Sea State Statistics and Extreme Waves Observed by Satellite

2008 ◽  
Author(s):  
S. Lehner ◽  
J. Schulz-Stellenfleth ◽  
Thomas Ko¨nig ◽  
X. Li
Keyword(s):  
Extreme Events ◽  
Wave Spectrum ◽  
Ocean Waves ◽  
Wave Mode ◽  
Extreme Value Statistics ◽  
Altimeter Data ◽  
Sea State ◽  
Data Set ◽  
Wave Properties ◽  
D Wave

For the design of ships as well as for the investigation of ship accidents it is important to have knowledge about both the two dimensional spectral wave properties as well as extreme value statistics of ocean waves. Although numerical wave models have reached a high level of accuracy, they still have weaknesses with respect to the details of the 2-D wave spectrum. Furthermore standard models like WAM provide estimates of the 2-D wave spectrum, i.e., second order sea state statistics and therefore lack information on individual wave properties and the occurrence of extreme events. In this study the potential of global Synthetic Aperture Radar (SAR) wave mode data acquired by the European satellites ERS-2 and ENVISAT to investigate ship accidents is discussed and compared to altimeter data and ECMWF model results. These data are acquired independent of light and weather conditions on a global scale. A historic data set of ERS-2 wave mode data acquired between 1998 and 2000 is co-located with accidents which occurred during that time. ENVISAT ASAR wave mode data acquired since 2002 are considered, too. Different ocean wave parameters like significant wave height and wave periods are derived from the SAR data. The potential role of the respective wave conditions for some recent accident is discussed in detail. This includes in particular the analysis of cross sea conditions, groupiness and extreme events.

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.

scholarly journals Extreme weather exposure identification for road networks – a comparative assessment of statistical methods

2017 ◽  
Vol 17 (4) ◽  
pp. 515-531 ◽  
Author(s):  
Matthias Schlögl ◽  
Gregor Laaha
Keyword(s):  
Extreme Events ◽  
Information Gain ◽  
Extreme Weather ◽  
Extreme Value ◽  
Added Value ◽  
Extreme Value Statistics ◽  
Extreme Weather Events ◽  
Mean Residual Life ◽  
Data Set ◽  
Return Levels

Abstract. The assessment of road infrastructure exposure to extreme weather events is of major importance for scientists and practitioners alike. In this study, we compare the different extreme value approaches and fitting methods with respect to their value for assessing the exposure of transport networks to extreme precipitation and temperature impacts. Based on an Austrian data set from 25 meteorological stations representing diverse meteorological conditions, we assess the added value of partial duration series (PDS) over the standardly used annual maxima series (AMS) in order to give recommendations for performing extreme value statistics of meteorological hazards. Results show the merits of the robust L-moment estimation, which yielded better results than maximum likelihood estimation in 62 % of all cases. At the same time, results question the general assumption of the threshold excess approach (employing PDS) being superior to the block maxima approach (employing AMS) due to information gain. For low return periods (non-extreme events) the PDS approach tends to overestimate return levels as compared to the AMS approach, whereas an opposite behavior was found for high return levels (extreme events). In extreme cases, an inappropriate threshold was shown to lead to considerable biases that may outperform the possible gain of information from including additional extreme events by far. This effect was visible from neither the square-root criterion nor standardly used graphical diagnosis (mean residual life plot) but rather from a direct comparison of AMS and PDS in combined quantile plots. We therefore recommend performing AMS and PDS approaches simultaneously in order to select the best-suited approach. This will make the analyses more robust, not only in cases where threshold selection and dependency introduces biases to the PDS approach but also in cases where the AMS contains non-extreme events that may introduce similar biases. For assessing the performance of extreme events we recommend the use of conditional performance measures that focus on rare events only in addition to standardly used unconditional indicators. The findings of the study directly address road and traffic management but can be transferred to a range of other environmental variables including meteorological and hydrological quantities.

Towards a unified framework for maximum wave computation from numerical models: outcomes from the LATEMAR project.

