Articles | Volume 18, issue 2
https://doi.org/10.5194/os-18-419-2022
© Author(s) 2022. 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-18-419-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Mar 2022
Research article |
| 28 Mar 2022
| 28 Mar 2022
Forecasting hurricane-forced significant wave heights using a long short-term memory network in the Caribbean Sea
Brandon J. Bethel
School of Marine Sciences, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Wenjin Sun
School of Marine Sciences, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Southern Ocean Science and Engineering Guangdong Laboratory
(Zhuhai), Zhuhai 519000, China
Changming Dong
CORRESPONDING AUTHOR
School of Marine Sciences, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Southern Ocean Science and Engineering Guangdong Laboratory
(Zhuhai), Zhuhai 519000, China
Department of Atmospheric and Oceanic Sciences, University of
California, Los Angeles, CA 90095, USA
Dongxia Wang
China State Shipbuilding Corporation (Chongqing) Haizhuang
Windpower Equipment Co., Ltd., Chongqing 400021, China
Cited articles
Ali, M. and Prasad, R.: SWH forecasting via an extreme learning machine
model integrated with improved complete ensemble empirical mode
decomposition, Renew. Sustain. Energy Rev., 104, 281–295,
https://doi.org/10.1016/j.rser.2019.01.014, 2019.
Alina, A. I., Rusu, L., and Catalin, A.: Nearshore Wave Dynamics at Mangalia Beach
Simulated by Spectral Models, J. Mar. Sci. Eng., 7, 206,
https://doi.org/10.3390/jmse7070206, 2019.
Allahdadi, M. N., He, R., and Neary, V. S.: Predicting ocean waves along the US east coast during energetic winter storms: sensitivity to whitecapping parameterizations, Ocean Sci., 15, 691–715, https://doi.org/10.5194/os-15-691-2019, 2019.
Arzani, A., Wang, J., and D'Souza, R. M.: Uncovering near-wall blood flow from
sparse data with physics-informed neural networks, Phys. Fluids, 33,
071905, https://doi.org/10.1063/5.0055600, 2021.
Avila-Alonso, D., Baetens, J. M., Cardenas, R., and De Baets, B.: Oceanic response to the consecutive Hurricanes Dorian and Humberto (2019) in the Sargasso Sea, Nat. Hazards Earth Syst. Sci., 21, 837–859, https://doi.org/10.5194/nhess-21-837-2021, 2021.
Aydoğan, B. and Ayat, B.: Performance evaluation of SWAN ST6 physics forced
by ERA5 wind fields for wave prediction in an enclosed basin, Ocean Eng.,
240, 109936, https://doi.org/10.1016/j.oceaneng.2021.109936, 2021.
Babanin, A. V., Rogers, W. E., and de Camargo, R.: Waves and Swells in High Wind
and Extreme Fetches, Measurements in the Southern Ocean, Front. Mar.
Sci., 6, 361, https://doi.org/10.3389/fmars.2019.00361, 2019.
Bethel, B. J., Dong, C., Zhou, S., and Cao, Y.: Bidirectional Modeling of Surface
Winds and Significant Wave Heights in the Caribbean Sea, J. Mar. Sci. Eng.,
9, 547, https://doi.org/10.3390/jmse9050547, 2021a.
Bethel, B. J., Dong, C., and Wang, J.: An Empirical Wind-Wave Model for
Hurricane-forced Wind Waves in the Caribbean Sea, Earth Space Sci., 8,
e2021EA001956, https://doi.org/10.1029/2021EA001956, 2021b.
Bhalachandran, S., Nadimpalli, R., Osuri, K.K., Marks Jr., F.D.,
Gopalakrishnan, S., Subramanian, S., Mohanty, U. C., and Niyogi, D.: On the
processes influencing rapid intensity changes of tropical cyclones over the
Bay of Bengal, Sci. Rep., 9, 3382,
https://doi.org/10.1038/s41598-019-40332-z, 2019.
Björkqvist, J.-V., Pettersson, H., and Kahma, K. K.: The wave spectrum in archipelagos, Ocean Sci., 15, 1469–1487, https://doi.org/10.5194/os-15-1469-2019, 2019.
