004 VilibiÄ , I., Å epiÄ , J., Rabinovich, A. B., & Monserrat, S. ( 2016 ). Modern approaches in meteotsunami research and early warning. Frontiers in Marine Science, 3 ( 57 ). https://doi.org/10.3389/fmars.2016.00057 VuÄ etiÄ , T., VilibiÄ , I., Tinti, S., & Maramai, A. ( 2009 ). The Great Adriatic flood of 21 June 1978 revisited: An overview of the reports. Physics and Chemistry of the Earth, 34 ( 17â 18 ), 894 â 903. https://doi.org/10.1016/j.pce.2009.08.005 Wang, M., Wan, Z., & Huang, Q. ( 2016 ). A new uncertain analysis method for the prediction of acoustic field with random and interval parameters. Shock and Vibration, 16 pp, 2016, 1 â 16. https://doi.org/10.1155/2016/3693262 Warner, J. C., Armstrong, B., He, R., & Zambon, J. B. ( 2010 ). Development of a Coupled Oceanâ Atmosphereâ Waveâ Sediment Transport (COAWST) modeling system. Ocean Modelling, 35 ( 3 ), 230 â 244. https://doi.org/10.1016/j.ocemod.2010.07.010 Whitmore, P., & White, B. ( 2014 ). Meteotsunami forecasting: Sensitivities demonstrated by the 2008 Boothbay, Maine, event. Natural Hazards, 74 ( 1 ), 11 â 23. https://doi.org/10.1007/s11069â 014â 1056â 0 Xiu, D., & Karniadakis, G. E. ( 2002 ). The Wienerâ Askey polynomial chaos for stochastic differential equations. SIAM Journal on Scientific Computing, 24 ( 2 ), 619 â 644. https://doi.org/10.1137/S1064827501387826 Le Maître, O. P., & Knio, O. M. ( 2010 ). Spectral methods for uncertainty quantification: With applications to computational fluid dynamics. Springer Science & Business Media. https://doi.org/10.1007/978â 90â 481â 3520â 2 Linares, A., Wu, C. H., Bechle, A. J., Anderson, E. J., & Kristovich, D. A. R. ( 2019 ). Scientific Reports, 9 ( 1 ), 2105. https://doi.org/10.1038/s41598â 019â 38716â 2 Luettich, R. A., Birkhahn, R. H., & Westerink, J. J. ( 1991 ). Application of ADCIRCâ 2DDI to Masonboro Inlet. A brief numerical modeling study. Contractors Report to the US Army Engineer Waterways Experiment Station: North Carolina. August, 1991 Arnst, M., & Ponthot, J.â P. ( 2014 ). An overview of nonintrusive characterization, propagation, and sensitivity analysis of uncertainties in computational mechanics. International Journal for Uncertainty Quantification, 4 ( 5 ), 387 â 421. https://doi.org/10.1615/int.j.uncertaintyquantification.2014006990 Beven, K. J. ( 2006 ). On undermining the science? Hydrological Processes, 20 ( 3 ), 141 â 146. Bulthuis, K., Arnst, M., Sun, S., & Pattyn, F. ( 2019 ). Uncertainty quantification of the multiâ centennial response of the Antarctic ice sheet to climate change. The Cryosphere, 13 ( 4 ), 1349 â 1380. https://doi.org/10.5194/tcâ 13â 1349â 2019 Burkardt J. ( 2014 ). Slow exponential growth for Gauss Patterson sparse grids, Proc. 12th Int. Coastal Symposium. http://people.sc.fsu.edu/~jburkardt/presentations/sgmga_gps.pdf Cho, K.â H., Choi, J.â Y., Park K.â S., Hyun S.â K., & Park J.â Y. ( 2013 ). A synoptic study on tsunamiâ like sea level oscillations along the west coast of Korea using an unstructuredâ grid ocean model. In Conley, D. C., Masselink, G., Russell, P. E., & O’Hare, T. J. (Eds.), Proc. 12th Int. Coastal Symposium (Vol. 1, pp. 678 â 683 ). Plymouth, MN: Coastal Processes Research Group, School of Marine Science and Engineering, Plymouth University. Denamiel, C., Å epiÄ , J., IvankoviÄ , D., & VilibiÄ , I. ( 2019 ). The Adriatic Sea and Coast modelling suite: Evaluation of the meteotsunami forecast component. Ocean Modelling, 135, 71 â 93. https://doi.org/10.1016/j.ocemod.2019.02.003 Denamiel, C., Å epiÄ , J., & VilibiÄ , I. ( 2018 ). Impact of geomorphological changes to harbor resonance during meteotsunamis: The Vela Luka Bay Test Case. Pure and Applied Geophysics, 175 ( 11 ), 3839 â 3859. https://doi.org/10.1007/s00024â 018â 1862â 5 Dusek, G., DiVeglio, C., Licate, L., Heilman, L., Kirk, K., Paternostro, C., & Miller, A. ( 2019 ). A meteotsunami climatology along the U.S. East Coast. Bulletin of the American Meteorological Society, 100 ( 7 ), 1329 â 