2020 ◽  
Author(s):  
Alvise Benetazzo ◽  
Francesco Barbariol ◽  
Paolo Pezzutto ◽  
Luciana Bertotti ◽  
Luigi Cavaleri ◽  
...  
Keyword(s):  
Numerical Models ◽  
Wave Spectrum ◽  
Wave Model ◽  
Ocean Waves ◽  
Unified Framework ◽  
Sea State ◽  
Large Wave ◽  
Wavewatch Iii ◽  
Wave Models ◽  
The Impact

<p>Reliable prediction of oceanic waves during severe marine storms has always been foremost for offshore platform design, coastal activities, and navigation safety. Indeed, many damaging accidents and casualties during storms were ascribed to the impact with abnormal and unexpected waves. However, predicting extreme wave occurrence is a challenging task, at first, because of their inherent randomness, and because the observation of large ocean waves, of primary importance to assess theoretical and numerical models, is limited by the costs and risks of deployment during severe open-ocean sea-state conditions.</p><p>In the context of the EU-based Copernicus Marine Environment Monitoring Service (CMEMS) evolution, the LATEMAR project (https://www.mercator-ocean.fr/en/portfolio/latemar/) aimed at improving the modelling of large wave events during marine storms. Indeed, at present, operational systems only provide average and peak wave parameters, with no information on individual waves whatsoever. However, developments of the state-of-the-art third-generation wave models demonstrated that using the directional wave spectrum moments into theoretical statistical models for wave extremes, forecasters are able to accurately infer the expected shape and likelihood of the maximum waves during storms.</p><p>The main purpose of the activity is therefore to provide the wave models WAM and WAVEWATCH III with common procedures to explicitly estimate the maximum wave heights for each sea state. LATEMAR achieved this goal by: performing an extensive assessment of the model maximum waves using field observations collected from an oceanographic tower; comparing WAM and WAVEWATCH III maximum wave estimates in the Mediterranean Sea; investigating the sensitivity of the maximum waves on the main sea state parameters. All model developments and evaluations resulting from this research project will be directly applicable to the wave model forecasting systems to expand their catalogue.</p>

Ocean Wave Measurements From ENVISAT ASAR Data Using a Parametric Inversion Scheme

2004 ◽  
Author(s):  
J. Schulz-Stellenfleth ◽  
S. Lehner ◽  
D. Hoja ◽  
J. C. Nieto-Borge
Keyword(s):  
A Priori ◽  
Wave Spectrum ◽  
Global Scale ◽  
Wave Mode ◽  
Ocean Wave ◽  
Wave System ◽  
Wave Spectra ◽  
Data Set ◽  
Envisat Asar ◽  
Sar Imaging

A parametric algorithm is presented to estimate two-dimensional ocean wave spectra from ENVISAT ASAR wave mode data on a global scale. The retrieval scheme makes use of prior information taken from numerical wave models. The Partition Rescale and Shift algorithm (PARSA) is based on a partitioning technique, which splits an a priori wave spectrum into its wave system components. Integral parameters of these systems, such as mean direction, mean wavelength, waveheight, and directional spreading are then adjusted iteratively to improve the consistency with the SAR observation. The method takes into account the full nonlinear SAR imaging process and uses a maximum a posteriori approach, which is based on statistical model quantifying the errors of the SAR imaging model, the SAR measurement, and the prior wave spectra. The method is applied to a global data set of ENVISAT ASAR data acquired during the CAL/VAL phase. The benefit of cross spectra compared to conventional symmetric image spectra is demonstrated.

scholarly journals Extreme weather exposure identification for road networks – a comparative assessment of statistical methods

2016 ◽  
Author(s):  
Matthias Schlögl ◽  
Gregor Laaha
Keyword(s):  
Extreme Events ◽  
Extreme Weather ◽  
Extreme Value ◽  
Added Value ◽  
Extreme Value Statistics ◽  
Extreme Weather Events ◽  
Mean Residual Life ◽  
Data Set ◽  
Partial Duration Series ◽  
Return Levels

