Open AccessGeneralized additive models fitting with autocorrelation for sea surface temperature anomaly data
Author(s) -
S. Ananda,
Miftahuddin
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/523/1/012002
Subject(s) - autocorrelation , sea surface temperature , anomaly (physics) , generalized additive model , climatology , indian ocean dipole , environmental science , precipitation , wind speed , extreme value theory , meteorology , atmospheric sciences , statistics , geography , geology , mathematics , physics , condensed matter physics
Climatic conditions in Sumatra Island are affected by Sea Surface Temperature Anomaly (SSTA) in the Indian Ocean or more commonly referred to as Indian Ocean Dipole (IOD). Extreme climate events also closely related to SSTA. Several climate features that affect SSTA such as air temperature, precipitation rain, relative humidity, wind speed, and solar radiation. SSTA is an increase or decrease in the mean of Sea Surface Temperature so required the analysis to assess extreme climatic events to the risk due to the occurrence of anomalies. Generalized Additive Models (GAM) with autocorrelation can be used to model this phenomenon. GAM method accommodates the nonlinear influence between response variables and predictor variables. The data used in this research is the time series data, daily data from 2006-2017 and there are gaps in it, where there is an autocorrelation value. The purpose of this research is to get a representative model and to know the factors that influence to SSTA. The results show that GAM’s best model with autocorrelation is GAM model by including month and year variables with the monthly autocorrelation structure. Factors that affecting SSTA are the air temperature, month and year as time covariates.