Estimation of extreme wave height return periods from short-term interpolation of multi-mission satellite data: application to the South Atlantic (original) (raw)
Related papers
We analyzed the spatial pattern of wave extremes in the South Atlantic Ocean by using multiple altimeter platforms spanning the period 1993-2015. Unlike the traditional approach adopted by previous studies, consisting in computing the monthly mean, median or maximum values inside a bin of certain size, we tackled the problem with a different procedure in order to capture more information from short term events. All satellite tracks occurring during two-day temporal window were added in the whole area and then gridded data was generated onto a mesh size of 2 • × 2 • through optimal interpolation. The 5 Peaks Over Threshold (POT) method was applied, along with the generalized Pareto distribution (GPD). The results showed a spatial distribution comparable to previous studies and, on the other hand, this method allowed capturing more information on shorter time scales without compromising spatial coverage. A comparison with buoy observations demonstrated that this approach improves the representativeness of short-term events in an extreme events analysis.
The Use of Satellite Altimeter Data to Estimate the Extreme Wave Climate
Journal of Atmospheric and Oceanic Technology, 1997
Since 1986, nine years of wave data derived from satellites have been accumulated, and this database will expand dramatically in the next two years as two more satellites are added. Several researchers have begun using this data to estimate extreme value statistics for waves. However, one potential problem with satellite data is space-time resolution, which is a poor match for the scales of storms. Satellites only revisit a site once every 10-35 days, and their tracks are separated by 100-200 km. With this coarse sampling, the satellite may miss storms since they have characteristic length and time scales as short as a few hours and tens of kilometers. The purpose of this paper is to explore the impact of this undersampling on the calculated 100-yr wave height. This is accomplished by running Monte Carlo simulations of simplified but realistic storms sampled by a simulated satellite and site. The authors study the sensitivity of the calculated 100-yr wave to variations in storm type, radius, and forward speed; number of satellites; satellite track; and satellite sampling region. The uncertainty, as measured by the coefficient of variation (cov), of the 100-yr wave based on 10 years of satellite data is 10% in regions like the North Sea that are dominated by extratropical storms, provided the satellite data is sampled over a 200-300-km region. This is about the level accepted by present offshore standards like the American Petroleum Institute. For regions dominated by tropical storms like the Gulf of Mexico, the cov for satellite-or site-derived extremes is much greater than 10% using 10 years of data. The situation improves with increased sample period, storm frequency, or the number of satellites. However, even in these cases some caution must still be exercised near the coast where the satellite data itself may be less reliable and sampling over large regions may remove real spatial gradients. Our conclusions apply to all existing satellite tracks including Geosat, Topex/Poseidon, and ERS.
On estimating extreme wave heights using combined Geosat, Topex/Poseidon and ERS-1 altimeter data
Applied Ocean Research, 2003
The estimation of extreme significant wave heights H s using altimeter observations is investigated. Data from the following three satellite missions are used: Geosat, ERS-1 and Topex/Poseidon. Practical methods of estimating extreme H s are described and limitations of their application to altimeter data are highlighted. Extreme H s are estimated using the three-parameter Weibull distribution, with maxima selected via the peaks over threshold method, and the Fisher-Tippet type I distribution, using data selected via the initial distribution method. Altimeter estimates are compared to extreme H s calculated from deep water buoy data. A comparative analysis of global estimates of satellite-derived extreme H s based on standard statistics investigates time-space undersampling and how it affects the reliability of long-term extreme wave estimates made using satellite altimeter data.
Accessing extremes of mid-latitudinal wave activity: methodology and application
Tellus A, 2009
A statistical methodology is proposed and tested for the analysis of extreme values of atmospheric wave activity at mid-latitudes. The adopted methods are the classical block-maximum and peak over threshold, respectively based on the generalized extreme value (GEV) distribution and the generalized Pareto distribution (GPD). Time-series of the 'Wave Activity Index' (WAI) and the 'Baroclinic Activity Index' (BAI) are computed from simulations of the General Circulation Model ECHAM4.6, which is run under perpetual January conditions. Both the GEV and the GPD analyses indicate that the extremes of WAI and BAI are Weibull distributed, this corresponds to distributions with an upper bound. However, a remarkably large variability is found in the tails of such distributions; distinct simulations carried out under the same experimental setup provide sensibly different estimates of the 200-yr WAI return level. The consequences of this phenomenon in applications of the methodology to climate change studies are discussed. The atmospheric configurations characteristic of the maxima and minima of WAI and BAI are also examined.
