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

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.

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

Remote Sensing ◽

10.3390/rs13020195 ◽

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.

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Sea State Estimation Based on Ship Motions Measurements and Data Fusion

Volume 5: Ocean Engineering ◽

10.1115/omae2013-10657 ◽

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.

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

Journal of Marine Science and Engineering ◽

10.3390/jmse8121039 ◽

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.

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Neural Networks Based Simulation of Significant Wave Height

Volume 4: Materials Technology; Ocean Engineering ◽

10.1115/omae2007-29171 ◽

2007 ◽

Cited By ~ 3

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.

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An Empirical Method for the Estimation of Towing Resistance of a Life Raft in Various Sea States

Volume 2: Structures, Safety and Reliability; Petroleum Technology Symposium ◽

10.1115/omae2007-29059 ◽

2007 ◽

Author(s):

Lawrence Mak ◽

Andrew Kuczora ◽

Antonio Simo˜es Re´

Keyword(s):

Wave Height ◽

Significant Wave Height ◽

Rayleigh Distribution ◽

Empirical Method ◽

Extreme Value Statistics ◽

Sea Trial ◽

Significant Wave ◽

Calm Water ◽

Wave Heights ◽

Force Variation

Current IMO regulations require life rafts to be tow tested only in calm water. In real evacuation situations, life rafts are deployed in the prevailing environmental conditions, with wind and waves. Added wave resistance is small at low wave heights but increases nonlinearly with increased wave height. If life rafts are to be towed in moderate seas (up to 4 m significant wave height), tow force estimates based only on calm water tow resistance become less reliable. Tow patches, towline, towing craft etc. also need to be designed to withstand dynamic wave loading in addition to mean load. Therefore, mean tow force, tow force variation and maximum tow force are important. A full-scale 16-person, commercially available, SOLAS approved life raft was towed in the tank, in upwind, head seas with significant wave height of 0.5 m. The measured tow force showed that it could be treated as a linear system with wave amplitude, by demonstrating that tow force is mainly inertial and follows a Rayleigh distribution. Therefore, extreme-value statistics used for waves can be applied to developing equations for predicting tow force. A method is proposed to predict life raft tow force at different tow speeds and in various sea states, with waves and wind. The method involved using tank experiments to obtain tow force response for one sea state. The information can then be used to predict life raft tow force in wind and waves for different sea states. Three equations are proposed to demonstrate that a simple tank experiment could provide valuable information necessary to empirically estimate the mean tow force, tow force variation and maximum tow force for a specific life raft in different sea states. The equations are developed for upwind, head seas. These equations were extensively validated using tow force measured in the tank. They were partially validated with limited sea trial data, by towing the same 16-person life raft and a 42-person life raft in upwind, head seas with significant wave height of 1.3 m. The equations were able to predict maximum tow forces to within 15% of the measured.

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Wave climate extremes from the Sea State CCI satellite data

10.5194/egusphere-egu21-7492 ◽

2021 ◽

Author(s):

Marta Ramirez ◽

Melisa Menendez ◽

Guillaume Dodet

Keyword(s):

Wave Height ◽

Satellite Data ◽

Significant Wave Height ◽

Regional Analysis ◽

Wave Climate ◽

Extratropical Cyclones ◽

Extreme Waves ◽

Extreme Wave ◽

Sea State ◽

Significant Wave

The knowledge of ocean extreme wave climate is of significant importance for a number of coastal and marine activities (e.g. coastal protection, marine spatial planning, offshore engineering). This study uses the recently released Sea State CCI v1 altimeter product to analyze extreme wave climate conditions at global scale. The dataset comprises 28-years inter-calibrated and denoised significant wave height data from 10 altimeter missions.First, a regional analysis of the available satellite information of extreme waves associated with both, tropical and extratropical cyclones, is carried out. As tropical cyclones, we analyze two intense events which affected the Florida Peninsula and Caribbean Islands: Wilma (in October 2005) and Irma (in August 2017) hurricanes. As extratropical cyclones, we focused on the extreme waves during the 2013-2014 winter season along the Atlantic European coasts. The extreme waves associated with these events are identified in the satellite dataset and are compared with in situ and high-resolution simulated data. The analysis of the satellite data during the storm tracks and its comparison against other data sources indicate that satellite data can provide added value for the analysis of extreme wave conditions that caused important coastal damages.After assessing the quality of extreme wave data measured by altimeters from this regional analysis, we explore a method to characterize wave height return values (e.g. 50yr return period significant wave height) from the multi-mission satellite data. The method is validated through comparisons with return values estimated from long-term wave buoy records. The extreme analysis is based on monthly maxima of satellite significant wave height computed over marine areas of varying extensions and centered on a target location (e.g. the wave buoy location for comparison and validation of the method).&#160; The extension of the areas is defined from a seasonal study of the spatial correlation and the error metrics of the satellite data against the selected coastal location. We found a threshold of 0.85 correlation as the isoline to select the satellite data subsample (i.er. larger areas to select satellite maxima are found during winter seasons). Finally, a non-stationary extreme model based on GEV distribution is applied to obtain quantiles of low probability. Outcomes from satellite data are validated against extreme estimates from buoy records.

