Correlation-based Weather Modeling: Exploring the Influence of Humidity and Cloud Cover on Temperature Trends

Author(s)	Dr. Anupam Kumar Pandey
Country	India
Abstract	Understanding the relationship between key atmospheric variables and temperature is essential for accurate weather prediction and climate analysis. This study investigates the influence of relative humidity and cloud cover on maximum and minimum temperature trends using correlation-based statistical modeling. Meteorological data spanning multiple years were collected and analyzed to determine how these variables interact and influence temperature behavior across seasonal and daily cycles. Results revealed a strong negative correlation between both humidity and cloud cover with maximum temperature, and a moderate positive correlation with minimum temperature. Multiple regression analysis further confirmed that both variables significantly contribute to temperature variability, with cloud cover having a greater impact during daytime and humidity playing a stronger role at night. These findings highlight the predictive potential of integrating humidity and cloud-related parameters in weather models to enhance forecasting accuracy and support climate-resilient planning.
Keywords	Weather modeling, relative humidity, cloud cover, temperature trends, correlation analysis
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-10-31
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.59464

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