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Journal articles on the topic 'Wave forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Arthur, Robert S. "WAVE FORECASTING AND HINDCASTING." Coastal Engineering Proceedings 1, no. 1 (May 12, 2010): 8. http://dx.doi.org/10.9753/icce.v1.8.

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As a result of wartime research on ocean surface waves a method has been available since 1943 for the prediction of wave characteristics of interest to engineers (O'Brien and Johnson, 1947). The initial stimulus for the development came during the planning of the invasion of North Africa, and the methods subsequently devised were later used in a number of amphibious operations (Bates, 1949). The same techniques have found useful peacetime application in problems connected with coastal engineering. Much of the application to date has consisted in applying wave prediction techniques to historical rather than current meteorological data, hence the term "wave hindcasting."

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2

Bretschneider, C. L. "REVISED WAVE FORECASTING RELATIONSHIPS." Coastal Engineering Proceedings 1, no. 2 (January 1, 2000): 1. http://dx.doi.org/10.9753/icce.v2.1.

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Data on the generation and decay of wind-generated gravity waves have been collected for several years by the University of California. These data together with the original data by Sverdrup and Munk have been analyzed, and the results were presented in dimensionless graphs suitable for use in wave forecasting (Bretschneider, 1951). No analysis was made of the effect of following or opposing winds.

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3

Bretschneider, C. L. "REVISIONS IN WAVE FORECASTING: DEEP AND SHALLOW WATER." Coastal Engineering Proceedings 1, no. 6 (January 29, 2011): 3. http://dx.doi.org/10.9753/icce.v6.3.

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During the past six years since the latest revisions in wave forecasting (Bretschneider 1951) were made, much information has become available such that another revision is in order. An abundance of published (and unpublished) accounts of wave generation and decay in both deep and shallow water from various sources, as well as new ideas in the art of wave forecasting, are used in this revision. Deep water wave forecasting relationships, relationships for the generation of wind waves in shallow water of constant depth, and techniques for forecasting wind waves over the Continental Shelf are included in this paper. Forecasting hurricane waves is also discussed, from the engineering design point of view. The concept of significant wave is still retained as the most practical method in wave forecasting to date. The significant period has definite significance in that the wave energy is propagated forward at a speed approximately equal to the corresponding group velocity. The graphical approach (Wilson 1955) for moving fetches and variable wind vectors is discussed, and is the best approach for forecasting waves. Without Wilson’s graphical technique it is difficult for any two forecasters supplied with the same meteorological data to obtain the same degree of verification, or determine whether the forecaster or the forecasting relationships are in error. It is quite possible that by use of this technique further revisions in wave forecasting are possible. The problem of wave variability is discussed, and the distribution functions are given. A short summary of the wave spectra (Bretschneider 1958) used in connection with the revisions is also given. When the present forecasting relationships are applied to sections of the world, other than that from which the basic data were procured, it is recommended that atmospheric stability factors be taken into account. This essentially involves a slight modification or calibration of the forecasting relationships and techniques, prior to general use in the area of interest.

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4

Shutts, Glenn. "Operational lee wave forecasting." Meteorological Applications 4, no. 1 (March 1997): 23–35. http://dx.doi.org/10.1017/s1350482797000340.

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5

Chawla, Arun, Hendrik L. Tolman, Vera Gerald, Deanna Spindler, Todd Spindler, Jose-Henrique G. M. Alves, Degui Cao, Jeffrey L. Hanson, and Eve-Marie Devaliere. "A Multigrid Wave Forecasting Model: A New Paradigm in Operational Wave Forecasting." Weather and Forecasting 28, no. 4 (July 30, 2013): 1057–78. http://dx.doi.org/10.1175/waf-d-12-00007.1.

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Abstract A new operational wave forecasting system has been implemented at the National Centers for Environmental Prediction (NCEP) using the third public release of WAVEWATCH III. The new system uses a mosaic of grids with two-way nesting in a single model. This global system replaces a previous operational wave modeling suite (based on the second release of WAVEWATCH III). The new forecast system consists of nine grids at different resolutions to provide the National Weather Service (NWS) and NCEP centers with model guidance of suitable resolution for all areas where they have the responsibility of providing gridded forecast products. New features introduced in WAVEWATCH III, such as two-way nesting between grids and carving out selected areas of the computational domain, have allowed the operational model to increase spatial resolution and extend the global domain closer to the North Pole, while at the same time optimizing the computational cost. A spectral partitioning algorithm has been implemented to separate individual sea states from the overall spectrum, thus providing additional products for multiple sea states. Field output data are now packed in version 2 of the gridded binary (GRIB2) format and apart from the standard mean wave parameters, they also include parameters of partitioned wave spectra. The partitioning is currently limited to three fields: the wind-wave component, and primary and secondary swells. The modeling system has been validated against data using a multiyear hindcast database as well as archived forecasts. A new software tool developed by the U.S. Army Corps of Engineers (USACE) is used to extend the analysis from overall error estimates to separate skill scores for wind seas and swells.

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Simpson, Alexandra, Merrick Haller, David Walker, Patrick Lynett, and David Honegger. "Wave-by-Wave Forecasting via Assimilation of Marine Radar Data." Journal of Atmospheric and Oceanic Technology 37, no. 7 (July 1, 2020): 1269–88. http://dx.doi.org/10.1175/jtech-d-19-0127.1.

