Abstract
This study explores the extended form of methodologies for trend detection in meteorological time series, focusing on one of the critical climatic variables, Humidity, which is collected from Silchar station of Assam, India. To assess the presence and magnitude of trends, a comprehensive approach is adopted, employing the Mann-Kendall (M-K) test, its modified version, and Sen’s slope estimation, alongside the Innovative Trend Analysis (ITA) tech-nique. These methods together offer a robust framework for detecting and quantifying trends in the dataset. The monthly data of 37 years from 1986-2022 at 3 pm and at12 pm has been processed to find out the variability of humidity for which M-K test, modified M-K test and ITA method with trend magnitude and slopes is adopted to check the seasonal variation of humidity at 3 pm and 12 pm. Based on the statistical outcomes derived from the three trend detection methods and their corresponding visualizations, the analysis reveals both increasing and decreasing trends in humidity across different seasons includes a significant trend in humidity measurements has been identified during the winter, monsoon, and postmonsoon seasons at 3 PM, with a corresponding pattern similarly apparent in the monsoon season 12 PM observations. These variations are observed in the humidity values recorded at both 3 pm and 12 pm, highlighting a seasonal divergence in humidity pat-terns, with certain periods exhibiting rising rates, while others demonstrate declining or no trends.
Published Version
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