In this study, we used Markov chain approach to analyze rainfall dataset from one buoy (4N90E) in the eastern Equatorial Indian Ocean. This study aims to determine the opportunity for transition (displacement) of daily rainfall intensity, where there are six states or conditions of rainfall intensity, i.e. no rain, very weak, weak rain, moderate rain, heavy rain, and very heavy rain. The Markov Chain method used is the Chapman-Kolmogorov equation and the steady state equation. The investigation of the 6-states in Markov chain model show that dynamic probability of transition state for rainfall data is reflexive properties majority. By using the model, it is concluded that the transition rate matrix of the largest transition probability in the area of 4N90E occurs at the transition from state-1 to state-1 as much is 0.72 and state-2 to state-2 is 0.60. The transition probability value becomes 0.5184 and 0.36 for the same state dominant of two periods P
2. The use of the 1st order Markov chain is better than 2nd order.
Climate change is one of the issues and a considerable threat to the environment in the future. These changes tend to fluctuate and vary significantly with time. Mostly, climate change issues are related to global temperature. The Earth’s climate system is effected by many parameters. Sea Surface Temperature Anomaly (SSTA) is one of those parameters. The phenomena of SSTA is a necessary indicator for understanding of climate change variability. Aceh province as to the west of Indonesia has location face directly to the Indian Ocean, especially in western region, southwest, and southern. Vector Autoregressive (VAR) approach has shown that SSTA dataset has stationer and non-cointegration properties in the period 2006-2017. Based on this research, it can be concluded that the best model for SSTA with climate parameters (air temperature, rainfall, relative humidity, wind speed and short-wave radiation) is VAR with the 4th optimal lag or VAR(4). The Impulse Response Function (IRF) analysis based on VAR(4) model, which is formed to look at the phenomena of SSTA to climate parameters, shows that wind speed, rainfall and short-wave radiation have a similar pattern of detection on the equilibrium line due to shock from SSTA. It takes around 5 days for the three variables to reach the equilibrium line. Whereas the air temperature and relative humidity variables have no significant effects of shocks that occur in the sea surface temperature anomaly.
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