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Optical Engineering

Regression analysis in modeling of air surface temperature and factors affecting its value in Peninsular Malaysia
Author(s): Jasim M. Rajab; Mohd Zubir M. Jafri; Hwee-San Lim; Khiruddin Abdullah
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Paper Abstract

This study encompasses air surface temperature (AST) modeling in the lower atmosphere. Data of four atmosphere pollutant gases (CO, O3, CH4, and H2Ovapor) dataset, retrieved from the National Aeronautics and Space Administration Atmospheric Infrared Sounder (AIRS), from 2003 to 2008 was employed to develop a model to predict AST value in the Malaysian peninsula using the multiple regression method. For the entire period, the pollutants were highly correlated (R = 0.821) with predicted AST. Comparisons among five stations in 2009 showed close agreement between the predicted AST and the observed AST from AIRS, especially in the southwest monsoon (SWM) season, within 1.3 K, and for in situ data, within 1 to 2 K. The validation results of AST with AST from AIRS showed high correlation coefficient (R = 0.845 ?starttoend?0.918), indicating the model's efficiency and accuracy. Statistical analysis in terms of β showed that H2Ovapor (0.565 to 1.746) tended to contribute significantly to high AST values during the northeast monsoon season. Generally, these results clearly indicate the advantage of using the satellite AIRS data and a correlation analysis study to investigate the impact of atmospheric greenhouse gases on AST over the Malaysian peninsula. A model was developed that is capable of retrieving the Malaysian peninsulan AST in all weather conditions, with total uncertainties ranging between 1 and 2 K.

Paper Details

Date Published: 21 May 2012
PDF: 14 pages
Opt. Eng. 51(10) 101702 doi: 10.1117/1.OE.51.10.101702
Published in: Optical Engineering Volume 51, Issue 10
Show Author Affiliations
Jasim M. Rajab, Univ. Sains Malaysia (Malaysia)
Mohd Zubir M. Jafri, Univ. Sains Malaysia (Malaysia)
Hwee-San Lim, Univ. Sains Malaysia (Malaysia)
Khiruddin Abdullah, Univ. Sains Malaysia (Malaysia)

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