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  Spatiotemporal dynamics of moisture influx and their role in precipitation extremes: A study of December 2023 in Kayalpattinam

Ganapathiraju, S. A., Kanth, U., V., M. S. L., Rathinasamy, M., Kumar, K. N., Vallejo-Bernal, S. M., Marwan, N., Kaur, S. (2026): Spatiotemporal dynamics of moisture influx and their role in precipitation extremes: A study of December 2023 in Kayalpattinam. - Atmospheric Research, 331, 108680.
https://doi.org/10.1016/j.atmosres.2025.108680

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https://zenodo.org/records/15235584 (Supplementary material)
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 Creators:
Ganapathiraju, Sree Anusha1, Author
Kanth, Utkarsh2, Author
V., Madhuri Sai Latha2, Author
Rathinasamy, Maheswaran1, Author
Kumar, Kondapalli Niranjan2, Author
Vallejo-Bernal, Sara M.1, Author
Marwan, Norbert1, Author                 
Kaur, Sukhwinder2, Author
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: In 2023, the coastal town of Kayalpattinam in Tamil Nadu recorded an extraordinary rainfall exceeding 950 mm on December 17 and 18, resulting in severe flash floods and devastation to livelihoods in the community. This study critically examines the physical mechanisms driving this event across scales. Employing regional reanalysis datasets, we elucidate the localized characteristics responsible for extreme precipitation and systematically address the associated uncertainties. The investigation revealed that the spatiotemporal dynamics of moisture transport played a vital role in the increased moisture influx over the region. In particular, the local convection combined with heightened atmospheric instability and intensified advection in the surrounding areas played a pivotal role in the formation of significant mid-tropospheric cyclones. These developed atmospheric phenomena are rarely observed in this region, which typically experiences tropical cyclones and depressions more frequently. This study emphasizes the necessity of conducting meticulous investigations to improve risk assessments and preparedness for future climatological phenomena of similar magnitude.

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Language(s): eng - English
 Dates: 2025-12-042025-12-172026-02-01
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: MDB-ID: No MDB - stored outside PIK (see locators/paper)
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Working Group: Development of advanced time series analysis techniques
Research topic keyword: Atmosphere
Research topic keyword: Extremes
Research topic keyword: Monsoon
Research topic keyword: Weather
Regional keyword: Asia
Model / method: Quantitative Methods
Model / method: Nonlinear Data Analysis
DOI: 10.1016/j.atmosres.2025.108680
 Degree: -

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Title: Atmospheric Research
Source Genre: Journal, SCI, Scopus, p3
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Publ. Info: -
Pages: - Volume / Issue: 331 Sequence Number: 108680 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1501191
Publisher: Springer