Abstract. The assessment of road infrastructure exposure to extreme weather events is of major importance for scientists and practitioners alike. In this study, we compare the different extreme value approaches and fitting methods with respect to their value for assessing the exposure of transport networks to extreme precipitation and temperature impacts. Based on an Austrian data set from 25 meteorological stations representing diverse meteorological conditions, we assess the added value of partial duration series over the standardly used annual maxima series in order to give recommendations for performing extreme value statistics of meteorological hazards. Results show the merits of the robust L-moment estimation, which yielded better results than maximum likelihood estimation in 62 % of all cases. At the same time, results question the general assumption of the threshold excess approach (employing partial duration series, PDS) being superior to the block maxima approach (employing annual maxima series, AMS) due to information gain. For low return periods (non-extreme events) the PDS approach tends to overestimate return levels as compared to the AMS approach, whereas an opposite behaviour was found for high return levels (extreme events). In extreme cases, an inappropriate threshold was shown to lead to considerable biases that may outperform the possible gain of information from including additional extreme events by far. This effect was neither visible from the square-root criterion, nor from standardly used graphical diagnosis (mean residual life plot), but from a direct comparison of AMS and PDS in synoptic quantile plots. We therefore recommend performing AMS and PDS approaches simultaneously in order to select the best suited approach. This will make the analyses more robust, in cases where threshold selection and dependency introduces biases to the PDS approach, but also in cases where the AMS contains non-extreme events that may introduce similar biases. For assessing the performance of extreme events we recommend conditional performance measures that focus on rare events only in addition to standardly used unconditional indicators. The findings of the study directly address road and traffic management, but can be transferred to a range of other environmental variables including meteorological and hydrological quantities.

X-Band Radar as a Tool to Determine Spectral and Single Wave Properties

2006 ◽  
Author(s):  
Konstanze Reichert ◽  
Katrin Hessner ◽  
Jens Dannenberg ◽  
Ina Traenkmann
Keyword(s):  
Real Time ◽  
Ocean Waves ◽  
Sea Surface ◽  
Surface Elevation ◽  
Radar Signal ◽  
Sea State ◽  
Single Wave ◽  
Radar Images ◽  
X Band ◽  
Wave Properties

The Wave Monitoring System WaMoS II was developed for real time measurements of directional ocean waves spectra to monitor the sea state from fixed platforms in deep water or coastal areas as well as from moving vessels. The system is based on a standard marine X-Band radar used for navigation and ship traffic control. WaMoS II digitises the analogous radar signal and analyses the sea clutter information to obtain directional wave spectra from the sea surface in real time even under harsh weather conditions and during night. Spectral sea state parameters such as significant wave height, peak wave period and peak wave direction both for wind sea and swell are derived. Within the EU funded project ‘MaxWave’ and the German project ‘SinSee’ new algorithms were developed to determine sea surface elevation maps from radar images which are used to investigate the spatial and temporal evolution of single waves simultaneously. In this paper a short overview describes the calculation of surface elevation maps and the detection of individual waves. Considering two case studies, the results of spatial single wave detection and corresponding temporal single wave properties are compared and discussed. Individual wave parameters derived from radar images are compared to individual waves measured by a buoy. An application of the method to characterise extreme sea states is discussed.

Derivation of Sea Ice Parameters Using ENVISAT ASAR Data

2002 ◽  
Author(s):  
Thomas Ko¨nig ◽  
Susanne Lehner ◽  
Johannes Schulz-Stellenfleth
Keyword(s):  
Sea Ice ◽  
Ocean Waves ◽  
Deformation Energy ◽  
Wave Mode ◽  
Polar Regions ◽  
Data Set ◽  
Basic Principles ◽  
Envisat Asar ◽  
Damping Characteristics ◽  
High Resolution Images

This study presents methods for sea ice analysis which make use of SAR wave mode data products, so-called imagettes, to be provided by the European satellite ENVISAT every 100 km along the track, thus forming a particulary dense coverage of the polar regions. Prior to ENVISAT a reprocessed ERS-2 wave mode data set is used to derive sea ice variables like deformation energy. The results are compared to sea ice model runs and SSM/I data. Furthermore, ENVISAT data are of particular interest for observation of the damping of ocean waves by sea ice as pairs of high resolution images with different polarizations will be available. It is e.g. shown that ENVISAT data allow a better distinction between effects due to ocean wave motion and mechanisms. The basic principles of extracting parameters describing sea ice damping characteristics are demonstrated using ERS-2 SAR data.

scholarly journals The Wave Heights Distribution of Random Wave Based on Ocean Basin

