Statistical analysis of Precipitable Water Vapor and rainfall variability in different geographical conditions of the Indian region
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Aaryan Kulkarni
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Devanaboyina Venkata Ratnam
Abstract
Monitoring Precipitable Water Vapor (PWV) with Global Navigation Satellite System (GNSS) receivers is becoming a more viable and effective solution for meteorological applications such as rainfall prediction, climate change, and weather forecasting etc. This study investigates the statistical relationship between GNSS-derived Precipitable Water Vapor (PWV) and rainfall variability across five Indian cities, Bangalore, Hyderabad, Guwahati, Thiruvananthapuram, and Madurai, representing diverse geographic and climatic zones. GNSS-derived PWV data from the GPS Aided Geo Augmented Navigation (GAGAN) network and rainfall data from the Indian Meteorological Department (IMD) were considered from March 2013 to February 2014. Data has been analyzed based on seasonal trends and the dynamic interaction between atmospheric moisture and precipitation. The results represented significant spatial and temporal differences in rainfall patterns, with Thiruvananthapuram recording the highest monthly rainfall (143.90 mm in October 2013) due to dual monsoon exposure, and Madurai the lowest (88.5 mm in October 2013) owing to its rain-shadow location. Guwahati shows strong orographic influence and early monsoon peaks (121.30 mm in June 2013), while inland cities like Bangalore and Hyderabad experience delayed rainfall peaks during post-monsoon periods. Average monthly rainfall and PWV values were found to be closely linked to geographical factors such as elevation, topography, and proximity to coastlines. A clear correlation was observed between PWV and subsequent rainfall events; sharp increases in PWV exceeding 30 mm daily or rising at rates above 0.5 mm/h often preceded heavy precipitation.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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