Cao, Y., Dong, C., Uchiyama, Y., Wang, J., and Yin, X.: Multiple-Scale Variations of Wind-Generated Waves in the Southern California Bight, J. Geophys. Res.-Oceans, 123, 9340–9356,
https://doi.org/10.1029/2018JC014505, 2018.
Campos, R. M., Costa, M. O., Almeida, F., and Guedes Soares, C.: Operational Wave
Forecast Selection in the Atlantic Ocean Using Random Forests, J. Mar. Sci.
Eng., 9, 298, https://doi.org/10.3390/jmse9030298, 2021.
Cecilio, R. O. and Dillenburg, S. R.: An ocean wind-wave climatology for the
Southern Brazilian Shelf, Part 1: Problem presentation and model validation,
Dyn. Atmos. Oceans, 89, 101101,
https://doi.org/10.1016/j.dynatmoce.2019.101101, 2020.
Chao, Y., Huang, H., Wang, D., Liu, Y., and Guo, Z.: The Characteristics of Storm
Wave Behavior and Its Effect on Cage Culture Using the ADCIRC+SWAN Model
in Houshui Bay, China, J. Ocean Univ. China, 19, 307–319,
https://doi.org/10.1007/s11802-020-3941-3, 2020.
Chen, J, Wang, Z., Tam, C., Lau, N., Lau, D. D., and Mok, H.: Impacts of climate
change on tropical cyclones and induced storm surges in the Pearl River
Delta region using pseudo-global-warming method, Sci. Rep., 10, 1965,
https://doi.org/10.1038/s41598-020-58824-8, 2020.
Chen, T.: Probabilistic forecasting of coastal wave height during typhoon
warning period using machine learning methods, Hydroinformatics, 21,
343–358, https://doi.org/10.2166/hydro.2019.115, 2019.
Chen, S. and Wang, Y.: Improving Coastal Ocean Wave Height Forecasting during
Typhoons by using Local Meteorological and Neighboring Wave Data in Support
Vector Regression Models, J. Mar. Sci. Eng., 8, 149,
https://doi.org/10.3390/jmse8030149, 2020.
Chen, J., Pillai, A. C., Johanning, L., and Ashton, I.: Using machine learning to
derive spatial wave data: A case study for a marine energy site, Environ.
Model. Softw., 142, 105066, https://doi.org/10.1016/j.envsoft.2021.105066,
2021.
Chen, Y., Gao, S., Li, X., and Shen, X.: Key Environmental Factors for Rapid
Intensification of the South China Sea Tropical Cyclones, Front. Earth Sci.,
8, 609727, https://doi.org/10.3389/feart.2020.609727, 2021.
Cheriton, O. M., Storlazzi, C. D., Rosenberger, K. J., Sherman, C.
E., and Schmidt, W. E.: Rapid observations of ocean dynamics
and stratification along a steep island coast during Hurricane
María, Sci. Adv., 7, eabf1552, https://doi.org/10.1126/sciadv.abf1552,
2021.
Cheung, K. F.: WaveWatch III (WW3) Samoa Regional Wave Model, 1 January – 31 December, 2019, Distributed by the Pacific Islands Ocean Observing System (PacIOOS) [data set], https://coastwatch.pfeg.noaa.gov/ (last access: 10 July 2021), 2013.
Choi, J. K. and Lee, B.: Combining LSTM Network Ensemble via Adaptive Weighting
for Improved Time Series Forecasting, Math. Probl. Eng., 2018, 2470171,
https://doi.org/10.1155/2018/2470171, 2018.
Collins, C., Hesser, T., Rogowski, P., and Merrifield, S.: Altimeter Observations
of Tropical Cyclone-generated Sea States: Spatial Analysis and Operational
Hindcast Evaluation, J. Mar. Sci. Eng., 9,
216, https://doi.org/10.3390/jmse9020216, 2021.
Constantin, A.: Nonlinear water waves: introduction and overview, Philos. T. Roy. Soc. A, 376, L20170310, https://doi.org/10.1098/rsta.2017.0310, 2018.
Christakos, K., Furevik, B. R., Aarnes, O. J., Breivik, Ø., Tuomi, L.,
and Byrkjedal, Ø.: The importance of wind forcing in fjord wave modelling,
Ocean Dyn., 70, 57–75, https://doi.org/10.1007/s10236-019-01323-w, 2020.
