Journal cover Journal topic
Ocean Science An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 2.539 IF 2.539
  • IF 5-year value: 3.129 IF 5-year
    3.129
  • CiteScore value: 2.78 CiteScore
    2.78
  • SNIP value: 1.217 SNIP 1.217
  • IPP value: 2.62 IPP 2.62
  • SJR value: 1.370 SJR 1.370
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 48 Scimago H
    index 48
  • h5-index value: 32 h5-index 32
OS | Articles | Volume 15, issue 2
Ocean Sci., 15, 349–360, 2019
https://doi.org/10.5194/os-15-349-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Ocean Sci., 15, 349–360, 2019
https://doi.org/10.5194/os-15-349-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 05 Apr 2019

Research article | 05 Apr 2019

Hybrid improved empirical mode decomposition and BP neural network model for the prediction of sea surface temperature

Zhiyuan Wu et al.
Related authors  
The long-term spatio-temporal variability of sea surface temperature in the Northwest Pacific and the Near China Sea
Zhiyuan Wu, Changbo Jiang, Mack Conde, Jie Chen, and Bin Deng
Ocean Sci. Discuss., https://doi.org/10.5194/os-2019-69,https://doi.org/10.5194/os-2019-69, 2019
Revised manuscript has not been submitted
Short summary
Cited articles  
Amezquita-Sanchez, J. P. and Adeli, H.: A new music-empirical wavelet transform methodology for time–frequency analysis of noisy nonlinear and non-stationary signals, Digit. Signal Process., 45, 55–68, https://doi.org/10.1016/j.dsp.2015.06.013, 2015. 
Banzon, V., Smith, T. M., Chin, T. M., Liu, C., and Hankins, W.: A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies, Earth Syst. Sci. Data, 8, 165–176, https://doi.org/10.5194/essd-8-165-2016, 2016. 
Bond, N. A., Cronin, M. F., Freeland, H., and Mantua, N.: Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophys. Res. Lett., 42, 3414–3420, https://doi.org/10.1002/2015GL063306, 2015. 
Buckley, M. W., Ponte, R. M., Forget, G., and Heimbach, P.: Low-frequency SST and upper-ocean heat content variability in the North Atlantic, J. Climate, 27, 4996–5018, https://doi.org/10.1175/JCLI-D-13-00316.1, 2014. 
Chen, C., Cane, M. A., Henderson, N., Lee, D. E., Chapman, D., Kondrashov, D., and Chekroun, M. D.: Diversity, nonlinearity, seasonality, and memory effect in ENSO simulation and prediction using empirical model reduction, J. Climate, 29, 1809–1830, https://doi.org/10.1175/JCLI-D-15-0372.1, 2016. 
Publications Copernicus
Download
Short summary
Sea surface temperature (SST) is related to ocean heat content, an important topic in the debate over global warming. In this paper, we propose a novel SST-predicting method based on the hybrid improved EMD algorithms and BP neural network method. SST prediction results based on the hybrid EEMD-BPNN and CEEMD-BPNN models are compared and discussed. A case study of SST in the North Pacific shows that the proposed hybrid CEEMD-BPNN model can effectively predict the time-series SST.
Sea surface temperature (SST) is related to ocean heat content, an important topic in the debate...
Citation