## Data analysis

Lynne Talley, Fall, 2016

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Reading
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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)

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Link to powerpoint presentation**

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Link to SIO210 CSP powerpoint presentation

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Topics:

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Basic concepts
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Random and systematic error

Mean, variance, standard deviation, correlation, covariance
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Time series analysis
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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)

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Multi-dimensional data analysis
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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

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Empirical Orthogonal Functions (EOFs)
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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)

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Last modified: Oct. 10 2016