Detection of Long-Term Climatic Patterns in Polar Weather Using Time Series Analysis

This paper presents a comprehensive, data-driven framework for the analysis and visualization of long-term polar meteorological time series, utilizing multi-year observational datasets from the Indian Antarctic research stations Maitri (2010–2016) and Bharati (2015–2022). The proposed framework integrates advanced preprocessing, statistical analysis, and visualization techniques to explore temporal trends, seasonal and diurnal variations, and interrelationships among key atmospheric variables, including temperature, wind speed and direction, relative humidity, and air pressure. The time series analysis reveals distinct seasonal patterns and inter-annual variability, with detailed findings from Maitri highlighting consistent diurnal structures. Notably, a persistent positive correlation between temperature and atmospheric pressure during the austral summer months was observed, suggesting complex, season-dependent interactions within the Antarctic climate system. These insights were derived through correlation analysis, time-series decomposition, and anomaly detection methods. The results offer critical contributions to polar meteorology by improving the understanding of atmospheric behavior in polar environments. Furthermore, the study contributes to ongoing research on climate variability and change in polar regions—areas that serve as early indicators of global climate dynamics.