Statistical Modeling of Impacts El Niño Southern Oscillations (ENSO) on Land Surface Temperature in Small Medium Size City: Case Study Kuching Sarawak

Abstract

El Niño Southern Oscillation (ENSO) can affect the daily temperature and the amount of rainfall and extreme weather such as floods and droughts. For that reason, scientists need to understand the process of developing ENSO and develop statistical models to predict the impact of ENSO to land surface temperature. The remote sensing data provide spatial information that allows analyzing the influence of ENSO on land surface temperature spatial patterns. This study examines the ability of remote sensing data to study and develop model statistical for predicting the ENSO effect on land surface temperature spatial patterns. Remote sensing data needs to go through a pre-process and digital Number conversion to Land Surface Temperature (LST). To ensure accurate remote sensing information, the calibration process is carried out using temperature records from the Meteorological Malaysia Department (MMD). The next step is to conduct a correlation analysis between LST and Oceanic Niño Index (ONI). The final step is to use linear regression in building a statistical model forecasting the influence of ENSO on temperature and LST. The result found that changes in ONI values influence the value of LST and temperature. Improving knowledge and understanding of ENSO can provide ideas and strategies in reducing and adapting to the impact of ENSO on human beings.