Predicting significant wave height off the northeast coast of the United States

2007 ◽  
Vol 34 (8-9) ◽  
pp. 1328-1335 ◽  
Author(s):  
Edgar L Andreas ◽  
Sinny Wang
Keyword(s):  
United States ◽  
Wave Height ◽  
Significant Wave Height ◽  
The United States ◽  
Significant Wave ◽  
Northeast Coast

scholarly journals NEARSHORE WAVE SPECTRA MEASURED DURING HURRICANE DAVID

1982 ◽  
Vol 1 (18) ◽  
pp. 5
Author(s):  
Michel K. Ochi ◽  
Ming-Hsing Chiu
Keyword(s):  
United States ◽  
Wave Height ◽  
Deep Water ◽  
Significant Wave Height ◽  
The United States ◽  
Wave Spectra ◽  
Significant Wave ◽  
Stages Of Growth

This paper presents the results of an analysis carried out on wave spectra measured at three nearshore sites along the United States Florida coast when Hurricane DAVID passed these sites in 1979. Included are (a) the variability of the shapes of wave spectra during the stages of growth and decay of the hurricane-generated seas, (b) the presentation of spectra according to various spectral formulations, and (c) a comparison between spectra measured at the coastal sites and those measured in deep water for the same severity (significant wave height) of hurricane-generated seas.

Inferring significant wave height from seismic data: A comparison between methods.

2021 ◽  
Author(s):  
Francisco Bolrão ◽  
Co Tran ◽  
Miguel Lima ◽  
Sheroze Sheriffdeen ◽  
Diogo Rodrigues ◽  
...  
Keyword(s):  
Real Time ◽  
Wave Height ◽  
Seismic Data ◽  
Significant Wave Height ◽  
Transfer Functions ◽  
Seismic Station ◽  
Meteorological Data ◽  
Ocean Waves ◽  
The United States ◽  
Significant Wave

<p>The most pervasive seismic signal recorded on our planet – microseismic ambient noise -results from the coupling of energy between atmosphere, oceans and solid Earth. Because it carries information on ocean waves (source), the microseismic wavefield can be advantageously used to image ocean storms. This imaging is of interest both to climate studies – by extending the record of oceanic activity back into the early instrumental seismic record – and to real-time monitoring – where real-time seismic data can potentially be used to complement the spatially dense but temporally sparse satellite meteorological data.<br>In our work, we develop empirical transfer functions between seismic observations and ocean activity observations, in particular, significant wave height. We employ three different approaches: 1) The approach of Ferretti et al (2013), who compute a seismic significant wave height and invert only for the empirical conversion parameters between oceanic and seismic significant wave heights; 2) The classical approach of Bromirski et al (1999), who computed an empirical transfer function between ground-motion recorded at a coastal seismic station and significant wave height measured at a nearby ocean buoy; and 3) A novel recurrent neural-network (RNN) approach to infer significant wave height from seismic data. <br>We apply the three approaches to seismic and ocean buoy data recorded in the east coast of the United States. All three approaches are able to successfully predict ocean significant wave height from the seismic data. We compare the three approaches in terms of accuracy, computational effort and robustness. In addition, we investigate the regimes where each approach works best.  The results show that the RNN approach is able to predict well the significant wave height recorded at the buoy. The prediction is improved if several nearby seismic stations are used rather than just one. <br>This work is supported by FCT through projects UIDB/50019/2020 – IDL and UTAP-EXPL/EAC/0056/2017 - STORM.</p>

scholarly journals On Correlation between Wind and Wave Storms

2021 ◽  
Vol 9 (12) ◽  
pp. 1426
Author(s):  
Valentina Laface ◽  
Felice Arena
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
Significant Wave Height ◽  
The United States ◽  
Storm Events ◽  
Significant Wave ◽  
Wind Speeds ◽  
Wave Heights ◽  
Significant Wave Heights ◽  
Sea Storm

The paper is focused on the formulation of an adequate criterion for associating wave storm events to the generating wind storm ones, and on the study of correlation between their characteristic parameters. In this context, the sea storm definition commonly used for storm identification from significant wave height data is adapted for wind storm, by processing wind speed data. A sensitivity analysis is proposed as function of the storm thresholds aiming at identifying optimal combination of wind speed and significant wave height thresholds allowing the association of relatively large number of events ensuring high correlation between wind and wave storm parameters. The analysis is carried out using as input data wind speeds and significant wave heights from four meteorological (buoys and anemometers) stations of the National Data Buoy Center moored off the East Coast of the United States. Results reveal that an optimal threshold combination is achieved assuming both wind speed and significant wave height threshold as 1.5 time their respective averages.

