Module 4 Flashcards

1
Q

Covariance matrices are always…

A

Square (where m=n) and symmetric

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2
Q

Principal Component Analysis (PCA)

A

Way of organizing complex data into simplistic structures that order variance

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3
Q

Scatter plots reveal…

A

Correlation and covariance

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4
Q

Covariance

A

Measure in how variance and mean values between two or more locations are related to each other

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5
Q

Row vector

A

A matrix of a single row where m = 1

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6
Q

Column vector

A

A matrix of a single column where n = 1

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7
Q

What is a useful working definition of climate?

A

The average of weather over time

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8
Q

Climate is what you expect while weather…

A

Is what you get

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9
Q

Eigenvectors (e)

A

Special column vectors which remain unchanged when multiplied by a matrix, except that they are multiplied by a scalar, the eigenvalue

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10
Q

How to arrange eigenvectors

A

In order of decreasing eigenvalue

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11
Q

Total variance is the sum of the…

A

Eigenvalues

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12
Q

Eigenvectors of a covariance matrix are called…

A

Principal components

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13
Q

Principal components are useful…

A

For identifying climate signals

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14
Q

What kind of information do principal components provide?

A

How data co-varies at different locations

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15
Q

Empirical Orthogonal Fuctions (EOFs)

A

Analysis to study patterns of variability and how they change with time

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16
Q

Pacific Decadal Oscillation (PDO)

A

The leading EOF of sea surface temperature anomalies over the North Pacific Ocean

17
Q

North Pacific Gyre Oscillation (NPGO)

A

Relates to changes in North Pacific Gyre Circulation, considering pressure gradients, geostrophic currents, and sea surface height

18
Q

Victoria Mode

A

Second EOF of sea surface temperature anomalies which measure north-to-south change rather than east-to-west

19
Q

Positive phase of NPGO

A

Stronger gyre circulations

20
Q

Negative phase of NPGO

A

Weaker gyre circulations