Drost, E., Lowe, R., Ivey, G., Jones, N. L., and Pequignet, C.: The Effects of
Tropical Cyclone Characteristics on the Surface Wave Fields in Australia's
North West Region, Cont. Shelf Res., 139, 35–53,
https://doi.org/10.1016/j.csr.2017.03.006, 2017.
Fan, S., Xiao, N., and Dong, S.: A novel model to predict SWH based on long
short-term memory network, Ocean Eng., 205, 107298,
https://doi.org/10.1016/j.oceaneng.2020.107298, 2020.
Gao, S., Huang, J., Liu, G., Bi, F., and Bai, Z.: A forecasting model for wave
heights based on a long short-term memory neural network, Acta Oceanol.
Sin., 40, 62–69, https://doi.org/10.1007/s13131-020-1680-3, 2021.
Geiger, T., Gütshow, J., Bresch, D. N., Emmanuel, K., and Frieler, K.: Double
benefit of limiting global warming for tropical cyclone exposure, Nat. Clim.
Change, 11, 861–866, https://doi.org/10.1038/s41558-021-01157-9, 2021.
Golbazi, M. and Archer, C. L.: Methods to Estimate Surface Roughness for Offshore
Wind Energy, Adv. Meteorol., 2019, 1–15,
https://doi.org/10.1155/2019/5695481, 2019.
Guan, X.: Wave height prediction based on CNN-LSTM, 2020 2nd
International Conference on Machine Learning, Big Data and Business
Intelligence (MLBDBI), 23–25 October 2020, Taiyuan, China, https://doi.org/10.1109/MLBDBI51377.2020.00009, 2020.
Guo, Y., Hou, Y., Liu, Z., and Du, M.: Risk Prediction of Coastal Hazards Induced
by Typhoon: A Case Study in the Coastal Region of Shenzhen, China, Remote
Sens., 12, 1731, https://doi.org/10.3390/rs12111731, 2020.
Hatzikyriakou, A. and Lin, N.: Simulating storm surge waves for structural
vulnerability estimation and flood hazard mapping, Nat. Hazard, 89, 939–962,
https://doi.org/10.1007/s11069-017-3001-5, 2017.
Haryanto, Y. D., Riama, N. F., Purnama, D. R., and Sigalingging, A. D.: The Effect of
the Difference in Intensity and Track of Tropical Cyclone on Significant
Wave Height and Wave Direction in the Southeast Indian Ocean, World
Sci. J., 2021, 5492048, https://doi.org/10.1155/2021/5492048, 2021.
Hegermiller, C. A., Warner, J. C., Olabarreita, M., and Sherwood, C. R.:
Wave-Current Interaction between Hurricane Matthew Wave Fields and the Gulf
Stream, J. Phys. Oceanogr., 49, 2283–2900,
https://doi.org/10.1175/JPO-D-19-0124.1, 2019.
Hochreiter, S. and Schmidhuber, J.: Long Short-term Memory, Neural Comput.,
9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997.
Hopkins, J., Elgar, S., and Raubenheimer, B.: Observations and model simulations
of wave-current interaction on the inner shelf, J. Geophys. Res.-Oceans,
121, 198–208, https://doi.org/10.1002/2015JC010788, 2015.
Hu, Y., Shao, W., Wei, Y., and Zuo, J.: Analysis of Typhoon-Induced Waves along
Typhoon Tracks in the Western North Pacific Ocean, 1998–2017, J. Mar. Sci.
Eng., 8, 521, https://doi.org/10.3390/jmse8070521, 2020.
Hu, H., van der Westhuysen, A. J., Chu, P., and Fujisaki-Manome, A.: Predicting
Lake Erie wave heights and periods using XGBoost and LSTM, Ocean Model., 164,
101832, https://doi.org/10.1016/j.ocemod.2021.101832, 2021.
Huang, W. and Dong, S.: Improved short-term prediction of SWH by decomposing
deterministic and stochastic components, Renew. Energy, 177, 743–758,
https://doi.org/10.1016/j.renene.2021.06.008, 2021.
