Generalized linear models for analyzing count data of rainfall occurrences

Abstract

Having the adequate knowledge about the behavior of climatic variables on the occurrences of rainfall is needed to the country’s economists and agriculturists for saving the country’s people from the devastating natural hazards like flash flood, drought, heavy rainfall, etc. Therefore, the study has been taken initiative to identify the influence of climatic variables for the occurrences of rainfall. The study has been developed generalized linear models (GLMs) for Poisson distribution for weekly and fortnightly count data of daily rainfall occurrences for the summer and monsoon seasons for five regional rainfall stations of Bangladesh. For these models, minimum and maximum temperatures and relative humidity are considered as explanatory variables. For five regional rainfall stations, the model selection procedures AIC and BIC indicate that the GLMs for the Poisson distribution satisfactorily explain the influence of climatic variables for the fortnightly occurrences of rainfall in the summer and monsoon seasons. The GLMs for the summer season of fortnightly occurrences of rainfall indicate that if one unit of relative humidity increases, then the probability of rainy days will be increased by 12 percent in Feni station, 6 percent in Sylhet, Khulna and Rajshahi stations, and 7 percent in Dhaka station. Besides, the GLMs for the monsoon season of fortnightly occurrences of rainfall indicate that if one unit increases of minimum temperature, then the probability of rainy days will be increased by 22 percent, 19 percent, 24 percent, 17 percent and 19 percent in Feni, Sylhet, Khulna, Rajshahi and Dhaka stations, respectively. Further, maximum temperature indicates negative influence on the occurrences of rainfall for all the stations and seasons of the period. The study indicates that the relative humidity for summer season and minimum temperature for monsoon season play remarkable role for changing fortnightly occurrences of rainfall in all the regions of the country.

Two decades of endemic dengue in Bangladesh (2000–2022): trends, seasonality, and impact of temperature and rainfall patterns on transmission dynamics

“…We then used a time series count generalized linear model (GLM), more specifically, a time series Poisson regression model, to determine whether the meteorological factors were associated with the change in dengue cases over time ( Sumi et al 2021 ). Monthly dengue cases were utilized as the outcome variable in this model predicted by temperature and rainfall data from the Bangladesh Meteorological Department (BMD).…”