Modeling Ocean Currents Through Complex Random Fields Indexed in Time

Abstract

Surface ocean currents are often of interest in environmental monitoring. These vectorial data can be reasonably treated as a finite realization of a complex-valued random field, where the decomposition in modulus (current speed) and direction (current direction) of the current field is natural. Moreover, when observations are also available for different time points (other than at several locations), it is useful to evaluate the evolution of their complex correlation over time (rather than in space) and the corresponding modeling which is required for estimation purposes. This paper illustrates a first approach where the temporal profile of surface ocean currents is considered. After introducing the fundamental aspects of the complex formalism of a random field indexed in time, a new class of models suitable for including the temporal component is proposed and applied to describe the time-varying complex covariance function of current data. The analysis concerns ocean current observations, taken hourly on 30 April 2016 through high frequency radar systems at some stations located in the Northeastern Caribbean Sea. The selected complex covariance model indexed in time is used for estimation purposes and its reliability is confirmed by a numerical analysis.

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Correspondence to Sandra De Iaco.

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Cappello, C., De Iaco, S., Maggio, S. et al. Modeling Ocean Currents Through Complex Random Fields Indexed in Time. Math Geosci (2020). https://doi.org/10.1007/s11004-020-09880-3

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Keywords

  • Vectorial data
  • Complex covariance
  • Complex random field indexed in time
  • Complex kriging