Hwang, P. A. and Fan, Y.: Effective Fetch and Duration of Tropical Cyclone Wind
Fields Estimated from Simultaneous Wind and Wave Measurements: Surface Wave
and Air-Sea Exchange Computation, J. Phys. Ocean., 47, 447–470,
https://doi.org/10.1175/JPO-D-16-0180.1, 2017.
Jörges, C., Berbenbrink, C., and Stumpe, B.: Prediction and reconstruction of
ocean wave heights based on bathymetric data using LSTM neural networks,
Ocean Eng., 232, 109046, https://doi.org/10.1016/j.oceaneng.2021.109046,
2021.
Joyce, B. R., Gonzalez-Lopez, J., Van der Westhuysen, A. J., Yang, D.,
Pringle, J., Westerink, J. J., and Cox, A. T.: U.S. IOOS Coastal and Ocean Modeling
Testbed: Hurricane-Induced Winds, Waves, and Surge for Deep Ocean,
Reef-Fringed Islands in the Caribbean, J. Geophys. Res.-Oceans, 124,
2876–2907, https://doi.org/10.1029/2018JC014687, 2019.
Kaji, D., Watanabe, K., and Kobayashi, M.: Multi-Decoder RNN Autoencoder Based on
Variational Bayes Method, 2020 International Joint Conference on Neural
Networks (IJCNN), 19–24 July 2020, 1–8, https://doi.org/10.1109/IJCNN48605.2020.9206686, 2020.
Kaloop, M. R., Beshr, A. A. A., Zarzoura, F., Ban, W. H., and Hu, J. W.: Predicting
lake wave height based on regression classification and multi input-single
output soft computing models, Arab. J. Geosci., 13, 591,
https://doi.org/10.1007/s12517-020-05498-1, 2020.
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., and Yang,
L.: Physics-informed machine learning, Nat. Rev. Phys., 3, 422–440,
https://doi.org/10.1038/s42254-021-00314-5, 2021.
Kashinath, K., Mustafa, M., and Albert, A.: Physicsinformed
machine learning: case studies for weather
and climate modelling, Philos. T. Roy. Soc. A, 379, 20200093,
https://doi.org/10.1098/rsta.2020.0093, 2021.
Khodkar, M. A. and Hassanzadeh, P.: A data-driven, physics-informed framework for
forecasting the spatiotemporal evolution of chaotic dynamics with
nonlinearities modeled as exogenous forcing, J. Comput. Phys., 440, 110412,
https://doi.org/10.1016/j.jcp.2021.110412, 2021.
Kim, K., Lee, J., Roh, M., Han, K., and Lee, G.: Prediction of Ocean Weather
Based on Denoising AutoEncoder and Convolutional LSTM, J. Mar. Sci. Eng.,
8, 805, https://doi.org/10.3390/jmse8100805, 2020.
Köllisch, N., Behrendt, J., Klein, M., and Hoffmann, N.: Nonlinear real time
prediction of ocean surface waves, Ocean Eng., 157, 387–400,
https://doi.org/10.1016/j.oceaneng.2018.03.048, 2018.
Kossin, J. P., Knapp, K. R., Olander, T. L., and Velden, C. S.: Global increase in
major tropical cyclone exceedance probability over the past four decades,
P. Natl. Acad. Sci. USA, 117, 11975–11980, https://doi.org/10.1073/pnas.1920849117, 2020.
Landsea, C., Franklin, J., and Beven, J.: The revised Atlantic hurricane database (HURDAT2), National Hurricane Center [data set], http://www.nhc.noaa.gov/data/hurdat/hurdat2-format-atlantic.pdf, (last access: 10 July 2021), 2015.
Leroux, D., Wood, K., Elsberry, R. L., Cayanan, E. O., Hendricks, E., Kucas,
M., Otto, P., Rogers, R., Sampson, B., and Yu, Z.: Recent Advances in Research
and Forecasting of Tropical Cyclone Track, Intensity, and Structure at
Landfall, Tropic. Cyclone Res. Rev., 7, 85–105,
https://doi.org/10.6057/2018TCRR02.02, 2018.
Li, M. and Liu, K.: Probabilistic Prediction of SWH Using Dynamic Bayesian
Network and Information Flow, Water, 12, 2075,
https://doi.org/10.3390/w12082075, 2020.
