Abstract
ABSTRACT This research study aimed to evaluate the daily heat temperature pattern by first-order Markov chain, considering daily high temperature data in summer season (February to May), monsoon season (June to September) and winter season (October to January) of Ahmedabad, Gujarat, India, from 2001 to 2011. States are defined by bifurcating temperature into 11 small intervals for summer and monsoon and 9 intervals for winter. Hence, 11 × 11 and 9 × 9 step transition probability matrices (TPM) were computed, using conditional probability based on the historical data of 11 years of the daily temperature of 3 seasons. The TPMs are used along with the truncated exponential distribution for daily temperature to generate daily temperature for each state, in each season and annually, which depicts a result near to that obtained from the original data. Thus, such model becomes useful to study the meteorological phenomenon.
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