In our research work, we used quantile regression analysis to find out the trend of the temperature of Bangladesh and to calculate the forecasted temperature for different areas. Different established methods were used earlier but because of the existence of extreme values, quantile regression is preferred compared to linear regression and other methods. Here we used average, maximum and minimum temperatures from 1990 to 2013 at eight different stations of Bangladesh. Dhaka and Mymensingh stations represent central region, Barisal and Feni represent southern region, Rangpur station represents northern region, Rajshahi represents north western region, Khulna represents west southern region and Sylhet station represents north eastern region. Data is collected from the Bangladesh Meteorological Department (BMD). Temperature of an area changes with seasons. So we used moving average method and deseasonalization to make the data linear. After removing seasonal variation and employing quantile regression, the results showed that the temperature of Bangladesh is increasing significantly year by year which is an alarming sign. Among these eight stations, the rate of change of temperature is highest for Sylhet station. We also analyzed seasonal adjusted forecast and the predictions of temperatures were cross checked with the observed temperatures of previous years.

trend, quantile regression, seasonal effect, deseasonalization, forecasting, Bangladesh.

Received: September 6, 2020; Accepted: October 27, 2020; Published: January 29, 2021

How to cite this article: Diponkar Das and Nazneen Sultana, Climate change in Bangladesh: a quantile trend of temperature, Advances and Applications in Statistics 66(2) (2021), 145-164. DOI: 10.17654/AS066020145

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

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