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Significant wave height extraction using a low‐frequency HFSWR system under low sea state

IET Radar Sonar & Navigation ◽

10.1049/iet-rsn.2018.0044 ◽

2018 ◽

Vol 12 (6) ◽

pp. 671-677 ◽

Cited By ~ 2

Author(s):

Xiaodong Li ◽

Changjun Yu ◽

Fulin Su ◽

Taifan Quan ◽

Xiaoliang Chu ◽

...

Keyword(s):

Wave Height ◽

Significant Wave Height ◽

Low Frequency ◽

Sea State ◽

Significant Wave

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Round Robin Assessment of Radar Altimeter Low Resolution Mode and Delay-Doppler Retracking Algorithms for Significant Wave Height

Remote Sensing ◽

10.3390/rs12081254 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1254 ◽

Cited By ~ 6

Author(s):

Florian Schlembach ◽

Marcello Passaro ◽

Graham D. Quartly ◽

Andrey Kurekin ◽

Francesco Nencioli ◽

...

Keyword(s):

Climate Change ◽

Wave Height ◽

Significant Wave Height ◽

Low Resolution ◽

Spectral Variability ◽

Sea State ◽

Significant Wave ◽

In Situ Data ◽

Wave Models

Radar altimeters have been measuring ocean significant wave height for more than three decades, with their data used to record the severity of storms, the mixing of surface waters and the potential threats to offshore structures and low-lying land, and to improve operational wave forecasting. Understanding climate change and long-term planning for enhanced storm and flooding hazards are imposing more stringent requirements on the robustness, precision, and accuracy of the estimates than have hitherto been needed. Taking advantage of novel retracking algorithms, particularly developed for the coastal zone, the present work aims at establishing an objective baseline processing chain for wave height retrieval that can be adapted to all satellite missions. In order to determine the best performing retracking algorithm for both Low Resolution Mode and Delay-Doppler altimetry, an objective assessment is conducted in the framework of the European Space Agency Sea State Climate Change Initiative project. All algorithms process the same Level-1 input dataset covering a time-period of up to two years. As a reference for validation, an ERA5-based hindcast wave model as well as an in-situ buoy dataset from the Copernicus Marine Environment Monitoring Service In Situ Thematic Centre database are used. Five different metrics are evaluated: percentage and types of outliers, level of measurement noise, wave spectral variability, comparison against wave models, and comparison against in-situ data. The metrics are evaluated as a function of the distance to the nearest coast and the sea state. The results of the assessment show that all novel retracking algorithms perform better in the majority of the metrics than the baseline algorithms currently used for operational generation of the products. Nevertheless, the performance of the retrackers strongly differ depending on the coastal proximity and the sea state. Some retrackers show high correlations with the wave models and in-situ data but significantly under- or overestimate large-scale spectral variability. We propose a weighting scheme to select the most suitable retrackers for the Sea State Climate Change Initiative programme.

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Error estimation of buoy, altimeter, and model significant wave height from triple collocation technique

10.5194/egusphere-egu21-14753 ◽

2021 ◽

Author(s):

Guillaume Dodet ◽

Jean-Raymond Bidlot ◽

Mickaël Accensi ◽

Mathias Alday ◽

Saleh Abdalla ◽

...

Keyword(s):

Wave Height ◽

Significant Wave Height ◽

Wave Model ◽

Sea State ◽

Significant Wave ◽

In Situ Data ◽

Collocation Technique ◽

Coastal Risk ◽

Marine Engineering

Ocean wave information is of major importance for a number of applications including climate studies, safety at sea, marine engineering (offshore and coastal), and coastal risk management. Depending on the scales and regions of interest, several data sources may be considered (e.g. in situ data, VOS observations, altimeter records, numerical wave model), each one with its pros and cons. In order to optimize the use of multiple source wave information (e.g. through assimilation scheme in NWP), the error characteristics of each measurement system need to be investigated and inter-compared. In this study, we use triple collocation technique to estimate the random error variances of significant wave height from in situ, altimeter and model data. The buoy dataset is a selection of ~100 in-situ measuring stations provided by the CMEMS In-Situ Thematic Assembly Center. The altimeter dataset is composed of the ESA Sea State CCI V1.1 L2P product. The model dataset is the result of WW3 Ifremer hindcast run forced with ERA5 winds using the recently updated T475 parameterization. In comparisons to previous studies using similar techniques, the large triple collocation dataset (~450 000 matchups in total) generated for this study provides some new insights on the error variability within in situ stations, satellite missions and upon sea state conditions.Moreover, the results of the triple collocation technique help developing improved calibration of the altimeter missions included in the ESA Sea State CCI V1.1 dataset.

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