Predictability of the Loop Current Variation and Eddy Shedding Process in the Gulf of Mexico Using an Artificial Neural Network Approach

Xiangming Zeng Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina

Search for other papers by Xiangming Zeng in
Current site
Google Scholar
PubMed
Close
,
Yizhen Li Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina

Search for other papers by Yizhen Li in
Current site
Google Scholar
PubMed
Close
, and
Ruoying He Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina

Search for other papers by Ruoying He in
Current site
Google Scholar
PubMed
Close
Restricted access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

Abstract

A novel approach based on an artificial neural network was used to forecast sea surface height (SSH) in the Gulf of Mexico (GoM) in order to predict Loop Current variation and its eddy shedding process. The empirical orthogonal function analysis method was applied to decompose long-term satellite-observed SSH into spatial patterns (EOFs) and time-dependent principal components (PCs). The nonlinear autoregressive network was then developed to predict major PCs of the GoM SSH in the future. The prediction of SSH in the GoM was constructed by multiplying the EOFs and predicted PCs. Model sensitivity experiments were conducted to determine the optimal number of PCs. Validations against independent satellite observations indicate that the neural network–based model can reliably predict Loop Current variations and its eddy shedding process for a 4-week period. In some cases, an accurate forecast for 5–6 weeks is possible.

Corresponding author address: Ruoying He, Dept. of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Campus Box 8208, 2800 Faucette Drive, Raleigh, NC 27695. E-mail: rhe@ncsu.edu

Abstract

A novel approach based on an artificial neural network was used to forecast sea surface height (SSH) in the Gulf of Mexico (GoM) in order to predict Loop Current variation and its eddy shedding process. The empirical orthogonal function analysis method was applied to decompose long-term satellite-observed SSH into spatial patterns (EOFs) and time-dependent principal components (PCs). The nonlinear autoregressive network was then developed to predict major PCs of the GoM SSH in the future. The prediction of SSH in the GoM was constructed by multiplying the EOFs and predicted PCs. Model sensitivity experiments were conducted to determine the optimal number of PCs. Validations against independent satellite observations indicate that the neural network–based model can reliably predict Loop Current variations and its eddy shedding process for a 4-week period. In some cases, an accurate forecast for 5–6 weeks is possible.

Corresponding author address: Ruoying He, Dept. of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Campus Box 8208, 2800 Faucette Drive, Raleigh, NC 27695. E-mail: rhe@ncsu.edu
Save
  • Alvarez, A., 2003: Performance of satellite-based ocean forecasting (SOFT) systems: A study in the Adriatic Sea. J. Atmos. Oceanic Technol., 20, 717729, doi:10.1175/1520-0426(2003)20<717:POSBOF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Alvarez, A., Lopez C. , Riera M. , Hernandez-Garcia E. , and Tintore J. , 2000: Forecasting the SST space-time variability of the Alboran Sea with genetic algorithms. Geophys. Res. Lett., 27, 27092712, doi:10.1029/1999GL011226.

    • Search Google Scholar
    • Export Citation
  • Androulidakis, Y. S., Kourafalou V. H. , and Le Hénaff M. , 2014: Influence of frontal cyclones evolution on the 2009 (Ekman) and 2010 (Franklin) Loop Current Eddy detachment events. Ocean Sci. Discuss., 11, 19491994, doi:10.5194/osd-11-1949-2014.

    • Search Google Scholar
    • Export Citation
  • Beckers, J.-M., and Rixen M. , 2003: EOF calculations and data filling from incomplete oceanographic datasets. J. Atmos. Oceanic Technol., 20, 18391856, doi:10.1175/1520-0426(2003)020<1839:ECADFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Beckers, J.-M., and Coauthors, 2002: Model intercomparison in the Mediterranean: MEDMEX simulations of the seasonal cycle. J. Mar. Syst., 33–34, 215251, doi:10.1016/S0924-7963(02)00060-X.

    • Search Google Scholar
    • Export Citation
  • Chelton, D. B., Schlax M. G. , and Samelson R. M. , 2011: Global observations of nonlinear mesoscale eddies. Prog. Oceanogr., 91, 167216, doi:10.1016/j.pocean.2011.01.002.

    • Search Google Scholar
    • Export Citation
  • Collecte Localisation Satellites, 2011: SSALTO/DUACS user handbook: (M)SLA and (M)ADT near-real time and delayed time products. Version 2rev4, Rep. SALP-MU-P-EA-21065-CLS, 49 pp.

  • Counillon, F., and Bertino L. , 2009: High-resolution ensemble forecasting for the Gulf of Mexico eddies and fronts. Ocean Dyn., 59, 8395, doi:10.1007/s10236-008-0167-0.

