An Investigation Towards The Suitability Of Vector Autoregressive Approach On Modeling Meteorological Data

Siti Mariam Norrulashikin

doi:10.5539/mas.v9n11p89

Abstract

In most meteorological problems, two or more variables evolve over time. These variables not only haverelationships with each other, but also depend on each other. Although in many situations the interest was onmodelling single variable as a vector time series without considering the impact other variables have on it. Thevector autoregression (VAR) approach to multiple time series analysis are potentially useful in many types ofsituations which involve the building of models for discrete multivariate time series. This approach has 4important stages of the process that are data pre-processing, model identification, parameter estimation, andmodel adequacy checking. In this research, VAR modeling strategy was applied in modeling three variables ofmeteorological variables, which include temperature, wind speed and rainfall data. All data are monthly data,taken from the Kuala Krai station from January 1985 to December 2009. Two models were suggested byinformation criterion procedures, however VAR (3) model is the most suitable model for the data sets based onthe model adequacy checking and accuracy testing.

Highlights

The climate change has been a global issue and always one of the most imperative topics in water resources
We focused on three unit root test, Augmented Dickey-Fuller (ADF) test, Phillip Peron (PP) test and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test
Rainfall was said to Granger-cause temperature and wind speed, meaning that temperature and wind speed could be better predicted using all the three variables; temperature, wind speed and rainfall than it could by using only temperature and wind speed alone

Summary

Introduction

The climate change has been a global issue and always one of the most imperative topics in water resources. Weather parameters such as Precipitation, Temperature, Wind speed and Relative Humidity modelling and forecasting could be practically useful in risk management, water resource management and making decisions on climate change. These variables have undeniable effects on the hydrological cycle, agriculture and the environments. Modelling these physical processes deterministically may become a very challenging task due to the complexity of natural systems. Studies on relative humidity can be seen in (Shiri et al, 2011; Jäntschi, 2011; Jamiyansharav, 2011; etc.)

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