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.

Sea State Estimation Based on Ship Motions Measurements and Data Fusion

2013 ◽  
Author(s):  
Céline Drouet ◽  
Nicolas Cellier ◽  
Jérémie Raymond ◽  
Denis Martigny
Keyword(s):  
Data Fusion ◽  
State Estimation ◽  
Wave Height ◽  
Significant Wave Height ◽  
Wave Spectrum ◽  
Ocean Waves ◽  
Ship Motions ◽  
Sea State ◽  
Significant Wave ◽  
X Band

In-service monitoring can help to increase safety of ships especially regarding the fatigue assessment. For this purpose, it is compulsory to know the environmental conditions encountered: wind, but also the full directional wave spectrum. During the EU TULCS project, a full scale measurements campaign has been conducted onboard the CMA-CGM 13200 TEU container ship Rigoletto. She has been instrumented to measure deformation of the ship as well as the sea state encountered during its trip. This paper will focus on the sea state estimation. Three systems have been installed to estimate the sea state encountered by the Rigoletto: An X-band radar from Ocean Waves with WAMOS® system and two altimetric wave radars from RADAC®. Nevertheless, the measured significant wave height can be disturbed by several external elements like bow waves, sprays, sea surface ripples, etc… Furthermore, ship motions are also measured and can provide another estimation of the significant wave height using a specific algorithm developed by DCNS Research for the TULCS project. As all those estimations are inherently different, it is necessary to make a fusion of those data to provide a single estimation (“best estimate”) of the significant wave height. This paper will present the data fusion process developed for TULCS and show some first validation results.

Climatology, Variability and Extrema of Ocean Waves: The Web-Based KNMI/ERA-40 Wave Atlas

2006 ◽  
Author(s):  
Andreas Sterl ◽  
Sofia Caires
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Wave Model ◽  
Ocean Waves ◽  
Wave Period ◽  
Web Based ◽  
Significant Wave ◽  
Wave Parameters ◽  
Wave Heights ◽  
The Web

The European Centre for Medium Range Weather Forecasts (ECMWF) has recently finished ERA-40, a reanalysis covering the period September 1957 to August 2002. One of the products of ERA-40 consists of 6-hourly global fields of wave parameters like significant wave height and wave period. These data have been generated with the Centre’s WAM wave model. From these results the authors have derived climatologies of important wave parameters, including significant wave height, mean wave period, and extreme significant wave heights. Particular emphasis is on the variability of these parameters, both in space and time. Besides for scientists studying climate change, these results are also important for engineers who have to design maritime constructions. This paper describes the ERA-40 data and gives an overview of the results derived. The results are available on a global 1.5° × 1.5° grid. They are accessible from the web-based KNMI/ERA-40 Wave Atlas at http://www.knmi.nl/waveatlas.

scholarly journals The Wide Swath Significant Wave Height: An Innovative Reconstruction of Significant Wave Heights From CFOSAT’s SWIM and Scatterometer Using Deep Learning

2021 ◽  
Vol 48 (6) ◽  
Author(s):  
J. K. Wang ◽  
L. Aouf ◽  
A. Dalphinet ◽  
Y. G. Zhang ◽  
Y. Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wave Height ◽  
Significant Wave Height ◽  
Significant Wave ◽  
Wave Heights ◽  
Significant Wave Heights ◽  
Wide Swath

A new method for retrieve significant wave height using deep learning

2021 ◽  
Author(s):  
S. Chen ◽  
C. Zheng ◽  
L. Sun ◽  
J. Yang ◽  
X. Li
Keyword(s):  
Deep Learning ◽  
Wave Height ◽  
Significant Wave Height ◽  
New Method ◽  
Significant Wave

Development of Operational Limit Diagrams for Offshore Lifting Procedures

2015 ◽  
Author(s):  
Leonardo Roncetti ◽  
Fabrício Nogueira Corrêa ◽  
Carl Horst Albrecht ◽  
Breno Pinheiro Jacob
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Technological Development ◽  
Automatic Generation ◽  
Dynamic Responses ◽  
Offshore Platforms ◽  
Peak Period ◽  
Significant Wave ◽  
Wave Heights ◽  
Offshore Crane

Lifting operations with offshore cranes are fundamental for proper functioning of a platform. Despite the great technological development, offshore cranes load charts only consider the significant wave height as parameter of environmental load, neglecting wave period, which may lead to unsafe or overestimated lifting operations. This paper aims to develop a method to design offshore crane operational limit diagrams for lifting of personnel and usual loads, in function of significant wave height and wave peak period, using time domain dynamic analysis, for a crane installed on a floating unit. The lifting of personnel with crane to transfer between a floating unit and a support vessel is a very used option in offshore operations, and this is in many cases, the only alternative beyond the helicopter. Due to recent fatal accidents with lifting operations in offshore platforms, it is essential the study about this subject, contributing to the increase of safety. The sea states for analysis were chosen covering usual significant wave heights and peak periods limits for lifting operations. The methodology used the SITUA / Prosim software to obtain the dynamic responses of the personnel transfer basket lifting and container loads on a typical FPSO. Through program developed by the author, it was implemented the automatic generation of diagrams as a function of operational limits. It is concluded that using this methodology, it is possible to achieve greater efficiency in the design and execution of personnel and routine load lifting, increasing safety and a wider weather window available.

