Spatio-temporal modelling of wind speed variations and extremes in the Caribbean and the Gulf of Mexico

The wind speed variability in the North Atlantic has been successfully modelled using a spatio-temporal transformed Gaussian field. However, this type of model does not correctly describe the extreme wind speeds attributed to tropical storms and hurricanes. In this study, the transformed Gaussian model is further developed to include the occurrence of severe storms. In this new model, random components are added to the transformed Gaussian field to model rare events with extreme wind speeds. The resulting random field is locally stationary and homogeneous. The localized dependence structure is described by time- and space-dependent parameters. The parameters have a natural physical interpretation. To exemplify its application, the model is fitted to the ECMWF ERA-Interim reanalysis data set. The model is applied to compute long-term wind speed distributions and return values, e.g., 100- or 1000-year extreme wind speeds, and to simulate random wind speed time series at a fixed location or spatio-temporal wind fields around that location.

[1]  Igor Rychlik,et al.  Slepian Models and Regression Approximations in Crossing and Extreme Value Theory , 1991 .

[2]  Brian J. Reich,et al.  A MULTIVARIATE SEMIPARAMETRIC BAYESIAN SPATIAL MODELING FRAMEWORK FOR HURRICANE SURFACE WIND FIELDS , 2007, 0709.0427.

[3]  Richard L. Smith,et al.  Models for exceedances over high thresholds , 1990 .

[4]  I. Rychlik,et al.  Velocities for moving random surfaces , 2003 .

[5]  Knut O. Ronold,et al.  New DNV Recommended Practice DNV-RP-C205 On Environmental Conditions And Environmental Loads , 2006 .

[6]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1967 .

[7]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[8]  Stuart G. Coles,et al.  Regional Modelling of Extreme Storms Via Max‐Stable Processes , 1993 .

[9]  Igor Rychlik,et al.  Estimation of return values for significant wave height from satellite data , 2011 .

[10]  Igor Rychlik,et al.  WAFO - A Matlab Toolbox For Analysis of Random Waves And Loads , 2000 .

[11]  I. Rychlik,et al.  Slepian models for moving averages driven by a non-Gaussian noise , 2014 .

[12]  Dagmar Nelissen,et al.  Study on the analysis of market potentials and market barriers for wind propulsion technologies for ships , 2016 .

[13]  Igor Rychlik,et al.  Laplace moving average model for multi-axial responses in fatigue analysis of a cultivator , 2013 .

[14]  I. Rychlik,et al.  Estimation of Weibull distribution for wind speeds along ship routes , 2017 .

[15]  Eric P. Smith,et al.  An Introduction to Statistical Modeling of Extreme Values , 2002, Technometrics.

[16]  Georg Lindgren,et al.  Stationary Stochastic Processes: Theory and Applications , 2012 .

[17]  Matthew A. Lackner,et al.  Probability distributions for offshore wind speeds , 2009 .

[18]  J. Elsner,et al.  Climatology Models for Extreme Hurricane Winds near the United States , 2006 .

[19]  D. Walshaw Modelling extreme wind speeds in regions prone to hurricanes , 2000 .

[21]  Igor Rychlik,et al.  Models for road surface roughness , 2012 .

[22]  M. Longuet-Higgins The statistical analysis of a random, moving surface , 1957, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[23]  J. Azaïs,et al.  Level Sets and Extrema of Random Processes and Fields , 2009 .

[24]  M. R. Leadbetter,et al.  Modelling and Statistical Analysis of ocean-wave data using transformed gaussian processes , 1997 .

[25]  L. Bondesson On simulation from infinitely divisible distributions , 1982, Advances in Applied Probability.

[26]  Ship Energy Efficiency Measures Status and Guidance , 2013 .

[27]  Convolution-invariant subclasses of generalized hyperbolic distributions , 2016 .

[28]  A. M. Walker Statistical Analysis of a Random, Moving Surface , 1957, Nature.

[29]  Helmut Küchenhoff,et al.  Modelling extreme wind speeds at a German weather station as basic input for a subsequent risk analysis for high-speed trains , 2004 .

[30]  I. Rychlik,et al.  Slepian noise approach for gaussian and Laplace moving average processes , 2015 .

[31]  S. Cambanis,et al.  Chaotic behavior of infinitely divisible processes , 1995 .

[32]  Feller William,et al.  An Introduction To Probability Theory And Its Applications , 1950 .

[33]  S. Larsen,et al.  Elements of extreme wind modeling for hurricanes , 2016 .

[34]  Steven R. Winterstein,et al.  SPRINGING AND SLOW-DRIFT RESPONSES: PREDICTED EXTREMES AND FATIGUE VS. SIMULATION , 1994 .

[36]  Samuel Kotz,et al.  The Laplace Distribution and Generalizations: A Revisit with Applications to Communications, Economics, Engineering, and Finance , 2001 .

[37]  S. Rice Mathematical analysis of random noise , 1944 .

[38]  A. H. Murphy,et al.  Time Series Models to Simulate and Forecast Wind Speed and Wind Power , 1984 .

[39]  Krzysztof Podgórski,et al.  A class of non-Gaussian second order random fields , 2011 .

[40]  Wengang Mao,et al.  Probabilistic Model for Wind Speed Variability Encountered by a Vessel , 2014 .