Spatiotemporal Prediction of Satellite Altimetry Sea Level Anomalies in the Tropical Pacific Ocean

This letter developes and validates a machine learning approach to forecast sea level anomalies (SLAs) derived from satellite altimetry in the tropical Pacific Ocean. The empirical orthogonal function (EOF), also known as principal component analysis, was used to extract dominant signals and reduce the dimensionality of data sets. Such dimensionality was decreased by describing spatial patterns (EOFs) and the corresponding temporal domains [principal components (PCs)]. Support vector regression (SVR) was employed to predict the time series obtained from the leading PCs. Thereafter, the temporal and spatial SLAs from the proposed EOFs were reconstructed to represent the spatiotemporal SLA prediction. Finally, the prediction result was compared with that of the conventional autoregressive integrated moving average (ARIMA) model. Both models reached satisfactory sea level predictions. Even so, intercomparison of the obtained results showed that the SVR significantly ( $P = 0.012$ ) outperformed the ARIMA model in sea level forecasting. That is, a considerably low root-mean-square error was attained for the differences between the predicted and observed mean SLAs.

[1]  Ian T. Jolliffe,et al.  Empirical orthogonal functions and related techniques in atmospheric science: A review , 2007 .

[2]  C. Shum,et al.  THE ACCURACY AND APPLICATIONS OF SATELLITE ALTIMETRY , 1995 .

[3]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[4]  Özgür Kisi,et al.  Forecasting daily lake levels using artificial intelligence approaches , 2012, Comput. Geosci..

[5]  A. Cazenave,et al.  Sea-Level Rise and Its Impact on Coastal Zones , 2010, Science.

[6]  I. Gibson.  Statistics and Data Analysis in Geology , 1976, Mineralogical Magazine.

[7]  J. Edward Jackson,et al.  A User's Guide to Principal Components. , 1991 .

[8]  M. C. Deo,et al.  Forecasting wind with neural networks , 2003 .

[9]  C. Kuo,et al.  Forecasting Caspian Sea level changes using satellite altimetry data (June 1992-December 2013) based on evolutionary support vector regression algorithms and gene expression programming , 2014 .

[10]  Kai Zhang,et al.  Spatial–temporal variations of spring drought based on spring-composite index values for the Songnen Plain, Northeast China , 2014, Theoretical and Applied Climatology.

[11]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[12]  N. White,et al.  A 20th century acceleration in global sea‐level rise , 2006 .

[13]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[14]  Lipo Wang.  Support vector machines : theory and applications , 2005 .

[15]  Phillip A. Arkin,et al.  Biennial Variations in Surface Temperature over the United States as Revealed by Singular Decomposition , 1981 .

[16]  Haiying Wang.  Principal Component Analysis on Temporal-spatial Variations of Sea Level Anomalies from T/P Satellite Altimeter Data over the Northwest Pacific , 2001 .

[17]  Francis Eng Hock Tay,et al.  A comparative study of saliency analysis and genetic algorithm for feature selection in support vector machines , 2001, Intell. Data Anal..

[18]  Alexander Y. Sun,et al.  Development of Data-Driven Models , 2015 .

[19]  Joseph L. Awange,et al.  Decadal rainfall variability modes in observed rainfall records over East Africa and their relations to historical sea surface temperature changes , 2012 .

[20]  R. Preisendorfer,et al.  Principal Component Analysis in Meteorology and Oceanography , 1988 .

[21]  Douglas A. G. Vieira,et al.  Recent Advances in Neural Networks Structural Risk Minimization Based on Multiobjective Complexity Control Algorithms , 2010 .

[22]  Stefan Rahmstorf,et al.  Sea-level rise: towards understanding local vulnerability , 2012 .

[23]  L. Fenoglio-Marc.  Analysis and representation of regional sea-level variability from altimetry and atmospheric–oceanic data , 2001 .

[24]  J. Hay,et al.  Coastal systems and low-lying areas , 2007 .

[25]  Ian T. Jolliffe,et al.  A Cautionary Note on Artificial Examples of EOFs , 2003 .

[26]  I K Fodor,et al.  A Survey of Dimension Reduction Techniques , 2002 .

[27]  J. Kusche,et al.  Multivariate Prediction of Total Water Storage Changes Over West Africa from Multi-Satellite Data , 2014, Surveys in Geophysics.