A statistical model to predict and analyze air surface temperature based on remotely sensed observations

Area of Science:

• Atmospheric Science
• Remote Sensing
• Climate Studies

Background:

• Air surface temperature (AST) is vital for hydrology, agriculture, and climate change research.
• Understanding atmospheric parameters influencing AST is crucial for accurate climate modeling.
• Iraq's specific atmospheric conditions require localized temperature prediction models.

Purpose of the Study:

• To develop a regression equation for calculating AST in Iraq.
• To analyze the influence of atmospheric parameters (O3, CH4, CO, H2Ovapor, OLR) on AST.
• To validate the model using satellite and ground station data.

Main Methods:

• Utilized data from the Atmospheric Infrared Sounder (AIRS) on the EOS Aqua Satellite (2003-2016).
• Employed multiple linear regression analysis to establish relationships between AST and atmospheric parameters.
• Compared predicted AST with observed AST from AIRS and ground stations across multiple locations and months.

Main Results:

• Five atmospheric parameters showed high correlation (R: 0.855-0.958) with predicted AST.
• Outgoing longwave radiation (OLR) was identified as a significant factor enhancing AST.
• Model predictions closely agreed with observed AST, with low variability (RMSE ≤ 1.9 K).

Conclusions:

• The developed regression model effectively calculates AST in Iraq.
• AIRS satellite data combined with regression analysis is advantageous for studying atmospheric impacts on AST.
• The findings highlight the significant role of OLR in influencing air surface temperature over Iraq.