Jasim Mohammed Rajab, Mohd. Zubir Mat Jafri, Hwee San Lim, Khiruddin Abdullah
الملخص العربي
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 ¼ 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 ¼ 0.845 to 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.
جاسم محمد رجب حسين | Jasim Mohammed Rajab | 1581