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Reliable environmental solutions deployed offshore
RPS took traditional LiDAR technology and integrated it into a buoy with power, data storage and satellite communication capabilities. Applying our deep expertise of offshore measurement, our design and development process was focussed on creating a reliable design to maximise data accuracy and return, while making the process of information gathering easier and more cost-effective for our clients. You can read the full story here. And for more information please make contact with one of our regional representatives below.
Equinor and RPS successfully launch new LiDAR buoys in South Korea
Equinor is planning to develop the floating offshore wind project Firefly. Over the next year, a feasibility study into the viability of wind resource will be conducted. RPS was engaged to deliver two Floating LiDAR buoys to collect the data that will determine future investment decisions. They were recently deployed into the East Sea from Ulsan, marking a significant project milestone. You can read the full story here. And for more information please make contact with one of our regional representatives below.
because it is the approximate distribution of the minimum of many variables, which could be seen as the weakest link among many links that can be broken in a structure. Popularized by reliability engineers, its use has spread to other areas, in particular to ocean engineering. It is given by ( ) 1 exp c uy Fy a (7) Although not generally advisable, there are certain situations in which it makes sense to fit a Weibull distribution of minima to the peak excesses in place of the GPD model. Indeed, suppose that the data really follow a Type I tail, or at least that this has been convincingly demonstrated on the basis of some statistical analyses (which is often the case in deep waters; see Caires and Sterl, 2005), then the asymptotic distribution of the excesses is exponential. Since the exponential is a special case of the Weibull distribution of minima, one might think that there would be no harm in fitting a Weibull distribution to the data rather than an exponential distribution. Now, if the data are truly exponential, this would actually entail more uncertainty in parameter estimates, which would be undesirable (intuitively, to know that the data are exactly exponential amounts to more information than knowing that they are Weibull). However, it may happen that, because the exponential is only valid asymptotically, the Weibull distribution will provide a better approximation to the data (since it has one more parameter and hence more flexibility), and in that case fitting the latter would provide better results than fitting the former. In any case, if one is to step outside the GPD domain one should do so on the basis of some justification. Still in this connection, we should add that, because wave data most often exhibits a type I tail or a slightly lighter type III tail, studies comparing estimates from the GPD with estimates from the Weibull are often inconclusive as to what is the most appropriate tail/distribution (e.g. Van Vledder et al., 1993) since there are no statistically significant differences between the two models. However, a good reason for always considering the GPD for POT data and the GEV for AM data is that they have a substantial and solid theoretical basis stemming from asymptotic considerations. Bivariate analysis We recommend that the method of Zachary et al. (1998) be used to compute estimates of joint densities, conditional densities, and joint return value plot. The method consists of carrying out the univariate extreme value analysis of the data using the POT/GPD approach, standardizing the marginal tails, and modelling the dependence structure of the variables by means of non-parametric (kernel) density estimates. For an example see Caires and Van Gent (2008). ACKNOWLEDGEMENTS The authors are thankful to Robin Morelissen and Carline Bos for their substantial contributions to the development of ORCA. REFERENCES Anderson, C W, Carter, DJT, and Cotton, PD (2001). Wave climate variability and impact on offshore design extreme. Shell International and the Organization of Oil and Gas Producers Rep., 99 p. Battjes, JA and Groenendijk, HW (2000). “Wave height distributions on shallow foreshores,” Coast. Eng., Vol 40, pp 161-182. Caires. S (2007). Extreme wave statistics. Confidence intervals. WL | Delft Hydraulics Report H4803.30. Caires, S (2009). A comparative simulation study of the annual maxima and the peaks-over-threshold methods. 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A Course in Large Sample Theory. Chapman and Hall, New York. Ferreira, JA, and Guedes Soares, C (1998). “An application of the peaks over threshold method to predict extremes of significant wave height,” J. Offshore Mech. Arct. Eng., Vol 120, pp 165–176. Ferreira, JA, and Guedes Soares, C (2000). “Modelling distributions of significant wave height,” Coast. Eng., Vol 40, pp 361–374. Goda, Y (1978). The observed joint distribution of periods and heights of sea waves. Proc. 16th Int. Conf. on Coastal Engineering, Hamburg. ASCE, New York, pp 227-246 Guedes Soares, C and Henriques, AC (1996). “Statistical uncertainty in long-term predictions of significant wave height,” J. Offshore Mech. Arct. Eng., Vol 11, pp 284–291. Holthuijsen, LH (2007). Waves in oceanic and coastal waters. Cambridge University Press. Hosking, JRM. and Wallis, JR (1987). Parameter and quantile estimation for the Generalized Pareto Distribution, Technometrics, Vol 29, pp 339–349. 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Tajvidi, N (2003). “Confidence Intervals and Accuracy Estimation for heavy-tailed Generalized Pareto Distributions,” Extremes, Vol 6, pp 111-123. Van Vledder, G, Goda, Y, Hawkes,P , Mansard, E, Martin, MJ, Mathiesen, M, Peltier, E, and Thompson, E (1993). “Case studies of extreme wave analysis: a comparative analysis,” Proc. WAVES'93 Conf., New Orleans, USA, pp 978-992. Zachary, S, Feld, G Ward, G and Wolfram, J (1998). “Multivariate extrapolation in the offshore environment,” Applied Ocean Research, Vol 20, pp. 273–295. 297 |
