This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric
parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour
(H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and
mean surface pressure (MSP)] data set, retrieved fromNASA’s Atmospheric Infrared Sounder (AIRS), for the
entire period (2003e2008) was employed to develop models to predict the value of columnar ozone (O3)
in study area. The combined method, which is based on using both multiple regressions combined with
principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined
approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was
carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was
negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO,
AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used
to fit the columnar ozone data using the atmospheric parameter’s variables as predictors. A variable
selection method based on high loading of varimax rotated principal components was used to acquire
subsets of the predictor variables to be comprised in the linear regression model of the atmospheric
parameter’s variables. It was found that the increase in columnar O3 value is associated with an
increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and
MSP. The result of fitting the best models for the columnar O3 value using eight of the independent
variables gave about the same values of the R (z0.93) and R2 (z0.86) for both the NEM and SWM
seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and
the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.
جاسم محمد رجب حسين | Jasim Mohammed Rajab | 1581