EOF Analysis Using Python Library
Posted: Thu Mar 31, 2016 11:09 amEmpirical Orthogonal Function (EOF) analysis is to simplify a spatial-temporal data set by transforming it to spatial patterns of variability and temporal projections of these patterns. The spatial patterns are the EOFs, and can be thought of as basis functions in terms of variance. The associated temporal projections are the pricipal components (PCs) and are the temporal coefficients of the EOF patterns.
In this article, we are using the EOF library created by Dawson to analysis SST data from AVHRR Level 4 dataset in central pacific ocean from 19982 to 2000. You can download the python eof python module and document from the link: http://ajdawson.github.io/eofs/. The results are shown in the following plots and you can download the sample code here: https://podaac-tools.jpl.nasa.gov/drive ... s_forum.py and data here: https://podaac-tools.jpl.nasa.gov/drive ... f/eof_data
In this article, we are using the EOF library created by Dawson to analysis SST data from AVHRR Level 4 dataset in central pacific ocean from 19982 to 2000. You can download the python eof python module and document from the link: http://ajdawson.github.io/eofs/. The results are shown in the following plots and you can download the sample code here: https://podaac-tools.jpl.nasa.gov/drive ... s_forum.py and data here: https://podaac-tools.jpl.nasa.gov/drive ... f/eof_data
- Figure 1. EOF 1 Mode and Time Series
- eof1.png (44.31 KiB) Viewed 10152 times
- Figure 2. EOF 2 Mode and Time Series
- eof2.png (42.36 KiB) Viewed 10152 times
- Figure 3. EOF 3 Mode and Time Series
- eof3.png (44.72 KiB) Viewed 10152 times
- Figure 4. EOF 4 Mode and Time Series
- eof4.png (45.92 KiB) Viewed 10152 times
- Figure 5. EOF 5 Mode and Time Series
- eof5.png (46.15 KiB) Viewed 10152 times
- Figure 6. Fraction of EOF Modes
- eof_fraction.png (14.61 KiB) Viewed 10152 times