Ordinal Logistic Regression Analysis on the Nature of Monthly Rainfall Against ENSO, IOD, Monsoon and SST

Abstract

Climate variability is strongly influenced by large-scale phenomena such as El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), regional scales such as monsoon and local scales such as Sea Surface Temperature (SST) in waters close to a location. These climate phenomena interact with each other so that climate variability forms so that rainfall conditions can vary above normal, normal or below normal. Ordinal logistic regression is a regression method that can describe the relationship of climate phenomena to the nature of rainfall which is ordinal data. Based on partial tests, it was shown that ENSO, IOD and SST had a significant influence in influencing the nature of rain in the Semarang area. El Nino, Neutral IOD and IOD+ will provide an opportunity for above-normal or normal rainfall of 0.11, 0.20, 0.03 respectively against below-normal rainfall properties compared to their references (La Nina and IOD-). Meanwhile, SST can increase the chance of rain in the upper normal or normal category by 2.44 compared to the below normal conditions. The probability of normal upper (lower) rainfall will increase when La Nina (El Nino) and IOD- (IOD+) occur accompanied by the warming (cooling) of SST in the Java Sea.