Water Level and Wave Height Estimates at NOAA Tide Stations from Acoustic and Microwave Sensors

2014 ◽  
Vol 31 (10) ◽  
pp. 2294-2308 ◽  
Author(s):  
Joseph Park ◽  
Robert Heitsenrether ◽  
William Sweet
Keyword(s):  
Standard Deviation ◽  
Water Level ◽  
Wave Height ◽  
Significant Wave Height ◽  
The United States ◽  
Water Levels ◽  
Acoustic System ◽  
Significant Wave ◽  
Height Estimates ◽  
Microwave Radar

Abstract The National Oceanic and Atmospheric Administration (NOAA) is transitioning the primary water level sensor at the majority of tide stations in the National Water Level Observation Network (NWLON) from an acoustic ranging system to a microwave radar system. Field comparison of the acoustic and microwave systems finds statistically equivalent performance when temperature gradients between the acoustic sensor and water surface are small and when significant wave height is less than roughly 0.5 m. When significant wave height is greater than approximately 0.5–1 m, the acoustic system consistently reports lower water levels. An analysis of 2 months of acoustic and microwave water level data at Duck, North Carolina, finds that the majority of differences between the two sensors can be attributed to systemic errors in the acoustic system and that the microwave system captures water level variability with higher fidelity than the acoustic system. NWLON real-time data products include the water level standard deviation, a statistic that can serve as a proxy for significant wave height. This study identifies 29 coastal water level stations that are candidates for monitoring wave height based on water level standard deviation, potentially adding a significant source of data for the sparsely sampled coastal wave fields around the United States, and finds that the microwave sensor is better suited than the acoustic system for wave height estimates.

On the Sensitivity of Passive Multistatic Radar Amplitude and Doppler Measurements to Significant Wave Height

2021 ◽  
pp. 1-5
Author(s):  
Jeffrey D. Ouellette ◽  
William T. Bounds ◽  
David J. Dowgiallo ◽  
Jakov V. Toporkov ◽  
Paul A. Hwang
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Significant Wave ◽  
Multistatic Radar

scholarly journals Estimation of Significant Wave Heights from ASCAT Scatterometer Data via Deep Learning Network

2021 ◽  
Vol 13 (2) ◽  
pp. 195
Author(s):  
He Wang ◽  
Jingsong Yang ◽  
Jianhua Zhu ◽  
Lin Ren ◽  
Yahao Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wave Height ◽  
Significant Wave Height ◽  
Incidence Angle ◽  
Ocean Waves ◽  
Global Scale ◽  
Learning Technology ◽  
Sea State ◽  
Significant Wave ◽  
Wave Heights

Sea state estimation from wide-swath and frequent-revisit scatterometers, which are providing ocean winds in the routine, is an attractive challenge. In this study, state-of-the-art deep learning technology is successfully adopted to develop an algorithm for deriving significant wave height from Advanced Scatterometer (ASCAT) aboard MetOp-A. By collocating three years (2016–2018) of ASCAT measurements and WaveWatch III sea state hindcasts at a global scale, huge amount data points (>8 million) were employed to train the multi-hidden-layer deep learning model, which has been established to map the inputs of thirteen sea state related ASCAT observables into the wave heights. The ASCAT significant wave height estimates were validated against hindcast dataset independent on training, showing good consistency in terms of root mean square error of 0.5 m under moderate sea condition (1.0–5.0 m). Additionally, reasonable agreement is also found between ASCAT derived wave heights and buoy observations from National Data Buoy Center for the proposed algorithm. Results are further discussed with respect to sea state maturity, radar incidence angle along with the limitations of the model. Our work demonstrates the capability of scatterometers for monitoring sea state, thus would advance the use of scatterometers, which were originally designed for winds, in studies of ocean waves.

scholarly journals Physical Modelling of the Effect on the Wave Field of the WaveCat Wave Energy Converter

2021 ◽  
Vol 9 (3) ◽  
pp. 309
Author(s):  
James Allen ◽  
Gregorio Iglesias ◽  
Deborah Greaves ◽  
Jon Miles
Keyword(s):  
Wave Height ◽  
Wave Energy ◽  
Significant Wave Height ◽  
Wedge Angle ◽  
Wave Energy Converter ◽  
Wave Period ◽  
Surface Elevation ◽  
Energy Converter ◽  
Significant Wave ◽  
Model Scale

The WaveCat is a moored Wave Energy Converter design which uses wave overtopping discharge into a variable v-shaped hull, to generate electricity through low head turbines. Physical model tests of WaveCat WEC were carried out to determine the device reflection, transmission, absorption and capture coefficients based on selected wave conditions. The model scale was 1:30, with hulls of 3 m in length, 0.4 m in height and a freeboard of 0.2 m. Wave gauges monitored the surface elevation at discrete points around the experimental area, and level sensors and flowmeters recorded the amount of water captured and released by the model. Random waves of significant wave height between 0.03 m and 0.12 m and peak wave periods of 0.91 s to 2.37 s at model scale were tested. The wedge angle of the device was set to 60°. A reflection analysis was carried out using a revised three probe method and spectral analysis of the surface elevation to determine the incident, reflected and transmitted energy. The results show that the reflection coefficient is highest (0.79) at low significant wave height and low peak wave period, the transmission coefficient is highest (0.98) at low significant wave height and high peak wave period, and absorption coefficient is highest (0.78) when significant wave height is high and peak wave period is low. The model also shows the highest Capture Width Ratio (0.015) at wavelengths on the order of model length. The results have particular implications for wave energy conversion prediction potential using this design of device.

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