Li, M., Zhang, R., and Liu, K.: A New Marine Disaster Assessment Model Combining
Bayesian Network with Information Diffusion, J. Mar. Sci. Eng., 9, 640,
https://doi.org/10.3390/jmse9060640, 2021.
Lim Kam Sian, K. T. C., Dong, C., Liu, H., Wu, R., and Zhang, R.: Effects of Model
Coupling on Typhoon Kalmaegi (2014) Simulation in the South China Sea,
Atmosphere, 11, 432, https://doi.org/10.3390/atmos11040432, 2020.
Liu, L. L., Wang, W., and Huang, R. X.: The Mechanical Energy Input to the Ocean
Induced by Tropical Cyclones, J. Phys. Ocean., 38, 1253–1266,
https://doi.org/10.1175/2007JPO3786.1, 2008.
Marsooli, R. and Lin, N.: Numerical Modeling of Historical Storm Tides and Waves
and Their Interactions Along the U.S. East and Gulf Coasts, J. Geophys. Res.-Oceans, 123, 3844–3874, https://doi.org/10.1029/2017JC013434, 2018.
Masoomi, H., van de Lindt, J. W., Ameri, M. R., Do, T., and Webb, B.: Combined
Wind-Wave-Surge Hurricane-Induced Damage Prediction for Buildings, J.
Struct. Eng., 145, 04018227, https://doi.org/10.1061/(ASCE)ST.1943-541X.0002241,
2018.
Meng, F., Song, T., Xu, D., Xie, P., and Li, Y.: Forecasting tropical cyclone
wave height using bidirectional gated recurrent unit, Ocean Eng., 234,
108795, https://doi.org/10.1016/j.oceaneng.2021.108795, 2021.
Morgenstern, T., Pahner, S., Mietrach, R., and Shütze, N.: Flood forecasting in small catchments using deep learning LSTM networks, EGU General Assembly, EGU21-15072, https://doi.org/10.5194/egusphere-egu21-15072, 2021.
National Data Buoy Center: Meteorological and oceanographic data collected from the National Data Buoy Center Coastal-Marine Automated Network (C-MAN) and moored (weather) buoys, Moored Buoys, NOAA National Centers for Environmental Information [data set], https://www.ndbc.noaa.gov/ (last access: 10 July 2021), 1971.
Niaki, S. A., Haghighat, E., Campbell, T., Poursartip, A., and Vaziri, R.:
Physics-informed neural network for modelling the thermochemical curing
process of composite-tool systems during manufacture, Comput. Methods Appl.
Mech. Eng., 384, 113959, https://doi.org/10.1016/j.cma.2021.113959, 2021.
Passaro, M., Hemer, M. A., Quartly, G. D., Scwatke, C., Dettmering, D., and Seitz, F.: Global coastal attenuation of wind-waves observed with radar altimetry, Nat. Commun., 12, 3812, https://doi.org/10.1038/s41467-021-23982-4, 2021.
Qiao, C. and Myers, A. T.: Modeling Spatio-Temporal Characteristics of Metocean
Conditions During Hurricanes Using Deep Neural Networks, ASME, 2020 39th
International Conference on Ocean, Offshore and Arctic Engineering, 3–7 August, https://doi.org/10.1115/OMAE2020-18989, 2020.
Qiao, C. and Myers, A. T.: Surrogate modeling of time-dependent 80
metocean conditions during hurricanes, Nat. Hazards, 110, 1545–1563,
https://doi.org/10.1007/s11069-021-05002-2, 2022.
Raj, N. and Brown, J.: An EEMD-BiLSTM Algorithm Integrated with Boruta Random
Forest Optimiser for SWH Forecasting along Coastal Areas of Queensland,
Australia, Remote Sens., 13, 1456, https://doi.org/10.3390/rs13081456,
2021.
Reikard, G. and Rogers, W. E.: Forecasting ocean waves: Comparing a physics-based
model with statistical methods, Coast. Eng., 58, 409–416,
https://doi.org/10.1016/j.coastaleng.2010.12.001, 2011.
Rollano, F. T., Brown, A., and Ellenson, A.: Breaking waves in deep water:
measurements and modeling of energy dissipation, Ocean Dynam., 69, 1165–1179,
https://doi.org/10.1007/s10236-019-01301-2, 2019.