Journal of Geophysical Research, 2006
1] Recent evidence suggests long-term changes in the intensity and frequency of extreme wave climate around the globe. These changes may be attributable to global warming as well as to the natural climate variability. A statistical model to estimate long-term trends in the frequency and intensity of severe storm waves is presented in this paper. The model is based on a time-dependent version of the Peak Over Threshold model and is applied to the Washington NOAA buoy (46005) significant wave height data set. The model allows consideration of the annual cycle, trends, and relationship to atmosphereocean-related indices. For the particular data set analyzed the inclusion of seasonal variability substantially improves the correlation between the model and the data. Also, significant correlations with the Pacific-North America pattern, as well as long-term trend, are detected. Results show that the model is appropriate for a rigorous analysis of long-term trends and variability of extreme waves and for providing time-dependent quantiles and confidence intervals. Citation: Méndez, F. J., M. Menéndez, A. Luceño, and I. J. Losada (2006), Estimation of the long-term variability of extreme significant wave height using a time-dependent Peak Over Threshold (POT) model,
A new method for modelling the space variability of significant wave height
Extremes, 2005
Significant wave height, H s , is a measure of the variability of the ocean surface and is defined to be four times the standard deviation of the height of the ocean surface. In this paper, we present a methodology for modelling estimates of H s over space and time, using data obtained from satellite measurements. These estimates can be thought of as a random surface in space which develops over time. For each fixed time and over some limited region in space, the field consisting of the H s estimates may be considered stationary. Furthermore, it is reasonable to assume that the (natural) logarithms of the H s estimates are normally distributed. Under these assumptions and for each fixed time, the marginal distribution over space of the random field of the logarithms of the H s estimates is fitted by estimating its mean and covariance function, where the form of the covariance function is chosen to allow for correlation patterns at different spatial scales in the data. Both the mean and the covariance function of this model are allowed to be time dependent. A new methodology is developed for estimating the parameters of the chosen covariance structure. The proposed model is validated along the TOPEX-Poseidon satellite tracks by computing distributions of different quantities for the fitted model and comparing these to empirical estimates. Finally, the fitted model is used to compute the distribution of the global maximum over a certain region in the North Atlantic and to reconstruct the H s field.
Spatial Assessment of Extreme Significant Waves Heights in the Gulf of Lions
Coastal Engineering Proceedings, 2014
In the analysis of coastal hazards, the features of extreme waves are determining information to question the impact of storms to the coast. The spatial behaviour of extreme waves is even more valuable especially since it is sparsely provided. Regarding recent applications in other contexts, a kind of statistical models called max-stable processes is relevant for modelling spatial extreme events. Max-stable processes are extensions of the well-known Generalised Extreme Value (GEV) distribution. Unlike univariate approaches, max-stable processes consider spatial dependence of a phenomenon. Such a modelling also overtakes a standard multivariate approach by providing information continuously over the area studied, even where no observation is available. Relying on such a stochastic modelling, the aim of this study is to discuss the extreme waves hazards in the Gulf of Lions, focusing on their spatial behaviour.
Wave Extremes in the Northeast Atlantic
Journal of Climate, 2012
A method for estimating return values from ensembles of forecasts at advanced lead times is presented. Return values of significant wave height in the North-East Atlantic, the Norwegian Sea and the North Sea are computed from archived +240-h forecasts of the ECMWF ensemble prediction system (EPS) from 1999 to 2009. We make three assumptions: First, each forecast is representative of a six-hour interval and collectively the data set is then comparable to a time period of 226 years. Second, the model climate matches the observed distribution, which we confirm by comparing with buoy data. Third, the ensemble members are sufficiently uncorrelated to be considered independent realizations of the model climate. We find anomaly correlations of 0.20, but peak events (> P 97 ) are entirely uncorrelated. By comparing return values from individual members with return values of subsamples of the data set we also find that the estimates follow the same distribution and appear unaffected by correlations in the ensemble. The annual mean and variance over the 11-year archived period exhibit no significant departures from stationarity compared with a recent reforecast, i.e., there is no spurious trend due to model upgrades.
Practical methods of extreme value estimation based on measured time series for ocean systems
Ocean Engineering, 1992
Three practical methods for computing the expected maxima of Gaussian time series for ocean system analysis are developed. These methods utilize Pierce's sample scaling concept to overcome the maxima counting and correlation difficulties, but minimize the associated complexity and uncertainties. The first (Direct) method removes the dependence on the envelope for maxima estimation of the time series by directly operating on the time series itself. The second (Poisson clumping) employs the notion of sample scaling factor, but requires neither computing the envelope nor segmenting. The third (Log-fit) is a simple logarithm curve fitting, using the slowly varying, logarithmic growth property of the expected maximum. The accuracy and computational efficiency of these methods are examined. The Direct method and the Poisson clumping method are found to have comparable accuracy. Employment of the envelope does not improve the accuracy of the estimate in practice. Hence, the Direct method and the Poisson clumping method should be preferred. The Poisson clumping method is more efficient than both the Direct method and Pierce's method because of its straightforwardness in implementation. The Log-fit method is the simplest to implement, and computationally the most efficient. Its accuracy is acceptable for many engineering preliminary designs.