Modelling wind direction data of Langkawi Island during Southwest monsoon in 2019 to 2020 using bivariate linear functional relationship model with von Mises distribution

• Published in: Journal of Physics: Conference Series
• Main Author: Mokhtar N.A.; Zubairi Y.Z.; Hussin A.G.; Badyalina B.; Ghazali A.F.; Ya'Acob F.F.; Shamala P.; Kerk L.C.
• Format: Conference paper
• Language: English
• Published: IOP Publishing Ltd 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114207010&doi=10.1088%2f1742-6596%2f1988%2f1%2f012097&partnerID=40&md5=d4f8a216318ce89b8e98f2edfc35a19b

The weather in Malaysia is characterised by two monsoons, namely, the southwest monsoon from May to September, and the northeast monsoon from November to March. Wind direction is essential in observing the weather patterns and global climate. In this study, our interest is on investigating the relationship of the wind direction data of Langkawi Island in Malaysia during the southwest monsoon from year 2019 to 2020. It is essential to highlight that wind direction data that is circular and this requires different statistical techniques from the techniques that are used to analyse linear data. In this paper, we model the relationship of wind direction data by using the bivariate functional relationship model with von Mises distribution. The magnificence of this model is that the existence of error terms in all variables is considered. When modelling the data, outliers of the wind direction data are identified by using the covratio method that considers row deletion. The covariance matrix of the parameter estimates of the data is obtained by using the Fisher information matrix. Also, the result is supported by the Q-Q plots of the von Mises that indicate the goodness-of-fit of the wind direction data to the von Mises distribution. Then, maximum likelihood estimation is used in obtaining the parameter estimates of the data and hence, the model of the wind direction data is attained. The implications of this study provides an improved comprehension of the behaviour of wind direction and may be used for the prediction of wind energy in future. © Published under licence by IOP Publishing Ltd.