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AbstractThis work describes a phase-resolving wave-forecasting algorithm that is based on the assimilation of marine radar image time series. The algorithm is tested against synthetic data and field observations. The algorithm couples X-band marine radar observations with a phase-resolving wave model that uses the linear mild slope equations for reconstruction of water surface elevations over a large domain of O(km) and a prescribed time window of O(min). The reconstruction also enables wave-by-wave forecasting through forward propagation in space and time. Marine radar image time series provide the input wave observations through a previously given relationship between backscatter intensity and the radial component of the sea surface slope. The algorithm assimilates the wave slope information into the model via a best-fit wave source function at the boundary that minimizes the slope reconstruction error over an annular region at the outer ranges of the radar images. The wave model is then able to propagate the waves across a polar domain to a location of interest at nearer ranges. The constraints on the method for achieving real-time forecasting are identified, and the algorithm is verified against synthetic data and tested using field observations.

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7

Chen, Shien-Tsung. "Probabilistic forecasting of coastal wave height during typhoon warning period using machine learning methods." Journal of Hydroinformatics 21, no. 2 (February 4, 2019): 343–58. http://dx.doi.org/10.2166/hydro.2019.115.

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Abstract This study applied machine learning methods to perform the probabilistic forecasting of coastal wave height during the typhoon warning period. The probabilistic forecasts comprise a deterministic forecast and the probability distribution of a forecast error. A support vector machine was used to develop a real-time forecasting model for generating deterministic wave height forecasts. The forecast errors of deterministic forecasting were then used as a database to generate probabilistic forecasts by using the modified fuzzy inference model. The innovation of the modified fuzzy inference model includes calculating the similarity of the data by performing fuzzy implication and resampling the potential data from the fuzzy database for probability distribution. The probabilistic forecasting method was applied to the east coast of Taiwan, where typhoons frequently cause large waves. Hourly wave height data from an offshore buoy and various typhoon characteristics were used as inputs of the probabilistic forecasting model. Validation results from real typhoon events verified that the proposed probabilistic forecasting model can generate the predicted confidence interval, which can properly enclose the observed wave height data, excluding some cases with extreme wave heights. Moreover, an objective measure was used to validate the proposed probabilistic forecasting method.

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8

Janssen, Peter A. E. M., and Jean-Raymond Bidlot. "Progress in Operational Wave Forecasting." Procedia IUTAM 26 (2018): 14–29. http://dx.doi.org/10.1016/j.piutam.2018.03.003.

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9

Deo, M. C., A. Jha, A. S. Chaphekar, and K. Ravikant. "Neural networks for wave forecasting." Ocean Engineering 28, no. 7 (July 2001): 889–98. http://dx.doi.org/10.1016/s0029-8018(00)00027-5.

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10

Monastersky, Richard. "Tsunami forecasting: The next wave." Nature 483, no. 7388 (March 2012): 144–46. http://dx.doi.org/10.1038/483144a.

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11

Janssen, Peter A. E. M. "Progress in ocean wave forecasting." Journal of Computational Physics 227, no. 7 (March 2008): 3572–94. http://dx.doi.org/10.1016/j.jcp.2007.04.029.

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12

Kyaw, Thit Oo, Tomoya Shibayama, Yoko Shibutani, and Yasuo Kotake. "DEVELOPMENT OF A DEEP-LEARNING BASED WAVE FORECASTING MODEL USING LSTM NETWORK." Coastal Engineering Proceedings, no. 36v (December 28, 2020): 31. http://dx.doi.org/10.9753/icce.v36v.waves.31.

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Forecasting of wave conditions plays an essential role for offshore construction and maintenance. Recently, machine learning-based wave forecasting models have been developed and their integrated usage with physics-based numerical models has become popular. These studies mostly apply Feed Forward Neural Networks (FFNNs) with an emphasis on prediction of time-series of waves, tides and storm surges. As a particularly different approach, we develop a deep learning-based wave forecasting model using Long Short-Term Memory (LSTM) network under Recurrent Neural Networks. As a case study, the model will be utilized to predict the wave conditions (low or high) near the Tottori Port, Japan.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/oMvIS9zkIOs

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13

Badulin, S. I., V. G. Grigorieva, L. Aouf, and A. Dalphinet. "HIGH RESOLUTION WAVE FORECASTING MODELS AND WAVE TURBULENCE THEORY." XXII workshop of the Council of nonlinear dynamics of the Russian Academy of Sciences 47, no. 1 (April 30, 2019): 15–17. http://dx.doi.org/10.29006/1564-2291.jor-2019.47(1).3.

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Results of high resolution sea wave modeling are treated within the theory of wave (weak) turbulence. Spatial resolution 1 km is shown likely to be excessive and lead to appearance of artificial structures in fields of wave periods and steepness. The research was supported by the state assignment of IO RAS, theme 0149-2019-0002.

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14

Doong, Dong-Jiing, Shien-Tsung Chen, Ying-Chih Chen, and Cheng-Han Tsai. "Operational Probabilistic Forecasting of Coastal Freak Waves by Using an Artificial Neural Network." Journal of Marine Science and Engineering 8, no. 3 (March 3, 2020): 165. http://dx.doi.org/10.3390/jmse8030165.