    • Search Google Scholar
    • Export Citation
  • Forristall, G. Z., Leben R. R. , and Hall C. A. , 2010: SS: Metocean: A statistical hindcast and forecast model for the Loop Current. Proc. Offshore Technology Conf., Houston, TX, OTC, Paper OTC-20602-MS, 12 pp., doi:10.4043/20602-MS.

  • Gopalakrishnan, G., Cornuelle B. D. , Hoteit I. , Rudnick D. L. , and Owens W. B. , 2013: State estimates and forecasts of the loop current in the Gulf of Mexico using the MITgcm and its adjoint. J. Geophys. Res. Oceans, 118, 32923314, doi:10.1002/jgrc.20239.

    • Search Google Scholar
    • Export Citation
  • Hannachi, A., 2004: A primer for EOF analysis of climate data. University of Reading Rep., 33 pp. [Available online at http://www.met.rdg.ac.uk/~han/Monitor/eofprimer.pdf.]

  • He, R., Weisberg R. H. , Zhang H. , Muller-Karger F. E. , and Helber R. W. , 2003: A cloud-free, satellite-derived, sea surface temperature analysis for the West Florida Shelf. Geophys. Res. Lett., 30, 1811, doi:10.1029/2003GL017673.

    • Search Google Scholar
    • Export Citation
  • Hendricks, J. R., Leben R. R. , Born G. H. , and Koblinsky C. J. , 1996: Empirical orthogonal function analysis of global TOPEX/POSEIDON altimeter data and implications for detection of global sea level rise. J. Geophys. Res., 101, 14 13114 145, doi:10.1029/96JC00922.

    • Search Google Scholar
    • Export Citation
  • Holbrook, N. J., and Bindoff N. L. , 2000: A statistically efficient mapping technique for four-dimensional ocean temperature data. J. Atmos. Oceanic Technol., 17, 831846, doi:10.1175/1520-0426(2000)017<0831:ASEMTF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hsieh, W. W., 2001: Nonlinear principal component analysis by neural networks. Tellus, 53A, 599615, doi:10.1034/j.1600-0870.2001.00251.x.

    • Search Google Scholar
    • Export Citation
  • Hsieh, W. W., 2004: Nonlinear multivariate and time series analysis by neural network methods. Rev. Geophys., 42, RG1003, doi:10.1029/2002RG000112.

    • Search Google Scholar
    • Export Citation
  • Hsieh, W. W., and Tang B. , 1998: Applying neural network models to prediction and data analysis in meteorology and oceanography. Bull. Amer. Meteor. Soc., 79, 18551870, doi:10.1175/1520-0477(1998)079<1855:ANNMTP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Jain, A. K., Mao J. , and Mohiuddin K. M. , 1996: Artificial neural networks: A tutorial. Computer, 29, 3144, doi:10.1109/2.485891.

  • Kaastra, I., and Boyd M. , 1996: Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10, 215236, doi:10.1016/0925-2312(95)00039-9.

    • Search Google Scholar
    • Export Citation
  • Krasnopolsky, V., 2013: The Application of Neural Networks in the Earth System Sciences: Neural Networks Emulations for Complex Multidimensional Mappings.Atmospheric and Oceanographic Sciences Library, Vol. 46, Springer Science & Business, 205 pp.

  • Kwong, K. M., Wong M. H. Y. , Liu J. N. K. , and Chan P. W. , 2012: An artificial neural network with chaotic oscillator for wind shear alerting. J. Atmos. Oceanic Technol., 29, 15181531, doi:10.1175/2011JTECHA1501.1.

    • Search Google Scholar
    • Export Citation
  • Leben, R. R., 2005: Altimeter-derived Loop Current metrics. Circulation in the Gulf of Mexico: Observations and Models,Geophys. Monogr., Vol. 161, Amer. Geophys. Union, 181–201.

  • Leben, R. R., and Honaker D. J. , 2006: What do we know and what can we predict about the timing of Loop Current Eddy separation? Proceedings of the Symposium on 15 Years of Progress in Radar Altimetry, D. Danesy, Ed., ESA Special Publ. SP-614, Paper 19.

  • Lee, T.-L., 2006: Neural network prediction of a storm surge. Ocean Eng., 33, 483494, doi:10.1016/j.oceaneng.2005.04.012.

  • Le Hénaff, M., Kourafalou V. H. , Morel Y. , and Srinivasan A. , 2012: Simulating the dynamics and intensification of cyclonic Loop Current Frontal Eddies in the Gulf of Mexico. J. Geophys. Res., 117, C02034, doi:10.1029/2011JC007279.

