scholarly journals CLTS-Net: A More Accurate and Universal Method for the Long-Term Prediction of Significant Wave Height

2021 ◽  
Vol 9 (12) ◽  
pp. 1464
Author(s):  
Shuang Li ◽  
Peng Hao ◽  
Chengcheng Yu ◽  
Gengkun Wu

Significant wave height (SWH) prediction plays an important role in marine engineering areas such as fishery, exploration, power generation, and ocean transportation. For long-term forecasting of a specific location, classical numerical model wave height forecasting methods often require detailed climatic data and incur considerable calculation costs, which are often impractical in emergencies. In addition, how to capture and use the dynamic correlation between multiple variables is also a major research challenge for multivariate SWH prediction. To explore a new method for predicting SWH, this paper proposes a deep neural network model for multivariate time series SWH prediction—namely, CLTS-Net. In this study, the sea surface wind and wave height in the ERA5 dataset of the relevant points P1, P2, and P3 from 2011 to 2018 were used as input information to train the model and evaluate the model’s SWH prediction performance. The results show that the correlation coefficients (R) of CLTS-Net are 0.99 and 0.99, respectively, in the 24 h and 48 h SWH forecasts at point P1 along the coast. Compared with the current mainstream artificial intelligence-based SWH solutions, it is much higher than ANN (0.79, 0.70), RNN (0.82, 0.83), LSTM (0.93, 0.91), and Bi-LSTM (0.95, 0.94). Point P3 is located in the deep sea. In the 24 h and 48 h SWH forecasts, the R of CLTS-Net is 0.97 and 0.98, respectively, which are much higher than ANN (0.71, 0.72), RNN (0.85, 0.78), LSTM (0.85, 0.78), and Bi-LSTM (0.93, 0.93). Especially in the 72 h SWH forecast, when other methods have too large errors and have lost their practical application value, the R of CLTS-Net at P1, P2, and P3 can still reach 0.81, 0.71, and 0.98. The results also show that CLTS-Net can capture the short-term and long-term dependencies of data, so as to accurately predict long-term SWH, and has wide applicability in different sea areas.

1996 ◽  
Vol 118 (4) ◽  
pp. 284-291 ◽  
Author(s):  
C. Guedes Soares ◽  
A. C. Henriques

This work examines some aspects involved in the estimation of the parameters of the probability distribution of significant wave height, in particular the homogeneity of the data sets and the statistical methods of fitting a distribution to data. More homogeneous data sets are organized by collecting the data on a monthly basis and by separating the simple sea states from the combined ones. A three-parameter Weibull distribution is fitted to the data. The parameters of the fitted distribution are estimated by the methods of maximum likelihood, of regression, and of the moments. The uncertainty involved in estimating the probability distribution with the three methods is compared with the one that results from using more homogeneous data sets, and it is concluded that the uncertainty involved in the fitting procedure can be more significant unless the method of moments is not considered.


2020 ◽  
Vol 8 (12) ◽  
pp. 1015
Author(s):  
Alicia Takbash ◽  
Ian R. Young

A non-stationary extreme value analysis of 41 years (1979–2019) of global ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis) significant wave height data is undertaken to investigate trends in the values of 100-year significant wave height, Hs100. The analysis shows that there has been a statistically significant increase in the value of Hs100 over large regions of the Southern Hemisphere. There have also been smaller decreases in Hs100 in the Northern Hemisphere, although the related trends are generally not statistically significant. The increases in the Southern Hemisphere are a result of an increase in either the frequency or intensity of winter storms, particularly in the Southern Ocean.


Author(s):  
Adil Rasheed ◽  
Jakob Kristoffer Süld ◽  
Mandar Tabib

Accurate prediction of near surface wind and wave height are important for many offshore activities like fishing, boating, surfing, installation and maintenance of marine structures. The current work investigates the use of different methodologies to make accurate predictions of significant wave height and local wind. The methodology consists of coupling an atmospheric code HARMONIE and a wave model WAM. Two different kinds of coupling methodologies: unidirectional and bidirectional coupling are tested. While in Unidirectional coupling only the effects of atmosphere on ocean surface are taken into account, in bidirectional coupling the effects of ocean surface on the atmosphere are also accounted for. The predicted values of wave height and local wind at 10m above the ocean surface using both the methodologies are compared against observation data. The results show that during windy conditions, a bidirectional coupling methodology has better prediction capability.