1345. https://doi.org/10.1175/BAMSâ Dâ 18â 0206.1 Ewing, M., Press, F., & Donn, W. L. ( 1954 ). An explanation of the Lake Michigan wave of 26 June 1954. Science, 120 ( 3122 ), 684 â 686. https://doi.org/10.1126/science.120.3122.684 Foo, J., Yosibash, Z., & Karniadakis, G. E. ( 2007 ). Stochastic simulation of riserâ sections with uncertain measured pressure loads and/or uncertain material properties. Computer Methods in Applied Mechanics and Engineering, 196 ( 41â 44 ), 4250 â 4271. https://doi.org/10.1016/j.cma.2007.04.005 Formaggia, L., Guadagnini, A., Imperiali, I., Lever, V., Porta, G., Riva, M., Scotti, A., & Tamellini, L. ( 2013 ). Global sensitivity analysis through polynomial chaos expansion of a basinâ scale geochemical compaction model. Computational Geosciences, 17 ( 1 ), 25 â 42. https://doi.org/10.1007/s10596â 012â 9311â 5 Ghanem, R., Higdon, R., & Owhadi, H. ( 2017 ). Handbook of uncertainty quantification. Springer. https://doi.org/10.1007/978â 3â 319â 12385â 1 Giraldi, L., Le Maître, O. P., Mandli, K. T., Dawson, C. N., Hoteit, I., & Knio, O. M. ( 2017 ). Bayesian inference of earthquake parameters from buoy data using a polynomial chaosâ based surrogate. Computational Geosciences, 21 ( 4 ), 683 â 699. https://doi.org/10.1007/s10596â 017â 9646â z Hibiya, T., & Kajiura, K. ( 1982 ). Origin of the Abiki phenomenon (a kind of seiche) in Nagasaki Bay. Journal of the Oceanographic Society of Japan, 38 ( 3 ), 172 â 182. https://doi.org/10.1007/BF02110288 IndexNoFollow eastern Adriatic extreme seaâ level hazard assessment meteotsunami early warning system Atmospheric and Oceanic Sciences Geological Sciences Science Article 2019 ftumdeepblue https://doi.org/10.1029/2019JC01557410.5150/cmcm.2017.03310.5194/nhessâ 2023-07-31T21:21:24Z The meteotsunami early warning system prototype using stochastic surrogate approach and running operationally in the eastern Adriatic Sea is presented. First, the atmospheric internal gravity waves (IGWs) driving the meteotsunamis are either forecasted with stateâ ofâ theâ art deterministic models at least a day in advance or detected through measurements at least 2 hr before the meteotsunami reaches sensitive locations. The extreme seaâ level hazard forecast at endangered locations is then derived with an innovative stochastic surrogate modelâ implemented with generalized polynomial chaos expansion (gPCE) method and synthetic IGWs forcing a barotropic ocean modelâ used with the input parameters extracted from deterministic model results and/or measurements. The evaluation of the system, both against five historical events and for all the detected potential meteotsunamis since late 2018 when the early warning system prototype became operational, reveals that the meteotsunami hazard is conservatively assessed but often overestimated at some locations. Despite some needed improvements and developments, this study demonstrates that gPCEâ based methods can be used for atmospherically driven extreme seaâ level hazard assessment and in geosciences in wide.Plain Language SummaryAtmospherically driven extreme seaâ level events are one of the major threats to people and assets in the coastal regions. Assessing the hazard associated with such events together with uncertainty quantification in a precise and timely manner is thus of primary importance in modern societies. In this study, an early warning system for the eastern Adriatic meteotsunamis, destructive long waves with periods from few minutes up to an hour generated by traveling atmospheric disturbances, is presented and evaluated. The system is based on stateâ ofâ theâ art deterministic atmospheric and ocean models as well as an innovative statistical model developed to forecast the meteotsunami hazard. The evaluation reveals that the meteotsunami hazard is ... Article in Journal/Newspaper The Cryosphere University of Michigan: Deep Blue Journal of Geophysical Research: Oceans 124 11 8485 8499

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