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Coastal freak waves (CFWs) are unpredictable large waves that occur suddenly in coastal areas and have been reported to cause casualties worldwide. CFW forecasting is difficult because the complex mechanisms that cause CFWs are not well understood. This study proposes a probabilistic CFW forecasting model that is an advance on the basis of a previously proposed deterministic CFW forecasting model. This study also develops a probabilistic forecasting scheme to make an artificial neural network model achieve the probabilistic CFW forecasting. Eight wave and meteorological variables that are physically related to CFW occurrence were used as the inputs for the artificial neural network model. Two forecasting models were developed for these inputs. Model I adopted buoy observations, whereas Model II used wave model simulation data. CFW accidents in the coastal areas of northeast Taiwan were used to calibrate and validate the model. The probabilistic CFW forecasting model can perform predictions every 6 h with lead times of 12 and 24 h. The validation results demonstrated that Model I outperformed Model II regarding accuracy and recall. In 2018, the developed CFW forecasting models were investigated in operational mode in the Operational Forecast System of the Taiwan Central Weather Bureau. Comparing the probabilistic forecasting results with swell information and actual CFW occurrences demonstrated the effectiveness of the proposed probabilistic CFW forecasting model.

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15

Zhu, Yuhang, Yineng Li, and Shiqiu Peng. "The Track and Accompanying Sea Wave Forecasts of the Supertyphoon Mangkhut (2018) by a Real-Time Regional Forecast System." Journal of Atmospheric and Oceanic Technology 37, no. 11 (November 2020): 2075–84. http://dx.doi.org/10.1175/jtech-d-19-0196.1.

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AbstractThe track and accompanying sea wave forecasts of Typhoon Mangkhut (2018) by a real-time regional forecasting system are assessed in this study. The real-time regional forecasting system shows a good track forecast skill with a mean error of 69.9 km for the forecast period of 1–72 h. In particular, it predicted well the landfall location on the coastal island of South China with distance (time) biases of 76.89 km (3 h) averaging over all forecasting made during 1–72 h and only 3.55 km (1 h) for the forecasting initialized 27 h ahead of the landfall. The sea waves induced by Mangkhut (2018) were also predicted well by the wave model of the forecasting system with a mean error of 0.54 m and a mean correlation coefficient up to 0.94 for significant wave height. Results from sensitivity experiments show that the improvement of track forecasting skill for Mangkhut (2018) are mainly attributed to application of a scale-selective data assimilation scheme in the atmosphere model that helps to maintain a more realistic large-scale flow obtained from the GFS forecasts, whereas the air–sea coupling has slightly negative impact on the track forecast skill.

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16

Smith, Jane McKee, Andrew B. Kennedy, Joannes J. Westerink, Alexandros A. Taflanidis, and Kwok Fai Cheung. "HAWAII HURRICANE WAVE AND SURGE MODELING AND FAST FORECASTING." Coastal Engineering Proceedings 1, no. 33 (December 14, 2012): 8. http://dx.doi.org/10.9753/icce.v33.management.8.

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The US Army Corps of Engineers’ Surge and Wave Island Modeling Studies developed a fast forecasting system for hurricane waves and inundation in Hawaii. The system is based on coupled high-resolution, high-fidelity simulations of waves and surge applying the SWAN and ADCIRC numerical models on a 2D finite-element grid. Additionally, wave runup is simulated on high-resolution cross-shore transects using the Boussinesq-equation model BOUSS-1D. Approximately 1500 storms were simulated to cover the range of hurricane parameters of landfall location, track angle at landfall, central pressure, forward speed, and radius of maximum winds expected to impact Hawaii. To create a forecast system that is fast and robust, a moving least-squares response surface surrogate model was developed based on the high-fidelity model results. The surrogate model is approximately seven orders of magnitude faster than the high-fidelity simulations. The efficiency of the surrogate model allows both deterministic and probabilistic simulations in seconds to minutes on a personal computer.

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17

Mérigaud, Alexis, Victor Ramos, Francesco Paparella, and John V. Ringwood. "Ocean forecasting for wave energy production." Journal of Marine Research 75, no. 3 (May 1, 2017): 459–505. http://dx.doi.org/10.1357/002224017821836752.

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18

Gómez Lahoz, M., and J. C. Carretero Albiach. "Wave forecasting at the Spanish coasts." Journal of Atmospheric & Ocean Science 10, no. 4 (December 2005): 389–405. http://dx.doi.org/10.1080/17417530601127522.

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19

Dixit, Pradnya, Shreenivas N. Londhe, and Yogesh H. Dandawate. "Wave Forecasting Using Neuro Wavelet Technique." International Journal of Ocean and Climate Systems 5, no. 4 (December 2014): 237–47. http://dx.doi.org/10.1260/1759-3131.5.4.237.

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20

Gao, Yuan, and Stuart Crampin. "Shear-wave splitting and earthquake forecasting." Terra Nova 20, no. 6 (December 2008): 440–48. http://dx.doi.org/10.1111/j.1365-3121.2008.00836.x.