    • Search Google Scholar
    • Export Citation
  • Li, Y., and He R. , 2014: Spatial and temporal variability of SST and ocean color in the Gulf of Maine based on cloud-free SST and chlorophyll reconstructions in 2003–2012. Remote Sens. Environ., 144, 98108, doi:10.1016/j.rse.2014.01.019.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., Weisberg R. H. , and Yuan Y. , 2008: Patterns of upper layer circulation variability in the South China Sea from satellite altimetry using the Self-Organizing Map. Acta Oceanol. Sin., 27 (Suppl.), 129144.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., MacFadyen A. , Ji Z.-G. , and Weisberg R. H. , 2013: Monitoring and Modeling the Deepwater Horizon Oil Spill: A Record Breaking Enterprise.John Wiley & Sons, 280 pp.

  • Lugo-Fernández, A., and Leben R. R. , 2010: On the linear relationship between Loop Current retreat latitude and eddy separation period. J. Phys. Oceanogr., 40, 27782784, doi:10.1175/2010JPO4354.1.

    • Search Google Scholar
    • Export Citation
  • Maier, H. R., and Dandy G. C. , 2000: Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications. Environ. Modell. Software, 15, 101124, doi:10.1016/S1364-8152(99)00007-9.

    • Search Google Scholar
    • Export Citation
  • Maier, H. R., Jain A. , Dandy G. C. , and Sudheer K. P. , 2010: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environ. Modell. Software, 25, 891909, doi:10.1016/j.envsoft.2010.02.003.

    • Search Google Scholar
    • Export Citation
  • Mason, E., Pascual A. , and McWilliams J. C. , 2014: A new sea surface height–based code for oceanic mesoscale eddy tracking. J. Atmos. Oceanic Technol., 31, 1181–1188, doi:10.1175/JTECH-D-14-00019.1

    • Search Google Scholar
    • Export Citation
  • Miles, T. N., and He R. , 2010: Temporal and spatial variability of Chl-a and SST on the South Atlantic Bight: Revisiting with cloud-free reconstructions of MODIS satellite imagery. Cont. Shelf Res., 30, 19511962, doi:10.1016/j.csr.2010.08.016.

    • Search Google Scholar
    • Export Citation
  • Mooers, C. N. K., Zaron E. D. , and Howard M. K. , 2012: Final report for phase I: Gulf of Mexico 3-D Operational Ocean Forecast System Pilot Prediction Project (GOMEX-PPP). Final Rep. to Research Partnership to Secure Energy for America, 149 pp.

  • Murphy, A. H., 1988: Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon. Wea. Rev., 116, 24172424, doi:10.1175/1520-0493(1988)116<2417:SSBOTM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Oey, L. Y., Ezer T. , Forristall G. , Cooper C. , DiMarco S. , and Fan S. , 2005a: An exercise in forecasting loop current and eddy frontal positions in the Gulf of Mexico. Geophys. Res. Lett., 32, L12611, doi:10.1029/2005GL023253.

    • Search Google Scholar
    • Export Citation
  • Oey, L. Y., Ezer T. , and Lee H.-C. , 2005b: Loop Current, rings and related circulation in the Gulf of Mexico: A review of numerical models and future challenges. Circulation in the Gulf of Mexico: Observations and Models, Geophys. Monogr., Vol. 161, Amer. Geophys. Union, 31–56.

  • Oey, L. Y., Ezer T. , Wang D.-P. , Fan S.-J. , and Yin X.-Q. , 2006: Loop Current warming by Hurricane Wilma. Geophys. Res. Lett., 33, L08613, doi:10.1029/2006GL025873.

    • Search Google Scholar
    • Export Citation
  • Oliveira, A. P., Soares J. , Božnar M. Z. , Mlakar P. , and Escobedo J. F. , 2006: An application of neural network technique to correct the dome temperature effects on pyrgeometer measurements. J. Atmos. Oceanic Technol., 23, 8089, doi:10.1175/JTECH1829.1.

    • Search Google Scholar
    • Export Citation
  • Pedder, M., and Gomis D. , 1998: Applications of EOF analysis to the spatial estimation of circulation features in the ocean sampled by high-resolution CTD soundings. J. Atmos. Oceanic Technol., 15, 959978, doi:10.1175/1520-0426(1998)015<0959:AOEATT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Richards, W. J., McGowan M. F. , Leming T. , Lamkin J. T. , and Kelley S. , 1993: Larval fish assemblages at the Loop Current boundary in the Gulf of Mexico. Bull. Mar. Sci., 53, 475537.

