Journal of Ocean University of China

, Volume 16, Issue 6, pp 998–1002 | Cite as

Parameter identification of JONSWAP spectrum acquired by airborne LIDAR



In this study, we developed the first linear Joint North Sea Wave Project (JONSWAP) spectrum (JS), which involves a transformation from the JS solution to the natural logarithmic scale. This transformation is convenient for defining the least squares function in terms of the scale and shape parameters. We identified these two wind-dependent parameters to better understand the wind effect on surface waves. Due to its efficiency and high-resolution, we employed the airborne Light Detection and Ranging (LIDAR) system for our measurements. Due to the lack of actual data, we simulated ocean waves in the MATLAB environment, which can be easily translated into industrial programming language. We utilized the Longuet-Higgin (LH) random-phase method to generate the time series of wave records and used the fast Fourier transform (FFT) technique to compute the power spectra density. After validating these procedures, we identified the JS parameters by minimizing the mean-square error of the target spectrum to that of the estimated spectrum obtained by FFT. We determined that the estimation error is relative to the amount of available wave record data. Finally, we found the inverse computation of wind factors (wind speed and wind fetch length) to be robust and sufficiently precise for wave forecasting.

Key words

JONSWAP spectrum parameter identification least square method airborne LIDAR 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This work is supported by the Scientific Instruments Development Program of NSFC (No. 615278010), and the National Key Basic Research Program of China (973 program) under grant No. 2014CB845301/2/3.


  1. Bermont, M. R., Horwood, R., Thurley, W. F., and Baker, J., 2006. Shallow angle wave profiling LIDAR. Journal of Atmospheric and Oceanic Technology, 24: 1150–1156.CrossRefGoogle Scholar
  2. Blenkinsopp, C. E., Turner, I. L., Allis, M. J., Peirson, W. L., and Garden, L. E., 2012. Application of LIDAR technology for measurement of time-varying free-surface profiles in a laboratory wave flume. Coastal Engineering Journal, 68: 1–5.CrossRefGoogle Scholar
  3. Cooley, J. W., and Tukey, J. W., 1965. An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation, 19: 297–301.CrossRefGoogle Scholar
  4. Dean, R. G., and Dalrymple, R. A., 1984. Water Wave Mechanics for Engineers and Scientists. Prentice-Hall Inc., Upper Saddle River, NJ, USA, 72pp.Google Scholar
  5. Hasselmann, K., 1973. Measurement of wind wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Deutsche Hydrographische Zeitschrift, A (8): 95.Google Scholar
  6. Hasselmann, K., Ross, D. B., Muller, P., and Sell, W., 1976. A parametric wave prediction model. Journal of Physical Oceanography, 6: 200–228.CrossRefGoogle Scholar
  7. Huang, W. M., Shen, C. X., Gill, E. W., and Horstmann, J., 2016. Surface current measurements using X Band marine radar with vertical polarization. IEEE Transactions on Geoscience and Remote Sensing, 54: 2988–2997.CrossRefGoogle Scholar
  8. Irish, J. L., and Lillycrop, A. J., 1999. Scanning laser mapping of the coastal zone: The SHOALS system. ISPRS Journal of photogrammetry and Remote Sensing, 54: 123–129.CrossRefGoogle Scholar
  9. Mitsuyasu, H., Tasai, F., Suhara, T., Mizuno, S., Ohkusu, M., Honda, T., and Rikiishi, K., 1979. Observation of the power spectrum of ocean waves using a cloverleaf buoy. Journal of Physical Oceanography, 10: 286–296.CrossRefGoogle Scholar
  10. Naderi, M., and Patzold, M., 2015. Design and analysis of a one-dimensional sea surface simulator using the sum-of-sinusoids principle. In: OCEANS 2015–MTS/IEEE Washington. Washington DC, 19–22.Google Scholar
  11. Nouguier, F., Grilli, S. T., and Gurin, C. A., 2014. Nonlinear Ocean wave reconstruction algorithms based on simulated spatiotemporal data acquired by a flash LIDAR camera. IEEE Transactions on Geoscience and Remote Sensing, 52: 1761–1771.CrossRefGoogle Scholar
  12. Synder, R. L., 1974. A field study of wave induced pressure fluctuation above surface gravity waves. Journal of Marine Research, 32: 491–531.Google Scholar
  13. Turner, I. L., Harley, M. D., and Drummond, C. D., 2016. UAVs for coastal surveying. Coastal Engineering, 114: 19–24.CrossRefGoogle Scholar
  14. Wang, C. K., and Phipot, W. D., 2006. Using airborne bathymetric LIDAR to detect bottom type variation in shallow waters. Remote Sensing of Environment, 106: 123–135.CrossRefGoogle Scholar

Copyright information

© Science Press, Ocean University of China and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Key Laboratory of Autonomous Systems and Networked Control, Ministry of EducationSouth China University of TechnologyGuangzhouP. R. China
  2. 2.LAGEP, Bâtiment CPEUniversité Claude Bernard Lyon 1VilleurbanneFrance

Personalised recommendations