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21

Ouellet, Y., and A. Drouin. "Définition des conditions de vagues pour la conception d'un havre de pêche à Sept-îles." Canadian Journal of Civil Engineering 18, no. 5 (October 1, 1991): 851–63. http://dx.doi.org/10.1139/l91-102.

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This paper presents the results of numerical studies to define the wave climate inside the Bay of Sept-îles, where it is proposed to build a small craft harbour. This wave climate is relatively complex as it results from waves coming from outside the Bay, that is from the estuary of the St. Lawrence, or generated inside the Bay itself. Such information is required to select the configuration of the harbour and the best location among the various sites proposed. Waves have been recorded at a station outside the Bay in 1966 – 1967 and inside the Bay in 1983. These records were used to calibrate the wave forecasting model based on wind data recorded at Sept-îles airport. They were also used to validate results obtained from a refraction model used to determine wave transformation from outside to inside the Bay. Then waves were forecasted over a long period (1953 – 1984), for the ice-free season, and used to study wave agitation inside different schemes proposed for harbour configuration and site. The study shows that there is a need to obtain better wave information and to improve the numerical models. Key words: wave forecasting, wave transformation, wave records, wave modeling, harbor, Sept-îles.

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22

Zhou, Shuyi, Brandon J. Bethel, Wenjin Sun, Yang Zhao, Wenhong Xie, and Changming Dong. "Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network." Journal of Marine Science and Engineering 9, no. 7 (July 5, 2021): 744. http://dx.doi.org/10.3390/jmse9070744.

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Wave forecasts, though integral to ocean engineering activities, are often conducted using computationally expensive and time-consuming numerical models with accuracies that are blunted by numerical-model-inherent limitations. Additionally, artificial neural networks, though significantly computationally cheaper, faster, and effective, also experience difficulties with nonlinearities in the wave generation and evolution processes. To solve both problems, this study employs and couples empirical mode decomposition (EMD) and a long short-term memory (LSTM) network in a joint model for significant wave height forecasting, a method widely used in wind speed forecasting, but not yet for wave heights. Following a comparative analysis, the results demonstrate that EMD-LSTM significantly outperforms LSTM at every forecast horizon (3, 6, 12, 24, 48, and 72 h), considerably improving forecasting accuracy, especially for forecasts exceeding 24 h. Additionally, EMD-LSTM responds faster than LSTM to large waves. An error analysis comparing LSTM and EMD-LSTM demonstrates that LSTM errors are more systematic. This study also identifies that LSTM is not able to adequately predict high-frequency significant wave height intrinsic mode functions, which leaves room for further improvements.

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23

Reikard, Gordon, Bryson Robertson, and Jean-Raymond Bidlot. "Wave energy worldwide: Simulating wave farms, forecasting, and calculating reserves." International Journal of Marine Energy 17 (April 2017): 156–85. http://dx.doi.org/10.1016/j.ijome.2017.01.004.

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24

Chen, Shien-Tsung, and Yu-Wei Wang. "Improving Coastal Ocean Wave Height Forecasting during Typhoons by using Local Meteorological and Neighboring Wave Data in Support Vector Regression Models." Journal of Marine Science and Engineering 8, no. 3 (February 26, 2020): 149. http://dx.doi.org/10.3390/jmse8030149.

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This study is aimed at applying support vector regression to perform real-time typhoon wave height forecasting with lead times of 1 to 3 h. Two wave rider buoys in the coastal ocean northeast of Taiwan provided real-time observation wave and meteorological data for the study. Information from actual typhoon events was collected and used for model calibration and validation. Three model structures were developed with different combinations of input variables, including wave, typhoon, and meteorological data. Analysis of forecasting results indicated that the proposed models have good generalization ability, but forecasts with longer lead times underestimate extreme wave heights. Comparisons of models with different inputs indicated that adding local meteorological data enhanced forecasting accuracy. Backup models were also developed in case local wave and meteorological data were unavailable. Analysis of these models revealed that when local wave heights are unknown, using neighboring wave heights can improve forecasting performance.

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25

Zheng, Chong Wei, Chong Yin Li, Xuan Chen, and Jing Pan. "Numerical Forecasting Experiment of the Wave Energy Resource in the China Sea." Advances in Meteorology 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/5692431.

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The short-term forecasting of wave energy is important to provide guidance for the electric power operation and power transmission system and to enhance the efficiency of energy capture and conversion. This study produced a numerical forecasting experiment of the China Sea wave energy using WAVEWATCH-III (WW3, the latest version 4.18) wave model driven by T213 (WW3-T213) and T639 (WW3-T639) wind data separately. Then the WW3-T213 and WW3-T639 were verified and compared to build a short-term wave energy forecasting structure suited for the China Sea. Considering the value of wave power density (WPD), “wave energy rose,” daily and weekly total storage and effective storage of wave energy, this study also designed a series of short-term wave energy forecasting productions. Results show that both the WW3-T213 and WW3-T639 exhibit a good skill on the numerical forecasting of the China Sea WPD, while the result of WW3-T639 is much better. Judging from WPD and daily and weekly total storage and effective storage of wave energy, great wave energy caused by cold airs was found. As there are relatively frequent cold airs in winter, early spring, and later autumn in the China Sea and the surrounding waters, abundant wave energy ensues.

