Freak waves: clues for prediction in ship accidents

Volume 2: Structures, Safety and Reliability, 2011

This paper concerns the description of the formation of a very large, quality-controlled dataset of raw wave measurements. As part of the CresT (Cooperative Research on Extreme Seas and their impacT) Joint Industry Project (JIP), the participants provided raw field measurements of water surface elevation from various installations across the globe. This paper will describe the data collection from the different installations, the strict quality control procedure employed to ensure a reliable dataset, and an overview of the occurrences of records containing freak wave events. Part 2 of this paper will then go on to describe the analysis performed on this dataset as well as the findings from the study.

Applied Ocean Research, 2005

A ship that founders presents a great disaster both from an economical and a human point of view. It is therefore of concern to meteorological centers to include sea state related parameters in marine weather forecasts when they exceed a certain threshold. At present a standardized warning system is not set up yet. To contribute towards the definition of adequate warning criteria, an investigation was undertaken of ship accidents reported as being due to bad weather. Sea state related parameters (i.e. meteorological centers' standard wave products) at the time of 270 accidents were analyzed and compared to known ship characteristics. In order to estimate a certain degree of severity, results were compared to wave climate variation. In particular the use of quantiles seemed to provide a reasonable description of dangerous seas.

Marine Structures, 2010

In a previous paper ((7]), we discussed the natural variabil-ity of some spectral parameters that may c haracterize a sea state, and concluded that deviations that might b e o b s e r v ed close to occurrences of extreme waves were well within the natural range of variability. A s a consequence, prediction of increased risk of rogue waves occurrence can-not be made from a simple local examination of the spectrum, but would require investigation of either simultaneous values of several parameters, or of the time history of a parameter over durations of the same order of magnitude as a storm. In the present study, w e i n vestigate that second possibility, i.e. since the excursions on the time-scale of a sea-state are not decisive, we consider the next time-scale, that of a whole storm. To this aim, the Frigg database is searched for storms, and an attempt is made to select within the set of storms a subset of \freaky storms" where there are observations of high crests with respe...

Transportation Research Part D: Transport and Environment, 2015

Oceanographic conditions like wind strength and wave height affect the risk of shipping incidents. Seasonal variations and trends in these effects are studied for weather-related incidents and pollution incidents, for six major ship types and for six regions across the globe. The employed database of more than five million observations combines information from various sources on oceanographic conditions, ship particulars, and incidents. The magnitude of wind and wave effects is found to vary by ship type, region, season, and period, and most of these effects are larger during autumn and winter season and in recent periods.

Coastal …, 2002

The purpose of this study is to describe the characteristics of occurrence of coastal freak waves and to investigate their statistical and spectral structures. According to Ochi's definition of freak wave, 175 coastal freak waves were obtained from a long-term full scale wave data bank, in which there are more than 4500 wave records. The probability distribution of occurrence of coastal freak waves was investigated. The goodness-of-fit testing shows that the Rayleigh distribution is more appropriate for describing the occurrence probability of coastal freak waves when the ratio of its wave height to the corresponding significant wave height is more than 2.4. However, the occurrence probability of coastal freak waves remains below the Rayleigh distribution. The relationship between coastal freak waves and the related sea states shows that the occurrence possibility of coastal freak waves may significantly increase in the sea of grouping waves or in the wave field of bimodal spectra. Wavelet Transform was applied to the in situ wave records to investigate the energy distribution on the time-frequency domain. It justified the previous conclusions and demonstrated that the wave groups and superposition of the swell and wind wave energy, which occupies 70% of the coastal freak samples, play major roles of inducing the coastal freak waves.

Ocean Engineering, 2018

The potential for coastal freak waves (CFWs) represents a threat to people living in coastal areas. CFWs are generated via the evolution of a wave and its interactions with coastal structures or rocks; however, the exact mechanism of their formation is not clear. Here, a data-driven warning model based on an artificial neural network (ANN) is proposed to predict the possibility of CFW occurrence. Seven parameters (significant wave height, peak period, wind speed, wave groupiness factor, Benjamin Feir Index (BFI), kurtosis, and wind-wave direction misalignment) collected prior to the occurrence of the CFW are used to develop the model. The buoy data associated with 40 known CFW events are used for model training, and the data associated with 23 such events are used for validation. The use of data obtained during the 6-h period prior to CFW occurrence combined with the same amount of non-CFW data is shown to produce the best model. Two validations using mediapublished and camera-recorded CFW events show that the accuracy rate (ACR) exceeds 90% and the recall rate (RCR) exceeds 87%, demonstrating the accuracy of the proposed model. This warning model has been implemented in operational runs since 2016.

Journal of Marine Science and Engineering

The method for the prediction of extreme vertical wave bending moments on a passenger ship based on the hindcast database along the shipping route is presented. Operability analysis is performed to identify sea states when the ship is not able to normally operate and which are likely to be avoided. Closed-form expressions are used for the calculation of transfer functions of ship motions and loads. Multiple operability criteria are used and compared to the corresponding limiting values. The most probable extreme wave bending moments for the short-term sea states at discrete locations along the shipping route are calculated, and annual maximum extreme values are determined. Gumbel probability distribution is then fitted to the annual extreme values, and wave bending moments corresponding to a return period of 20 years are determined for discrete locations. The system reliability approach is used to calculate combined extreme vertical wave bending moment along the shipping route. The ...

This article investigates whether changes in oceanographic conditions can be filtered out to measure their effect of the overall safety level of ships. The article is based on a unique dataset of 3.2 million observations from 20,729 individual vessels for the time period 1979 to 2007 in the North Atlantic and Arctic region. It combines ship particular information, ship safety inspections, casualties, ship economic cycles and oceanographic data. Standard econometric models are used to measure whether the effect of significant wave height and wind strength towards the probability of casualty can be measured and tests whether it changed over the time period on hand since changes in oceanographic conditions have been confirmed in the literature for the North Atlantic. The results show that the effect of wind strength and significant wave height can be measured towards the probability of casualty although there is no clear seasonal pattern while overall; the probability of casualty is in...

Journal of Hydraulic Research, 2008

A statistically-based model is applied to forecast sea states for severe storms. The model is based on the application of a neural network and predicts extreme sea state parameters at specified locations. The results show that the neural network can be applied to forecast extreme sea state parameters. This requires a special treatment of the input data. The analysis shows that different types of input data and training data sets should be considered and the representativity of the training data set must be improved. Moreover, a sensitivity analysis should be conducted to remove excess information from the input data. The processing of data sets significantly reduces the number of parameters applied in the model and improves the prediction for most severe storms. The analysis indicates that this neural network model may be helpful in the selection of a measurement system for the forecasting of extreme sea state parameters. This is important because typical installations of wave buoys along the coast have a limited forecasting applicability range.