Kapal ◽  
2020 ◽  
Vol 17 (3) ◽  
pp. 114-122
Author(s):  
Nurman Firdaus ◽  
Baharuddin Ali ◽  
Mochammad Nasir ◽  
M Muryadin
Keyword(s):  
Wave Height ◽  
Wave Spectrum ◽  
Ocean Waves ◽  
Ocean Basin ◽  
Distribution Model ◽  
Exceedance Probability ◽  
Random Wave ◽  
Theoretical Spectrum ◽  
Sea State ◽  
Wave Heights

The wave height parameter in ocean waves is one of the important information for a marine structure design. The present paper investigates the results of wave heights distribution from laboratory-generated for single sea state. Data of the random wave time series collected at the ocean basin are analyzed using the wave spectrum and compared with the theoretical spectrum in this study. The random wave data is varied with four sea states consisting of sea states 3, 4, 5 and 6 obtained from laboratory measurements. The parameter conditions of generated sea waves are represented by a value of significant wave height and wave peak period in the range of sea states. The individual wave heights data in each sea state are presented in the form of exceedance probability distribution and the predictions using a linear model. This study aims to estimate the wave heights distribution using the Rayleigh and Weibull distribution model. Furthermore, the accuracy of the wave heights distribution data's prediction results in each sea state has been compared and examined for both models. The applied linear models indicate similar and reasonable estimations on the observed data trends.

Detection of Rogue Waves From Space

2003 ◽  
Author(s):  
Susanne Lehner ◽  
Johannes Schulz-Stellenfleth ◽  
Andreas Niedermeier ◽  
Jose Carlos Nieto Borge
Keyword(s):  
Measurement Techniques ◽  
Wave Spectrum ◽  
Ocean Waves ◽  
Rogue Waves ◽  
Global Scale ◽  
Sar Images ◽  
Data Set ◽  
Directional Information ◽  
Wave Measurement ◽  
Ship Security

Space borne Synthetic aperture radar are able to provide high resolution measurements of ocean waves on a global scale. SAR is still the only instrument providing directional information on waves on a continuous basis in the open ocean. The present study uses a reprocessed data set of complex SAR images acquired by the European Remote Sensing satellite ERS-2 to estimate different wave parameters relevant for ship security. In addition, a new method is presented to derive two dimensional sea surface elevation fields from complex SAR data. The method permits to analyze wave fields in more detail than conventional SAR wave measurement techniques, which only estimate the wave spectrum. The technique provides parameters like maximum to significant wave height ratios, wave steepness, or the probability of wave breaking. Global maps and statistics of the new parameters are presented.

Development and Application of a Global Satellite Database of Wind and Wave Conditions

2015 ◽  
Author(s):  
I. R. Young ◽  
S. Zieger ◽  
A. V. Babanin
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
Extreme Values ◽  
Wave Spectrum ◽  
The United States ◽  
Wave Climate ◽  
Global Scale ◽  
Extreme Value Statistics ◽  
Data Set ◽  
Individual Calibration

Oceanographic satellites have now been in operation for almost 30 years, collecting global data on oceanic winds and waves. During this period, a variety of satellites have been operational. These include altimeters (wind speed and wave height), SSMI radiometers (wind speed), scatterometers (wind speed and direction) and Synthetic Aperture Radar, SAR (full directional wave spectrum). Data from these instruments potentially represents an invaluable resource for offshore engineering design and facilities operation. This paper describes the development of a unique database containing data from all these instruments over their full periods of operation. The paper will describe the calibration and cross-validation of all instruments. This analysis shows the limitations of individual instruments and the relative accuracies. Instruments are calibrated against a very comprehensive buoy data set from the United States, Canada, UK, France, Spain, Australia and New Zealand. The extensive buoy dataset means that it is possible to have individual calibration buoys and independent validation sites. Further validation is provided by examining cross-over points between different satellite instruments where they image the same region of ocean at the same time. The paper will also demonstrate the application of this database. These applications include the evaluation of seasonal wind and wave climate on a global scale, the determination of extreme value statistics (100 year return values) for wind speed and wave height, long term trends in wind speed and wave height and potential trends in extreme values.

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