    • Search Google Scholar
    • Export Citation
  • Rixen, M., Beckers J.-M. , Alvarez A. , and Tintore J. , 2002: Results on SSH neural network forecasting in the Mediterranean Sea. Remote Sensing of the Ocean and Sea Ice 2001, M. Rixen et al., Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 4544), 24, doi:10.1117/12.452757.

  • Sammarco, P. W., Atchison A. D. , and Boland G. S. , 2004: Expansion of coral communities within the Northern Gulf of Mexico via offshore oil and gas platforms. Mar. Ecol.: Prog. Ser., 280, 129143, doi:10.3354/meps280129.

    • Search Google Scholar
    • Export Citation
  • Shay, L. K., and Uhlhorn E. W. , 2008: Loop Current response to Hurricanes Isidore and Lili. Mon. Wea. Rev., 136, 32483274, doi:10.1175/2007MWR2169.1.

    • Search Google Scholar
    • Export Citation
  • Small, R. J., and Coauthors, 2008: Air–sea interaction over ocean fronts and eddies. Dyn. Atmos. Oceans,45, 274319, doi:10.1016/j.dynatmoce.2008.01.001.

    • Search Google Scholar
    • Export Citation
  • Tang, B., Hsieh W. W. , Monahan A. H. , and Tangang F. T. , 2000: Skill comparisons between neural networks and canonical correlation analysis in predicting the equatorial Pacific sea surface temperatures. J. Climate, 13, 287–293, doi:10.1175/1520-0442(2000)013<0287:SCBNNA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tang, Y., and Hsieh W. W. , 2001: Coupling neural networks to incomplete dynamical systems via variational data assimilation. Mon. Wea. Rev., 129, 818834, doi:10.1175/1520-0493(2001)129<0818:CNNTID>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Vautard, R., Yiou P. , and Ghil M. , 1992: Singular-spectrum analysis: A toolkit for short, noisy chaotic signals. Physica D, 58, 95126, doi:10.1016/0167-2789(92)90103-T.

    • Search Google Scholar
    • Export Citation
  • Wu, A., Hsieh W. W. , and Tang B. , 2006: Neural network forecasts of the tropical Pacific sea surface temperatures. Neural Networks, 19, 145154, doi:10.1016/j.neunet.2006.01.004.

    • Search Google Scholar
    • Export Citation
  • Xu, F.-H., Oey L.-Y. , Miyazawa Y. , and Hamilton P. , 2013: Hindcasts and forecasts of Loop Current and eddies in the Gulf of Mexico using local ensemble transform Kalman filter and optimum-interpolation assimilation schemes. Ocean Modell., 69, 2238, doi:10.1016/j.ocemod.2013.05.002.

    • Search Google Scholar
    • Export Citation
  • Xue, Z., He R. , Fennel K. , Cai W.-J. , Lohrenz S. , and Hopkinson C. , 2013: Modeling ocean circulation and biogeochemical variability in the Gulf of Mexico. Biogeosciences, 10, 72197234, doi:10.5194/bg-10-7219-2013.

    • Search Google Scholar
    • Export Citation
  • Yin, X.-Q., and Oey L.-Y. , 2007: Bred-ensemble ocean forecast of loop current and rings. Ocean Modell., 17, 300326, doi:10.1016/j.ocemod.2007.02.005.

    • Search Google Scholar
    • Export Citation
  • Yin, Y., Lin X. , Li Y. , and Zeng X. , 2014: Seasonal variability of Kuroshio intrusion northeast of Taiwan Island as revealed by self-organizing map. Chin. J. Oceanol. Limnol., 32, 1435–1442, doi:10.1007/s00343-015-4017-x.

    • Search Google Scholar
    • Export Citation
  • Yip, Z. K., and Yau M. K. , 2012: Application of artificial neural networks on North Atlantic tropical cyclogenesis potential index in climate change. J. Atmos. Oceanic Technol., 29, 12021220, doi:10.1175/JTECH-D-11-00178.1.

    • Search Google Scholar
    • Export Citation
  • Zeng, X., Li Y. , He R. , and Yin Y. , 2015: Clustering of Loop Current patterns based on the satellite-observed sea surface height and self-organizing map. Remote Sens. Lett., 6, 11–19, doi:10.1080/2150704X.2014.998347.

    • Search Google Scholar
    • Export Citation
  • Zhao, Y., and He R. , 2012: Cloud-free sea surface temperature and colour reconstruction for the Gulf of Mexico: 2003–2009. Remote Sens. Lett., 3, 697706, doi:10.1080/01431161.2012.666638.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1233 466 133
PDF Downloads 696 142 13