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26

Raj, Nawin, and Jason Brown. "An EEMD-BiLSTM Algorithm Integrated with Boruta Random Forest Optimiser for Significant Wave Height Forecasting along Coastal Areas of Queensland, Australia." Remote Sensing 13, no. 8 (April 9, 2021): 1456. http://dx.doi.org/10.3390/rs13081456.

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Using advanced deep learning (DL) algorithms for forecasting significant wave height of coastal sea waves over a relatively short period can generate important information on its impact and behaviour. This is vital for prior planning and decision making for events such as search and rescue and wave surges along the coastal environment. Short-term 24 h forecasting could provide adequate time for relevant groups to take precautionary action. This study uses features of ocean waves such as zero up crossing wave period (Tz), peak energy wave period (Tp), sea surface temperature (SST) and significant lags for significant wave height (Hs) forecasting. The dataset was collected from 2014 to 2019 at 30 min intervals along the coastal regions of major cities in Queensland, Australia. The novelty of this study is the development and application of a highly accurate hybrid Boruta random forest (BRF)–ensemble empirical mode decomposition (EEMD)–bidirectional long short-term memory (BiLSTM) algorithm to predict significant wave height (Hs). The EEMD–BiLSTM model outperforms all other models with a higher Pearson’s correlation (R) value of 0.9961 (BiLSTM—0.991, EEMD-support vector regression (SVR)—0.9852, SVR—0.9801) and comparatively lower relative mean square error (RMSE) of 0.0214 (BiLSTM—0.0248, EEMD-SVR—0.043, SVR—0.0507) for Cairns and similarly a higher Pearson’s correlation (R) value of 0.9965 (BiLSTM—0.9903, EEMD–SVR—0.9953, SVR—0.9935) and comparatively lower RMSE of 0.0413 (BiLSTM—0.075, EEMD-SVR—0.0481, SVR—0.057) for Gold Coast.

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27

Lazarus, Steven M., Samuel T. Wilson, Michael E. Splitt, and Gary A. Zarillo. "Evaluation of a Wind-Wave System for Ensemble Tropical Cyclone Wave Forecasting. Part II: Waves." Weather and Forecasting 28, no. 2 (April 1, 2013): 316–30. http://dx.doi.org/10.1175/waf-d-12-00053.1.

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Abstract A wind-wave forecast system, designed with the intention of generating unbiased ensemble wave forecasts for extreme wind events, is assessed. Wave hindcasts for 12 tropical cyclones (TCs) are forced using a wind analysis produced from a combination of the North American Regional Reanalysis (NARR) and a parametric wind model. The default drag parameterization is replaced by one that is more in line with recent studies where a cap at weak-to-moderate wind speeds is applied. Quadrant-based significant wave height (Hs) statistics are composited in a storm-relative reference frame and stratified by the radius of maximum wind, storm speed, and storm intensity. Improvements in Hs are gleaned from both downscaling the NARR winds and tuning the wave model. However, the paradigm whereby the drag coefficient depends solely on the wind speed is limiting. Results indicate that Hs is biased low in the right quadrants (for all statistical subcategories). Conversely, Hs is high biased in the left-rear quadrant even though the analysis wind field is underforecast there. At radii less than 100 nautical miles, the model peak wave direction is offset from the observed, with the model (buoy) peak more in line with (to the left of) the direction of the tropical cyclone motion. As a result, the predominant storm-relative wind direction, which is northwesterly in the left-rear quadrant, opposes that of the buoy peak wave direction, while the model peak is more crosswise with respect to the wind. This will likely reduce the magnitude of the wind stress in the model.

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28

Shakina, N. P., E. N. Skriptunova, and A. A. Zav’yalova. "Orographic Turbulence Forecasting from Numerical Model Output Data." Meteorologiya i Gidrologiya, no. 1 (2021): 40–52. http://dx.doi.org/10.52002/0130-2906-2021-1-40-52.

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A method to forecast orographic turbulence based on the COSMO-Ru7 numerical model output data is presented. The calculation algorithm represents the critical amplitude approach for gravity waves and allows identifying zones of mountain wave breaking that generate turbulence. Its intensity is estimated through the wave drag value. The calculation was based on the model data for the territory of European Russia for April–September in 2019. Its results obtained from forecast data are in good agreement with those from initial model data for the forecast time. The turbulent zones are more intense at night than in the daytime and are situated over obstacles (for example, over the Crimean and Ural mountains) and on their lee slopes, in accordance with the physics of the phenomenon

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29

Linstone, H. A. "Corporate planning, forecasting, and the long wave." Futures 34, no. 3-4 (April 2002): 317–36. http://dx.doi.org/10.1016/s0016-3287(01)00047-7.

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30

Deo, M. C., and C. Sridhar Naidu. "Real time wave forecasting using neural networks." Ocean Engineering 26, no. 3 (August 1998): 191–203. http://dx.doi.org/10.1016/s0029-8018(97)10025-7.

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31

Mandal, S., and N. Prabaharan. "Ocean wave forecasting using recurrent neural networks." Ocean Engineering 33, no. 10 (July 2006): 1401–10. http://dx.doi.org/10.1016/j.oceaneng.2005.08.007.