1978 ◽  
Vol 1 (16) ◽  
pp. 2 ◽  
Author(s):  
Michel K. Ochi

This paper discusses the statistical properties of long-term ocean and coastal waves derived from analysis of available data. It was found from the results of the analysis that the statistical properties of wave height and period obey the bi-variate log-normal probability law. The method to determine the confidence domain for a specified confidence coefficient is presented so that reliable information in severe seas where data are always sparse can be obtained from a contingency table. Estimation of the extreme significant wave height expected in the long-term is also discussed.


2015 ◽  
Vol 12 (6) ◽  
pp. 2955-3001
Author(s):  
H. Cannaby ◽  
M. D. Palmer ◽  
T. Howard ◽  
L. Bricheno ◽  
D. Calvert ◽  
...  

Abstract. Singapore is an island state with considerable population, industries, commerce and transport located in coastal areas at elevations less than 2 m making it vulnerable to sea-level rise. Mitigation against future inundation events requires a quantitative assessment of risk. To address this need, regional projections of changes in (i) long-term mean sea level and (ii) the frequency of extreme storm surge and wave events have been combined to explore potential changes to coastal flood risk over the 21st century. Local changes in time mean sea level were evaluated using the process-based climate model data and methods presented in the IPCC AR5. Regional surge and wave solutions extending from 1980 to 2100 were generated using ~ 12 km resolution surge (Nucleus for European Modelling of the Ocean – NEMO) and wave (WaveWatchIII) models. Ocean simulations were forced by output from a selection of four downscaled (~ 12 km resolution) atmospheric models, forced at the lateral boundaries by global climate model simulations generated for the IPCC AR5. Long-term trends in skew surge and significant wave height were then assessed using a generalised extreme value model, fit to the largest modelled events each year. An additional atmospheric solution downscaled from the ERA-Interim global reanalysis was used to force historical ocean model simulations extending from 1980–2010, enabling a quantitative assessment of model skill. Simulated historical sea surface height and significant wave height time series were compared to tide gauge data and satellite altimetry data respectively. Central estimates of the long-term mean sea level rise at Singapore by 2100 were projected to be 0.52 m (0.74 m) under the RCP 4.5 (8.5) scenarios respectively. Trends in surge and significant wave height 2 year return levels were found to be statistically insignificant and/or physically very small under the more severe RCP8.5 scenario. We conclude that changes to long-term mean sea level constitute the dominant signal of change to the projected inundation risk for Singapore during the 21st century. We note that the largest recorded surge residual in the Singapore Strait of ~ 84 cm lies between the central and upper estimates of sea level rise by 2100, highlighting the vulnerability of the region.


2017 ◽  
Author(s):  
M. M. Amrutha ◽  
V. Sanil Kumar

Abstract. The growth and decay of surface wind-waves during one-month period in a typical Indian summer monsoon is investigated based on the data collected at 9 to 15 m water depth at 4 locations in the nearshore waters of the eastern Arabian Sea covering a spatial distance of ~ 350 km. The significant wave height varied from 0.7 to 5.5 m during the data collection considered in the analysis. The heights of waves during the measurement period often exceed 3 m. The most extreme wave height is 1.50 to 1.62 times the significant wave height and the most extreme crest height of the wave is 1.23 to 1.35 times the significant wave height of the same 30-minutes record. The average ratio of crest height of the wave to the height of the same wave is 0.58 to 0.67. The height of waves having maximum crest height is smaller than the maximum wave height during 30 minutes period. Measured waves are predominantly swell, but since the majority of wave generation during the monsoon is adjacent to the study area and the wind–wave coupling is strong, wave periods are rarely above 15 s. The numerical wave model could estimate the wave height reasonably well during the wave growth compared to the wave decay period. Hovmöller diagrams show a considerable spatial variability in the wave and wind pattern in the Indian Ocean during the high wave event at the eastern Arabian Sea.


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