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 Reliability of Extreme Significant Wave Height Estimation from Satellite Altimetry and In Situ Measurements in the Coastal Zone

2020 ◽  
Vol 8 (12) ◽  
pp. 1039
Author(s):  
Ben Timmermans ◽  
Andrew G. P. Shaw ◽  
Christine Gommenginger
Keyword(s):  
Coastal Zone ◽  
Wave Height ◽  
Satellite Data ◽  
Significant Wave Height ◽  
Wave Climate ◽  
In Situ Measurements ◽  
Altimeter Data ◽  
Sea State ◽  
Significant Wave

Measurements of significant wave height from satellite altimeter missions are finding increasing application in investigations of wave climate, sea state variability and trends, in particular as the means to mitigate the general sparsity of in situ measurements. However, many questions remain over the suitability of altimeter data for the representation of extreme sea states and applications in the coastal zone. In this paper, the limitations of altimeter data to estimate coastal Hs extremes (<10 km from shore) are investigated using the European Space Agency Sea State Climate Change Initiative L2P altimeter data v1.1 product recently released. This Sea State CCI product provides near complete global coverage and a continuous record of 28 years. It is used here together with in situ data from moored wave buoys at six sites around the coast of the United States. The limitations of estimating extreme values based on satellite data are quantified and linked to several factors including the impact of data corruption nearshore, the influence of coastline morphology and local wave climate dynamics, and the spatio-temporal sampling achieved by altimeters. The factors combine to lead to considerable underestimation of estimated Hs 10-yr return levels. Sensitivity to these factors is evaluated at specific sites, leading to recommendations about the use of satellite data to estimate extremes and their temporal evolution in coastal environments.

Neural Networks Based Simulation of Significant Wave Height

2007 ◽  
Author(s):  
H. Bazargan ◽  
H. Bahai ◽  
A. Aminzadeh-Gohari ◽  
A. Bazargan
Keyword(s):  
Neural Networks ◽  
Wave Height ◽  
Significant Wave Height ◽  
Optimization Technique ◽  
Conditional Probability Density ◽  
Significant Wave ◽  
The Pacific ◽  
Wave Heights ◽  
Significant Wave Heights ◽  
Long Term Forecasting

A large number of ocean activities call for real time or on-line forecasting of wind wave characteristics including significant wave height (Hs). The work reported in this paper uses statistics, and artificial neural networks trained with an optimization technique called simulated annealing to estimate the parameters of a probability distribution called hepta-parameter spline for the conditional probability density functions (pdf’s) of significant wave heights given their eight immediate preceding 3-hourly observed Hs’s. These pdf’s are used in the simulation of significant wave heights related to a location in the Pacific. The paper also deals with short and long term forecasting of Hs for the region through generating random variates from the spline distribution.

scholarly journals Capability of Jason-2 Subwaveform Retrackers for Significant Wave Height in the Calm Semi-Enclosed Celebes Sea

2020 ◽  
Vol 12 (20) ◽  
pp. 3367
Author(s):  
Kaoru Ichikawa ◽  
Xi-Feng Wang ◽  
Hitoshi Tamura
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Leading Edge ◽  
Sea State ◽  
Significant Wave ◽  
Contamination Sources ◽  
The Pacific ◽  
Unique System ◽  
Calm Water ◽  
Good Agreement

Satellite altimetry is a unique system that provides repeated observations of significant wave height (SWH) globally, but its measurements could be contaminated by lands, slicks, or calm water with smooth surface. In this study, capability of subwaveform retrackers against 20 Hz Jason-2 measurements is examined in the calm Celebes Sea. Distances between contamination sources and Jason-2 observation points can be determined using sequentially assembled adjacent waveforms (radargram). When no contamination sources are present within a Jason-2 footprint, subwaveform retrackers are in excellent agreement with the Sensor Geophysical Data Records (SGDR) MLE4 retracker that uses full-length waveforms, except that Adaptive Leading Edge Subwaveform (ALES) retracker has a positive bias in a calm sea state (SWH < 1 m), which is not unusual in the Celebes Sea. Meanwhile, when contamination sources exist within 4.5 km from Jason-2 observation points, SGDR occasionally estimates unrealistically large SWH values, although they could be partly eliminated by sigma0 filters. These datasets are then compared with WAVEWATCH III model, resulting in good agreement. The agreement becomes worse if swells from the Pacific is excluded in the model, suggesting constant presence of swells despite the semi-enclosed nature. In addition, outliers are found related with locally-confined SWH events, which could be inadequately represented in the model.

Uncertainties in Extreme Wave Height Estimates for Hurricane-Dominated Regions

2007 ◽  
Vol 129 (4) ◽  
pp. 300-305 ◽  
Author(s):  
Philip Jonathan ◽  
Kevin Ewans
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Small Data ◽  
Extreme Wave ◽  
Significant Wave ◽  
Wave Characteristics ◽  
Height Estimates ◽  
Wave Heights ◽  
Using Data ◽  
High Percentile

Inherent uncertainties in estimation of extreme wave heights in hurricane-dominated regions are explored using data from the GOMOS Gulf of Mexico hindcast for 1900–2005. In particular, the effect of combining correlated values from a neighborhood of 72 grid locations on extreme wave height estimation is quantified. We show that, based on small data samples, extreme wave heights are underestimated and site averaging usually improves estimates. We present a bootstrapping approach to evaluate uncertainty in extreme wave height estimates. We also argue in favor of modeling supplementary indicators for extreme wave characteristics, such as a high percentile (95%) of the distribution of 100-year significant wave height, in addition to its most probable value, especially for environments where the distribution of 100-year significant wave height is strongly skewed.

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