SIOC 210: Introduction to Physical Oceanography

Data analysis

Lynne Talley, Fall, 2019


DPO Chapter 6 (6.1, 6.2, 6.3.1, 6.4, 6.5 (not 6.5.3, 6.5.4), 6.6.2, 6.7.1, 6.7.2)

Link to presentation
pdf of powerpoint


Basic concepts
Random and systematic error
Mean, variance, standard deviation, correlation, covariance

Time series analysis
Also known as spectral analysis, Fourier analysis, harmonic analysis
Can be done in either the time (frequency) domain or spatial (wavenumber) domain.
Determines dominant frequencies of variability (especially useful when the forcing has a well-defined frequency)
Shape of spectra can reveal underlying physics (red vs. white spectrum)
Fundamental frequency and Nyquist frequency

Multi-dimensional data analysis
Objective analysis
Maps spatially non-uniform data to a grid
incorporates correlation length scale and noise of observations
estimate is a weighted sum of nearby observations

Empirical Orthogonal Functions (EOFs)
Also known as Principle Component Analysis, Factor Analysis
Compact description of principal spatial and temporal variability
Called "empirical" because spatial structures are defined by the data as opposed to a set of mathematical basis functions (e.g. sine waves, Bessel functions, Legendre polynomials)

SIO 210 HOME Last modified: Oct. 7 2019