Add Principal Oscillation Patterns (POP)

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Is your feature request related to a problem? Please describe.
Principal Oscillation Patterns (POP) can capture the temporal evolution of spatial patterns, allowing for the analysis of the dynamics and interactions between different modes of variability in time series data. Unlike typical Empirical Orthogonal Function (EOF) or Hilbert EOF analysis, which focus on extracting dominant spatial patterns and their time coefficients, POP specifically addresses the phase relationships and oscillatory behavior of these patterns by explicitly modeling the temporal evolution of spatial patterns as oscillatory modes, providing insights into the underlying physical processes driving the variability.

Describe the solution you'd like
Add Principal Oscillation Patterns (POPs) by Hasselmann (1988).

Describe alternatives you've considered
None.

Additional context
None.