Attempt to Using Machine Learning Technique for Temperature Profile Estimation

Accurate temperature profile estimation is a critical component in various atmospheric studies and applications, including weather forecasting, climate modeling, and atmospheric dynamics research. This study explores the potential of employing machine learning techniques to enhance temperature profile estimation by combining data from satellite and reanalysis dataset. This study attempts to leverage the strengths of both datasets by employing machine learning algorithms to develop an ensemble model that combines the high-resolution satellite measurements with the global coverage of reanalysis dataset. Specifically, the eXtreme Gradient Boosting (XGBoost) algorithm, a powerful and efficient machine learning technique, is utilized to capture the complex relationships between the variables and produce enhanced temperature profile estimates