Abstract
Mixed extreme value models (Mínguez et al., Stoch Environ Res Risk Assess 27:757–768, 2013b) have proved to be an appropriate tool for dealing with wave maxima because they take full advantage of upper tail information from both (1) hindcast or wave reanalysis and (2) instrumental records, which reduces the uncertainty on return level estimates. However, in order to characterize stochastically the differences between instrumental and reanalysis maxima, the method developed in Mínguez et al. (Stoch Environ Res Risk Assess 27:757–768, 2013b) only uses information about annual maxima. This technical note revisits the MEV method so that those differences between instrumental and reanalysis maxima could be characterized using information on independent storm peaks, instead of annual extremes. This strategy increases the size of data sets during the estimation process, reducing uncertainty. The revisited mixed extreme value model is illustrated using data from the same location studied in Mínguez et al. (Stoch Environ Res Risk Assess 27:757–768, 2013b), and results are compared.
This is a preview of subscription content, log in via an institution to check access.
References
Beran MA, Nozdryn-Plotnicki MK (1977) Estimation of low return period floods. Bull Int Ass Hydrol Sci 2:275–282
Brockwell PJ, Davis RA (1991) Time series: theory and methods, 2nd edn. Springer, New York, NY
Caires S, Sterl A (2005) A new non-parametric method to correct model data: application to significant wave height from the ERA-40 reanalysis. J Atmos Ocean Technol 22:443–459
Camus P, Méndez FJ, Medina R (2011) A hybrid efficient method to downscale wave climate to coastal areas. Coast Eng. doi:10.1016/j.coastaleng.2011.05.007
Castillo E (1988) Extreme value theory in engineering. Academic Press, New York
Castillo E, Hadi AS, Balakrishnan N, Sarabia JM (2005) Extreme value and related models in engineering and science applications. Wiley, New York
Cavaleri L, Sclavo M (2006) The calibration of wind and wave model data in the mediterranean sea. Coast Eng 53:613–627
Coles S (2001) An introduction to statistical modeling of extreme values. Springer Series in Statistics
Davidson AC, Smith RL (1990) Models for exceedances over high thresholds. J R Stat Soc Ser B (Methodol) 52(3):393–442
Forsythe GE, Malcolm MA, Moler CB (1976) Computer methods for mathematical computations. Prentice-Hall, Englewood Cliffs, NJ
Katz RW, Parlange MB, Naveau P (2002) Statistics of extremes in hydrology. Adv Water Res 25:1287–1304
Leadbetter M, Lindgren G, Rootzén H (1983) Extremes and related properties of random sequences and processes. Springer, New York
Massey FJ (1951) The Kolmogorov–Smirnov test for goodness of fit. J Am Stat Assoc 46(253):68–78
Mínguez R, Espejo A, Tomás A, Méndez FJ, Losada IJ (2011) Directional calibration of wave reanalysis databases using instrumental data. J Atmos Ocean Technol 28:1466–1485. doi:10.1175/JTECH-D-11-00008.1
Mínguez R, Reguero BG, Luceño A, Méndez FJ (2012) Regression models for outlier identification (hurricanes and typhoons) in wave hindcast databases. J Atmos Ocean Technol 29:267–285. doi:10.1175/JTECH-D-11-00059.1
Mínguez R, Guanche Y, Méndez FJ (2013a) Point-in-time and extreme-value probability simulation technique for engineering design. Struct Saf 41:29–36. doi:10.1016/j.strusafe.2012.10.002
Mínguez R, Tomás A, Méndez FJ, Medina R (2013b) Mixed extreme wave climate model for reanalysis databases. Stoch Environ Res Risk Assess 27:757–768. doi:10.1007/s00477-012-0604-y
Reguero BJ, Menéndez M, Méndez FJ, Mínguez R, Losada IJ (2012) A global ocean wave (GOW) calibrated reanalysis from 1948 onwards. Coast Eng 65:38–55. doi:10.1016/j.coastaleng.2012.03.003
Shampine LF (2008) Vectorized adaptive quadrature in MATLAB. J Comput Appl Math 211:131–140
Vanem E, Huseby A, Natvig B (2012a) A Bayesian hierarchical spatio-temporal model for significant wave height in the North Atlantic. Stoch Environ Res Risk Assess 26:609–632. doi:10.1007/s00477-011-0522-4
Vanem E, Huseby A, Natvig B (2012b) Modelling ocean wave climate with a Bayesian hierarchical space-time model and a log-transform of the data. Ocean Dyn 62:355–375. doi:10.1007/s10236-011-0505-5
Acknowledgments
The authors acknowledge to Puertos del Estado the availability REDCOS coastal buoy network and the Environmental Hydraulics Institute “IH Cantabria” for the reanalysis data used for this study. R. Mínguez was partly funded by the unemployment benefit of the Public Service of National Employment (SEPE) from the Spanish Ministry of Employment and Social Security. F. Del Jesus is funded by Fundación Iberdrola.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mínguez, R., Jesus, F.D. Revisited mixed extreme wave climate model for reanalysis data bases. Stoch Environ Res Risk Assess 29, 1851–1856 (2015). https://doi.org/10.1007/s00477-014-0937-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00477-014-0937-9