application of pca in matlab - university of british …vradic/eosc510/tutorial4.pdf · application...
TRANSCRIPT
Application of PCA in MATLAB
● Objectives:
1) Apply PCA on daily sea level pressure data for North Pacific (1979-2010)-> Tutorial4_SLP_data.m
2) Apply PCA on monthly sea surface temperature data for Tropical Pacific (1979-2015), looking for ENSO modes
-> Tutorial4_SST_data.m
Example 1: SLP dataPCA objective: we would like to capture the essence of the spatial SLP patterns
In MATLAB:% PCA:[eigenvectors,PCs,eigenvalues]=princomp(y);
% contribution of each mode to total variance:variance=eigenvalues./sum(eigenvalues);
Example 2: SST monthly data Data from ERA Interim climate reanalysis, Jan 1979- Jun 2015
The Oceanic Niño Index (ONI) has become the de-facto standard that NOAA uses for identifying El Niño (warm) and La Niña (cool) events in the tropical Pacific. It is the running 3-month mean SST anomaly for the Niño 3.4 region (i.e., 5oN-5oS, 120o-170oW).
Events are defined as 5 consecutive overlapping 3-month periods at or above the +0.5o anomaly for warm (El Niño) events and at or below the -0.5 anomaly for cold (La Niña) events.
Example 2: SST dataData from ERA Interim climate reanalysis, Jan 1979- Jun 2015
Here the domain is illustrated (SST in deg C)
Example 2: SST data% some pre-processing1) remove the average seasonal cycle, 2) take the 3-mean running average
Example 2: SST dataPCA objective: we would like to capture the essence of the spatial SST patterns (3-month running mean data)
In MATLAB:% PCA:[eigenvectors,PCs,eigenvalues]=princomp(y);
% contribution of each mode to total variance:variance=eigenvalues./sum(eigenvalues);