Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

Jasim M. Rajab; M.Z. MatJafri; H.S. Lim

Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

Date

2013

Authors

Jasim M. Rajab

M.Z. MatJafri

H.S. Lim

Publisher

Elsevier

Abstract

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 from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) 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.

Keywords

Ozone (O3), Regression analysis, Principal component analysis, Atmosphere infrared sounder (AIRS)

Citation

Rajab, J. M., MatJafri, M. Z., & Lim, H. S. (2013). Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia. Atmospheric Environment, 71, 36-43.