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32

Zamani, Ahmadreza, Dimitri Solomatine, Ahmadreza Azimian, and Arnold Heemink. "Learning from data for wind–wave forecasting." Ocean Engineering 35, no. 10 (July 2008): 953–62. http://dx.doi.org/10.1016/j.oceaneng.2008.03.007.

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33

Gaur, Surabhi, and M. C. Deo. "Real-time wave forecasting using genetic programming." Ocean Engineering 35, no. 11-12 (August 2008): 1166–72. http://dx.doi.org/10.1016/j.oceaneng.2008.04.007.

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34

Shahabi, Sajad, Mohammad-Javad Khanjani, and Masoud-reza Hessami. "Significant Wave Height Forecasting using GMDH Model." International Journal of Computer Applications 133, no. 16 (January 15, 2016): 13–16. http://dx.doi.org/10.5120/ijca2016908129.

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35

Pinson, P., G. Reikard, and J. R. Bidlot. "Probabilistic forecasting of the wave energy flux." Applied Energy 93 (May 2012): 364–70. http://dx.doi.org/10.1016/j.apenergy.2011.12.040.

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36

Zhang, Zhixu, Chi-Wai Li, Yok-Sheung Li, and Yiquan Qi. "Incorporation of artificial neural networks and data assimilation techniques into a third-generation wind–wave model for wave forecasting." Journal of Hydroinformatics 8, no. 1 (January 1, 2006): 65–76. http://dx.doi.org/10.2166/jh.2006.005.

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Although the third-generation formulation of the ocean wave model describes the wave generation, dissipation and nonlinear interaction processes explicitly, many empirical parameters exist in the model which have to be determined experimentally. With the advance in oceanographic remote-sensing techniques, information on oceanic parameters including significant wave height (SWH) can be obtained daily by satellite altimeters. The assimilation of these data into the wave model provides a way of improving the hindcasting results. However, for wave forecasting, no altimeter data exist during the forecasting period, by definition. To improve the forecasting accuracy of the wave model, Artificial Neural Networks (ANN) are introduced to mimic the errors introduced by the wave model. This is achieved by training the ANN using the wave model output as input, and the results after data assimilation as the targeted output. The trained ANN is then used as a post-processor of the output from the wave model. The proposed method has been applied in wave simulation in the northwestern Pacific Ocean. The statistical interpolation method is used to assimilate the altimeter data into the wave model output and a back-propagation ANN is used to mimic the relation between the wave model outputs with or without data assimilation. The results show that an apparent improvement in the accuracy of forecasting can be obtained.

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37

Janssen, Peter A. E. M. "Quasi-linear Theory of Wind-Wave Generation Applied to Wave Forecasting." Journal of Physical Oceanography 21, no. 11 (November 1991): 1631–42. http://dx.doi.org/10.1175/1520-0485(1991)021<1631:qltoww>2.0.co;2.

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38

Kirezci, Cagil, and Alexander V. Babanin. "COUPLING SPECTRAL AND PHASE-RESOLVING WAVE MODEL FOR FORECASTING OF EXTREME WAVES IN WIND SEAS." Coastal Engineering Proceedings, no. 36 (December 30, 2018): 20. http://dx.doi.org/10.9753/icce.v36.waves.20.

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â€˜â€™Freakâ€™â€™ or â€˜â€™Rogueâ€™â€™ waves, when single individual wave height exceed two times of the significant wave height (Hi>2Hs), has been considered as one of the most dangerous sea states. Freak waves are believed to have caused many catastrophes, which result in ship damage and human casualties (Kharif and Pelinovsky, 2003). Occurrence of such waves are extremely unlikely according to Rayleigh distribution (Dean, 1990), however, in real ocean conditions occurrence of such events are higher than commonly used distributions. The main objective of this study is the coupling of Spectral WaveWatch III (WW3) model and phase resolving wave models, which will advance the application of the third generation wave models one-step further and increase the precision of model outputs and forecasting of such â€œunlikelyâ€ extreme conditions.

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39

Xiao, Bai, Hao Wang, and Gang Mu. "Spatial Load Forecasting Based on Load Forecasting Reliability." Applied Mechanics and Materials 672-674 (October 2014): 1075–80. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.1075.

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A spatial load forecasting method based on reliability of load forecasting is proposed. It calculates the correlation of wave comprehensive index, variance, maximum predictable ability of each power supply small area’s historical load data by using the analysis theory of grey degree based on the analysis of load forecasting error last target year. The weight of each factor effected on prediction outcomes according to the gray correlation degree is determined, then the load forecasting reliability model of each power supply area is constructed. Finally, by using the adjustment role of load forecasting reliability, the load of target year is forecasted. Actual example shows that the spatial load forecasting method based on reliability of load forecasting is correct and effective.

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40

Asghar, Malik Rizwan, Tomoki Ushiyama, Muhammad Riaz, and Mamoru Miyamoto. "Flood and Inundation Forecasting in the Sparsely Gauged Transboundary Chenab River Basin Using Satellite Rain and Coupling Meteorological and Hydrological Models." Journal of Hydrometeorology 20, no. 12 (December 1, 2019): 2315–30. http://dx.doi.org/10.1175/jhm-d-18-0226.1.

