Markov Chain: First Step towards Heat Wave Analysis in Malaysia

Abstract

Extreme temperature has been carried out around the world to provide awareness and proper opportunity for the societies to prepare necessary arrangements. In this present paper, the first order Markov chain model was applied to estimate the probability of extreme temperature based on the heat wave scales provided by the Malaysian Meteorological Department. In this study, the 24-year period (1994-2017) daily maximum temperature data for 17 meteorological stations in Malaysia was assigned to the four heat wave scales which are monitoring, alert level, heat wave and emergency. The analysis result indicated that most of the stations had three categories of heat wave scales. Only Chuping station had four categories while Bayan Lepas, Kuala Terengganu, Kota Bharu and Kota Kinabalu stations had two categories. The limiting probabilities obtained at each station showed a similar trend which the highest proportion of daily maximum temperature occurred in the scale of monitoring and followed by the alert level. This trend is apparent when the daily maximum temperature data revealed that Malaysia is experiencing two consecutive days of temperature below 35˚C.

Selecting the probability distribution of annual maximum temperature in Malaysia

“…The anthropogenic or human factors such as land conversion, industrialization and transportation release greenhouse gasses which amplify the air temperature [4]. Several studies have shown that extreme temperatures will increase heat waves [5]. Stress from excess heat may lead to blood pressure and heart diseases.…”