Analysis of rainfall occurrence in consecutive days using Markov models with covariate dependence in selected regions of Bangladesh

Abstract

The objectives of this study are to reveal the regional variation and to identify the responsible factors for making the transition of rainfall status in two consecutive days in Bangladesh at three selected regions. The covariate-dependent first-order Markov models have been used to find out how the change of rainfall status (rain and no rain) is affected by the selected factors. Among the considered factors, we found that the relative humidity, sunshine hour, maximum temperature, and cloud cover show positive association with the transition type no rain to rain for all three stations. However, the minimum temperature, maximum temperature, and sea level pressure have a negative effect on this transition type in all selected stations with the exception that the maximum temperature in Rajshahi and the minimum temperature in Rajshahi and Sylhet do not have any significant effect. On the other hand, with the exception, the maximum temperature in Rajshahi and sunshine hour in Dhaka and Sylhet, the relative humidity, maximum temperature, sunshine hour, and cloud cover show positive association with the transition type rain to rain, and the minimum temperature and sea level pressure show negative association with this transition type. The regional variations and responsible factors for changing the rainfall status also disclosed among the three selected regions over four seasons such as pre-monsoon, monsoon, post-monsoon, and winter. In all four seasons, the transition of the rainfall status experienced statistically significant positive impact for the exploratory variables relative humidity, sunshine hour, and cloud cover and negative impact for maximum temperature, minimum temperature, and sea level pressure with some exceptions. However, the humidity and cloud cover play the most important role in making the transition of the rainfall status between two consecutive days.