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Abstract Flood forecasting in a transboundary river basin is challenging due to insufficient data sharing between countries in the upper and lower reaches of a basin. A solution is the use of satellite-observed rainfall and numerical weather prediction (NWP) for hydrological forecasting. We applied this method to the transboundary sparsely gauged Chenab River basin in Pakistan and India to reproduce the exceptionally high flood in 2014. We employed global NWPs by three weather centers to consider forecast uncertainty and downscaled them using the Weather Research and Forecasting (WRF) Model to prepare precipitation inputs. For hydrological simulations, we used a kinematic wave model, the Integrated Flood Analysis System (IFAS), for the upper-reach basin with high mountains and steep slopes, and we used a diffusive-wave rainfall–runoff–inundation (RRI) model for low altitudes and mild slopes. In our forecasting experiment, the precipitation by the global NWP was not able to predict flood peaks consistently. However, the downscaled rainfall by regional NWP showed good performance in predicting flood waves quantitatively, and a multimodel approach provided added value in issuing reliable warning as early as 6 days in advance. A confident streamflow forecasting near the border of the countries also led to reliable inundation forecasting by the RRI model in the lower-reach basin.

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Hung, Nguyen Manh, Do Le Thuy, and Duong Cong Dien. "Storm wave modeling with swan comparison of measurement data and modeling results for the storm MUIFA 11/2004." Vietnam Journal of Mechanics 27, no. 4 (December 31, 2005): 229–39. http://dx.doi.org/10.15625/0866-7136/27/4/5733.

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With the average of 4-5 storms hitting the Vietnamese coastline every year, our country is suffering from great damage of infrastructures and lost of human life. Most storms induce significant waves which, especially in the coastal zones, can destroy houses, coastal structures and move large amounts of sand from beaches to offshore resulting in shoreline erosion. Therefore modeling of storm waves is an important task of engineers, scientists, weather forecasting specialists of our country. In order to meet the need of mitigation storm effects, to improve the storm wave forecasting capability in general and to study the coastal evolution in the Red River Delta (RRD) in particular, the authors have developed the S\i\TAN model for the storm wave calculation in the East Sea. In the paper some formulations of the model has been used to get the best agreement with the measured wave field in the storm MUIFA 11 /2004. The wind and wave data at the oil platform MSPl and wave height field over the sea were used to compare the model results. The obtained results afford the promising of using the SWAN model in research and weather forecast.

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Chen, Yanhui, Yingchao Zou, Yuzhen Zhou, and Chuan Zhang. "Multi-step-ahead Crude Oil Price Forecasting based on Grey Wave Forecasting Method." Procedia Computer Science 91 (2016): 1050–56. http://dx.doi.org/10.1016/j.procs.2016.07.147.

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43

Alves, Jose-Henrique G. M., Arun Chawla, Hendrik L. Tolman, David Schwab, Gregory Lang, and Greg Mann. "The Operational Implementation of a Great Lakes Wave Forecasting System at NOAA/NCEP*." Weather and Forecasting 29, no. 6 (December 1, 2014): 1473–97. http://dx.doi.org/10.1175/waf-d-12-00049.1.

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Abstract The development of a Great Lakes wave forecasting system at NOAA’s National Centers for Environmental Prediction (NCEP) is described. The system is an implementation of the WAVEWATCH III model, forced with atmospheric data from NCEP’s regional Weather Research and Forecasting (WRF) Model [the North American Mesoscale Model (NAM)] and the National Digital Forecast Database (NDFD). Reviews are made of previous Great Lakes wave modeling efforts. The development history of NCEP’s Great Lakes wave forecasting system is presented. A performance assessment is made of model wind speeds, as well as wave heights and periods, relative to National Data Buoy Center (NDBC) measurements. Performance comparisons are made relative to NOAA’s Great Lakes Environmental Research Laboratory (GLERL) wave prediction system. Results show that 1- and 2-day forecasts from NCEP have good skill in predicting wave heights and periods. NCEP’s system provides a better representation of measured wave periods, relative to the GLERL model in most conditions. Wave heights during storms, however, are consistently underestimated by NCEP’s current operational system, whereas the GLERL model provides close agreement with observations. Research efforts to develop new wave-growth parameterizations and overcome this limitation have led to upgrades to the WAVEWATCH III model, scheduled to become operational at NCEP in 2013. Results are presented from numerical experiments made with the new wave-model physics, showing significant improvements to the skill of NCEP’s Great Lakes wave forecasting system in predicting storm wave heights.

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Pascolo, Sara, Marco Petti, and Silvia Bosa. "Wave Forecasting in Shallow Water: A New Set of Growth Curves Depending on Bed Roughness." Water 11, no. 11 (November 5, 2019): 2313. http://dx.doi.org/10.3390/w11112313.

