Applied topological data analysis to El-Niño Southern Oscillation
Abstract
The El Niño Southern Oscillation (ENSO) is one of the most powerful climate phenomena
that can change global air circulation, affecting temperature and rainfall around the planet. ENSO
has three phases: El Niño and La Niña are two extreme phases of the ENSO cycle, and ENSO
neutral is a transitional period between El Niño and La Niña.
Topological data analysis (TDA) is an innovative approach which focuses on a data set's
"shape" or topological structures such loops, holes, and voids. We used TDA to describe the
homology groups of the two-dimensional function determined by sea surface temperatures of
tropical Pacific Ocean. The persistent homology of the function, which is determined by the
monthly mean sea surface temperature in an expressed area of the Pacific, was used to identify
ENSO phases. We classify using TDA summaries and compare the results to the existing NOAA
classification utilizing topology information of sea surface temperatures of correctly specified
ENSO phases.
We compute the morse filtration's persistent homology for each month, then utilize the
ECC to summarize the persistent homology. To reduce arbitrary errors and make the curve
continuous, we utilized the smoothing function. We used LDA algorithm as a classification
method and for dimension reduction, we used PCA transformation.
Description
Thesis (M.S.)-- Wichita State University, College of Liberal Arts and Sciences, Dept. of Mathematics, Statistics, and Physics