Skip to main content

Advertisement

Log in

On the statistical characterization of sea surface conditions in the mid Atlantic

  • Original Article
  • Published:
Modeling Earth Systems and Environment Aims and scope Submit manuscript

Abstract

The statistical characterization of sea state over the mid Atlantic was examined by using a 37-year (1980–2016) daily wave height and wind speed data obtained from simulations. Analysis of the daily wave height and wind speed and characterization of the annual, seasonal, sub-seasonal and monthly mean sea state from these parameters were done. Results showed that the average sea surface conditions in the mid Atlantic alternates between the slight and moderate sea states. Clearly, the sea surface condition of the mid Atlantic is generally rougher in winter months than the rest of the months of the year. However, the occurrence of the slight sea state is large (50–90%) in the eastern mid Atlantic, while the moderate sea state showed a high occurrence (40–90%) over a large region of the ocean. Rough sea condition with frequencies between 15 and 30% prevailed in the northwestern region of the ocean while higher occurrences (0.8–8%) of very rough sea distribute in the upper northwestern mid Atlantic. Hardly do the calm, high, very high and phenomenal sea conditions occur in the mid Atlantic.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Bitner-Gregersen E (2015) Joint met-ocean description for design and operations of marine structures. Appl Ocean Res 51:279–292

    Article  Google Scholar 

  • Chalikov DV, Belevich MY (1993) One-dimensional theory of the wave boundary layer. Bound Layer Meteorol 63:65–96

    Article  Google Scholar 

  • Ewans KC (2015) A wavelet-based test for swell stationarity. Appl Ocean Res 51:255–267

    Article  Google Scholar 

  • Forristall GZ (1978) On the statistical distribution of wave heights in a storm. J Geophys Res Atmos 83(C5):2553–2558

    Article  Google Scholar 

  • Hanafin JA, Quilfen Y, Ardhuin F, Sienkiewicz J, Queffeulou P, Obrebski M, Chapron B, Reul N, Collard F, Corman D, De Azevedo BE, Vandemark D, Stutzmann E (2012) Phenomenal sea states and swell from a North Atlantic storm in February 2011. Am Meteorol Soc. https://doi.org/10.1175/BAMS-D-11-00128.2

    Article  Google Scholar 

  • Jahns HO, Wheeler JD (1973) Long-term wave probabilities based on hindcasting of severe storms. J Pet Technol 25(4):473–486

    Article  Google Scholar 

  • Lucas C, Guedes SC (2015a) On the spectral modelling of swell spectra. Ocean Eng 108:749–759

    Article  Google Scholar 

  • Lucas C, Guedes SC (2015b) Bivariate distributions of significant wave height and mean wave period of combined sea states. Ocean Eng 106:341–353

    Article  Google Scholar 

  • Lucas C, Muraleedharan G, Guedes SC (2014) Outliers identification in a wave hindcast dataset used for regional frequency analysis. In: Guedes SC, Santos TA (eds) Maritime technology and engineering. Taylor and Francis Group, London, pp 1317–1327

    Chapter  Google Scholar 

  • Nguyen TD, Sorensen AJ, Quek ST (2007) Design of hybrid controller for dynamic positioning from calm to extreme sea conditions. Automatica 43:768–785. https://doi.org/10.1016/j.automatica.2006.11.017 (Elsevier, ScienceDirect)

    Article  Google Scholar 

  • Osinowo AA, Lin X, Zhao D, Wang Z (2017) Statistical analyses of sea state conditions in South China Sea. J Ocean Univ China (Ocean Coast Sea Res) 16(3):357–369. https://doi.org/10.1007/s11802-017-3188-9. http://www.ouc.edu.cn/xbywb/E-mail:xbywb@ouc.edu.cn (ISSN 1672-5182)

    Article  Google Scholar 

  • Sandwell DT, Agreen RW (1984) Seasonal variation in wind speed and sea state from global satellite measurements. J Geophys Res 89(C2):2041–2051

    Article  Google Scholar 

  • Santoro A, Guedes SC, Arena F (2013) Analysis of experimental results on the space-time evolution of wave groups in crossing seas. In: Proceedings of the 32nd international conference on ocean, offshore and arctic engineering. Nantes, France, Paper OMAE2013-11533

  • Schneggenburger C, Gunther H, Rosentha W (2000) Spectral wave modeling with non-linear dissipation: validation and applications in a coastal tidal environment. Coast Eng 41(1–3):201–235

    Article  Google Scholar 

  • Schule JJ (1966) Sea state. In: Fairbridge RW (ed) The encyclopedia of oceanography (Encyclopedia of Earth Sciences Series, Vol. 1). Van Nostrand Reinhold Company, New York, pp 786–792

    Google Scholar 

  • Silva D, Rusu E, Guedes SC (2013) Evaluation of various technologies for wave energy conversion in the Portuguese nearshore. Energies 6:1344–1364

    Article  Google Scholar 

  • Soares CS, Guedes SC (2007) Comparison of bivariate models of distributions of significant wave height and wave period. In: Proceedings of the 26th international conference on offshore mechanics and arctic engineering (OMAE 2007). ASME, NY, USA, paper OMAE 2007-29740

  • Sundar V, Ananth PN (1988) Wind climate for Madras harbor, India. J Wind Eng Ind Aerodyn 31(2):323–333

    Article  Google Scholar 

  • Thompson EF (1980) Energy Spectra in Shallow U.S. Coastal Waters. Tech. Paper 80-2, Army US, Corps of Engineers, Coastal Engineering Research Centre

  • Titov LF (1969) Wind-driven waves. Israel Program for Scientific Translations, Jerusalem

    Google Scholar 

  • Tolman HL (1999) User manual and system documentation of WAVEWATCH-III Version 1.18. Washington. pp 1–56

  • Tolman HL (2009) User manual and system documentation of WAVEWATCH-III Version 3.14. NOAA/NWS/NCEP/MMAB Technical Note, Washington, pp 1–194

    Google Scholar 

  • Tolman HL, Chalikov DV (1996) Source terms in a third generation wind–wave model. J Phys Oceanogr 26(2):497–492 518

    Google Scholar 

  • Wilks DS (1995) Statistical methods in the atmospheric sciences, 2 edn. International geophysics series, vol 59. Academic Press, United States, p 464 (ISBN‐10: 0127519653)

    Google Scholar 

  • Yang JS, Chen XY, Wang J, Zhang R, Huang WG (2008) Data fusion of significant wave height from multiple satellite altimeters. In: Proceedings of SPIE, v7154

  • Zhou L, Li Z, Mou L, Wang A (2014) Numerical simulation of wave field in the South China Sea using WAVEWATCH III. Chin J Oceanol Limnol 32(3):656–664. https://doi.org/10.1007/s00343-014-3155-x

    Article  Google Scholar 

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Adekunle Ayodotun Osinowo.

Ethics declarations

Conflicts of interest

We have no conflicts of interest to disclose.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Osinowo, A.A., Okogbue, E.C., Adefisan, E.A. et al. On the statistical characterization of sea surface conditions in the mid Atlantic. Model. Earth Syst. Environ. 4, 1487–1507 (2018). https://doi.org/10.1007/s40808-018-0494-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40808-018-0494-1

Keywords

Navigation