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Forecasting relationships have been recognized as an important tool to be applied together, or not, with complete numerical modelling in order to reconstruct the wave field in coastal areas properly when the available wave data is limited. In recent years, the literature has offered several comprehensive sets of field experiments investigating the form of the asymptotic, depth-limited wind waves. This has made it possible to reformulate the original deep water equations, taking into account the effects of water depth, if wind waves are locally generated in shallow and confined basins. The present paper is an initial attempt to further contribute to the shallow water forecasting curves which are currently available, also considering the role on the wave generation of a variable equivalent bottom roughness. This can offer the possibility of applying shallow growth curves to a broad variety of contexts, for which bed composition and forms can be different. Simple numerical tests have been conducted to reproduce the fully developed conditions of wave motion with variable roughness values. To validate the new set of equations, they have been applied to a real shallow lake for which both experimental and numerical wave data is available. The comparison of the obtained results is very encouraging in proceeding with this approach.

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45

Lenartz, F., J. M. Beckers, J. Chiggiato, C. Troupin, L. Vandenbulcke, and M. Rixen. "Super-ensemble techniques applied to wave forecast: performance and limitations." Ocean Science Discussions 7, no. 2 (March 16, 2010): 709–37. http://dx.doi.org/10.5194/osd-7-709-2010.

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Abstract. Nowadays, several operational forecasts of surface gravity waves are available for a same region through different forecasting systems. However, their results may considerably diverge, and choosing one single forecasting system among them is not an easy task. A recently developed approach consists in merging different forecasts and past observations into a single multi-model prediction system, called the super-ensemble. First implemented in meteorology, the method has also already been tested with success in oceanography for determining temperature, acoustic properties or surface drift. During the DART06 campaigns organized by the NURC, four wave forecasting systems were simultaneously run in the Adriatic Sea, while significant wave height was measured at six stations and along the tracks of two remote sensors, hence offering an opportunity to evaluate the skills of the super-ensemble techniques. The improvement shown during both the learning and testing periods was essentially due to a bias reduction, though the correlation was also increased. The possibility of extrapolating locally obtained results in the whole domain of interest was assessed against satellite observations. Though definitive conclusions can not be drawn from these experiments, the results open the door for further investigations.

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46

Wensink, H., and T. Schilperoort. "REAL-TIME WAVE FORECASTING WITH ADAPTIVE ARMAX MODELS." Coastal Engineering Proceedings 1, no. 20 (January 29, 1986): 63. http://dx.doi.org/10.9753/icce.v20.63.

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A method is described for improving the accuracy of the wave predictions which are required for the operational guidance of the shipping traffic in approach channels to harbours and to support offshore activities. The method, as being described here, is based on the assimilation of wave physics and real-time hydrometeorological observations into a statistical time series model called ARMAX.

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47

Reikard, Gordon, Pierre Pinson, and Jean-Raymond Bidlot. "Forecasting ocean wave energy: The ECMWF wave model and time series methods." Ocean Engineering 38, no. 10 (July 2011): 1089–99. http://dx.doi.org/10.1016/j.oceaneng.2011.04.009.

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48

Fusco, Francesco, and John V. Ringwood. "Short-Term Wave Forecasting for Real-Time Control of Wave Energy Converters." IEEE Transactions on Sustainable Energy 1, no. 2 (July 2010): 99–106. http://dx.doi.org/10.1109/tste.2010.2047414.

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49

Londhe, S. N., and Vijay Panchang. "One-Day Wave Forecasts Based on Artificial Neural Networks." Journal of Atmospheric and Oceanic Technology 23, no. 11 (November 1, 2006): 1593–603. http://dx.doi.org/10.1175/jtech1932.1.

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Abstract Sophisticated wave models like the Wave Model (WAM) and Simulating Waves Nearshore (SWAN)/WAVEWATCH are used nowadays along with atmospheric models to produce forecasts of ocean wave conditions. These models are generally run operationally on large ocean-scale domains. In many coastal areas, on the other hand, operational forecasting is not performed for a variety of reasons, yet the need for wave forecasts remains. To address such cases, the production of forecasts through the use of artificial neural networks and buoy measurements is explored. A modeling strategy that predicts wave heights up to 24 h on the basis of judiciously selected measurements over the previous 7 days was examined. A detailed investigation of this strategy using data from six National Data Buoy Center (NDBC) buoys with diverse geographical and statistical properties demonstrates that 6-h forecasts can be obtained with a high level of fidelity, and forecasts up to 12 h showed a correlation of 67% or better relative to a full year of data. One limitation observed was the inability of the artificial neural network model to correctly predict the magnitude of the highest waves; although the occurrence of high waves was predicted, the peaks were underestimated. The inclusion of several years of data and the judicious selection of the training set, especially the inclusion of extreme events, were shown to be crucial for the model to recognize interannual variability and provide more reliable forecasts. Real-time simulations performed for April 2005 demonstrate the efficiency of this technology for operational forecasting.

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Neumann, Gerhard. "NOTES ON THE GENERATION AND GROWTH OF OCEAN WAVES UNDER WIND ACTION." Coastal Engineering Proceedings 1, no. 3 (January 1, 2000): 7. http://dx.doi.org/10.9753/icce.v3.7.

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The basic problem of forecasting wind-generated waves is the development of equations which express the energy budget between wind and waves, and the derivation of physical laws governing the growth of the component wave trains. The waves can grow only in the case where the supply of energy by wind exceeds the loss of energy by friction and turbulence. Thus any attempt to calculate the growth of ocean waves under wind action requires a knowledge of the energy supply and the energy dissipation in every phase of wave development.

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