Romero, L., Lenain, L., and Melville, W. K.: Observations of Surface Wave-Current
Interaction, J. Phys. Ocean., 47, 615–632,
https://doi.org/10.1175/JPO-D-16-0108.1, 2017.
Sahoo, B., Jose, F., and Bhaskaran, P. K.: Hydrodynamic response of Bahamas
archipelago to storm surge and hurricane generated waves – A case study for
Hurricane Joaquin, Ocean Eng., 184, 227–238,
https://doi.org/10.1016/j.oceaneng.2019.05.026, 2019.
Sahoo, B., Sahoo, T., and Bhaskaran, P. K.: Wave-current-surge interaction in a
changing climate over a shallow continental shelf region, Reg. Stud. Mar.
Sci., 46, 101910, https://doi.org/10.1016/j.rsma.2021.101910, 2021.
Shao, Z., Liang, B., Li, H., Li, P., and Lee, D.: Extreme significant wave height of tropical cyclone waves in the South China Sea, Nat. Hazards Earth Syst. Sci., 19, 2067–2077, https://doi.org/10.5194/nhess-19-2067-2019, 2019.
Sharifineyestani, E. and Tahvildari, N.: Nonlinear Wave Evolution in Interaction
with Currents and Viscoleastic Muds, J. Mar. Sci. Eng., 9, 529,
https://doi.org/10.3390/jmse9050529, 2021.
Song, H., Kuang, C., Gu, J., Zou, Q., Liang, H., Sun, X., and Ma, Z.: Nonlinear
tide-surge-wave interaction at a shallow coast with large scale sequential
harbor constructions, Estuar. Coast. Shelf Sci., 233, 106543,
https://doi.org/10.1016/j.ecss.2019.106543, 2020.
Sun, Y., Perrie, W., and Toulany, B.: Simulation of Wave-Current Interactions
Under Hurricane Conditions Using an Unstructured-Grid Model: Impacts on
Ocean Waves, J. Geophys. Res.-Oceans, 123, 3739–3760,
https://doi.org/10.1029/2017JC012939, 2018.
Tamizi, A. and Young, I. R.: The Spatial Distribution of Ocean Waves in Tropical
Cyclones, J. Phys. Ocean., 50, 2123–2139,
https://doi.org/10.1175/JPO-D-20-0020.1, 2020.
Tamizi, A., Alves, J., and Young, I. R.: The Physics of Ocean Wave Evolution
within Tropical Cyclones, J. Phys. Ocean, 51, 2373–2388,
https://doi.org/10.1175/JPO-D-21-0005.1, 2021.
Tian, D., Zhang, H., Zhang, W., Zhou, F., Sun, X., Zhou, Y., and Ke, D.: Wave
Glider Observations of Surface Waves During Three Tropical Cyclones in the
South China Sea, Water, 12, 1331, https://doi.org/10.3390/w12051331,
2020.
Violante-Carvalho, N., Arruda, W. Z., Carvalho, L. M., Rogers, W. E., and Passaro, M.:
Diffraction of irregular ocean waves measured by altimeter
in the lee of islands, Remote Sens. Environ., 265, 112653,
https://doi.org/10.1016/j.rse.2021.112653, 2021.
Wang, X., Yao, C., Gao, G., Jiang, H., Xu, D., Chen, G., and Zhang, Z.:
Simulating tropical cyclone waves in the East China Sea with an event-based,
parametric-adjusted model, J. Ocean., 76, 439–457,
https://doi.org/10.1007/s10872-020-00555-5, 2020.
Wang, J., Wang, Y., and Yang, J.: Forecasting of SWH Based on Gated Recurrent
unit Network in the Taiwan Strait and Its Adjacent Waters, Water, 13, 86,
https://doi.org/10.3390/w13010086, 2021.
Wei, C. and Cheng, J.: Nearshore two-step typhoon wind-wave prediction using
deep recurrent neural networks, Hydroinformatics, 22, 346–367,
https://doi.org/10.2166/hydro.2019.084, 2020.
