Characterising the variability in ocean data using fractal and correlation dimension analysis

Abstract

Understanding the variability of ocean data is essential for predicting its behaviour in time domain. This study aims to examine the usability of several nonlinear time series analysis techniques for diagnosing the complexity of the underlying dynamics of various ocean data. For such purpose, one-year data from an offshore platform and two-year data from a weather buoy were collected and analysed. The results indicate that all the ocean data considered here are chaotic in nature, reflected by the unfolded structure in the reconstructed phase space. In general, the surface currents speed possess the largest fractal dimension (or lowest Hurst exponent) and correlation dimension, while the significant wave height has the lowest values. This implies that the dynamics associated with surface currents speed tend to be the most complex, which can lead to more random and unpredictable temporal behaviour. The fractal analysis shows that the fluctuation of these ocean data are mostly persistent, and the dependence of fractal properties on window length appears to vary amongst different ocean data. Overall, the results derived from various analysis techniques are consistent, which confirms the potential of using chaotic time series analysis technique for characterizing ocean data variability.