Abstract
Statistical models of rainfall have been applied in the understanding of the rainfall past trends, identifying for any anomalies, and making projections of future climate change in Malaysia. Herein, we analyse the rainfall data of 7-year period using the gamma and beta regression models to fit Malaysian extreme precipitation events of two stations, each in the West Coast region and the East Coast region, with extreme precipitation calendar date (in the angular form) as the predictor of the models. While the significance test as the p-value is much less than 0.05, it shows that there is a significant relationship between the climatology response variables. The deviance residual plot will be used to check the goodness of fit for diagnostic checking. The results show the models are useful in highlighting the latest trends and projections of climate change in Malaysia.
Highlights
The use of statistical models of rainfall has been applied worldwide to give a better understanding about the rainfall pattern and its characteristics
Rainfall models with weather predictors are valuable to explore the trends of rainfall in Malaysia
The importance of understanding the pattern of rainfall is emphasized in order to develop the new statistical model of the occurrence and amount of daily rainfall data with a set of predictors simultaneously. 7-year period of rainfall amount, mean of wind speed, and solar radiation influenced of the southwest and northeast monsoons were analysed by using the log gamma and beta regression models to fit Malaysian extreme precipitation events of two stations extreme precipitation calendar date in the angular form
Summary
The use of statistical models of rainfall has been applied worldwide to give a better understanding about the rainfall pattern and its characteristics. This process involves the understanding of the past trends, identifying for any anomalies, and making projections of future climate change in Malaysia. Based on [3], the rainfall events can be modelled as a Poisson process whereas the intensity of each rainfall event is Gamma distributed. By assuming rainfall arrives in forms of storms following a Poisson process, and the current intensity at each arrival time increases by a random amount based on Gamma distribution. The response variable is not always normally distributed in real data
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