Wei, Z.: Forecasting wind waves in the US Atlantic Coast using an artificial
neural network model: Towards an AI-based storm forecast system, Ocean
Eng., 237, 109646, https://doi.org/10.1016/j.oceaneng.2021.109646, 2021.
Wu, M., Stefanakos, C., and Gao, Z.: Multi-Step-Ahead Forecasting of Wave
Conditions Based on a Physics-Based Machine Learning (PBML) Model for Marine
Operations, J. Mar. Sci. Eng., 8, 992,
https://doi.org/10.3390/jmse8120992, 2020.
Yim, S. C., Osborne, A. R., and Mohtat, A.: Nonlinear Ocean Wave Models and
Laboratory Simulation of High Seastates and Rogue Waves. Proceedings of the
ASME 2017 International Conference on Ocean, Offshore and Arctic
Engineering, OMAE2017, 25–30 June 2017, Trondheim, Norway,
https://doi.org/10.1115/OMAE2017-62706, 2017.
Yu, Y., Si, X., Hu, C., and Zhang, J.: A Review of Recurrent Neural Networks:
LSTM Cells and Networks, Neural Comput., 31, 1235–1270,
https://doi.org/10.1162/neco_a_01199, 2019.
Zegarra, M. A., Schmid, J. P., Palomino, L., and Seminario, B.: Impact
of Hurricane Dorian in the Bahamas: A View from the Sky,
Washington, D.C.: Inter-American Development Bank, https://doi.org/10.18235/0002163, 2020.
Zhang, L. and Oey, L.: An Observational Analysis of Ocean Surface Waves in
Tropical Cyclones in the Western North Pacific Ocean, J. Geophys. Res.-Oceans, 124, 184–195, https://doi.org/10.1029/2018JC014517, 2018.
Zhang, C. and Li, C.: Effects of hurricane forward speed and approach angle on
storm surges: an idealized numerical experiment, Acta. Oceanol. Sin., 38,
48–56, https://doi.org/10.1007/s13131-018-1081-z, 2019.
Zhang, Z., Rai, R., Chowdhury, S., and Doermann, D.: MIDPhyNet: Memorized
Infusion of Decomposed Physics in Neural Networks to Model Dynamic Systems,
Neurocomputing, 428, 116–129, https://doi.org/10.1016/j.neucom.2020.11.042,
2020.
Zhao, K. and Wang, J.: SWH forecasting based on the hybrid EMD-SVM method,
Ind. J. Geo Mar. Sci., 48, 1957–1962, 2018.
Zhang, L. and Oey, L.: An Observational Analysis of Ocean Surface Waves in
Tropical Cyclones in the Western North Pacific Ocean, J. Geophys. Res.-Oceans, 124, 184–195, 2018.
Zhou, S., Bethel, B. J., Sun, W., Zhao, Y., Xie, W., and Dong,
C.: Improving SWH Forecasts Using a Joint Empirical Mode
Decomposition-Long Short-Term Memory Network, J. Mar. Sci.
Eng., 9, 744, https://doi.org/10.3390/jmse9070744, 2021a.
Zhou, S., Xie, W., Lu, Y., Wang, Y., Zhou, Y., Hui, N., and Dong, C.:
ConvLSTM-Based Wave Forecasts in the South and East China Seas, Front. Mar.
Sci., 8, 680079, https://doi.org/10.3389/fmars.2021.680079, 2021b.
Zobeiry, N. and Humfeld, K. D.: A physics-informed machine learning approach for
solving heat transfer equation in advanced manufacturing and engineering
applications, Eng. Appl., 101, 104232,
https://doi.org/10.1016/j.engappai.2021.104232, 2021.
Zubier, K.: Using an Artificial Neural Network for Wave Height Forecasting in
the Red Sea, Ind. J. Geo Mar. Sci., 49, 184–191, 2020.
Short summary
Ocean surface waves forced by tropical cyclones can cause tremendous damage to offshore structures and coastal communities. As such, forecasting their evolution is of utmost importance. This study investigates the usage of a long short-term memory neural network to forecast hurricane-forced waves in the Caribbean Sea. Results strongly suggest that forecasts can be performed to a high degree of accuracy up to 12 h at minimal computational expense and even outperform a wave model.
Ocean surface waves forced by tropical cyclones can cause tremendous damage to offshore...
