<![CDATA[Radiosonde temperature data serve as a cornerstone for understanding atmospheric dynamics and investigating long-term climate trends. Despite their significance, these datasets are often hindered by challenges such as instrumental biases, shifts in observational protocols, and limited vertical resolution, which can obscure critical atmospheric patterns. This study introduces a Python-based automated framework designed to streamline radiosonde data analysis, emphasizing homogenization, vertical resolution enhancement, and advanced visualization techniques. By utilizing robust libraries such as pandas, matplotlib, and seaborn, the framework effectively mitigates inconsistencies and promotes reproducibility. The findings highlight significant improvements in data quality, allowing for more accurate identification of temperature trends across the troposphere and stratosphere. Additionally, this approach reduces analytical biases and enhances the resolution of key atmospheric processes. The proposed framework contributes a valuable methodology for climate researchers, offering new opportunities to advance studies on atmospheric behavior and climate change dynamics.]]>
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