Treffer: Spatial and Temporal Relevance of Indian Rain Index to Urban Landscapes: An Evaluation Using Dynamic Time Warping and Correlation Techniques.

This study delves into the Indian Rain Index (IRI) in 150 cities in India, utilizing an innovative blend of Dynamic Time Warping (DTW) and correlation analysis methods. By utilizing data from National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications (NASA's MERRA-2) database from 1984 to 2023, the study investigates the temporal and magnitude correlations of local rainfall patterns with IRI. The goal is to assess IRI's relevance in various urban settings and suggest improvements to enhance its usefulness. Integrating advanced computational processes, statistical analysis, and geospatial visualization techniques, the study employs Python for data retrieval and processing, and R for DTW analysis. Furthermore, the research categorizes cities into four groups based on their DTW distances and correlation measures. The results suggest that although IRI is suitable for gauging weather-related risk in specific cities, it falls short in many areas, revealing a disparity between national and local rainfall patterns. This paper adds to the conversation on improving the accuracy of rainfall indices by merging traditional meteorological practices with innovative computing methods. Additionally, the study explores the implications of IRI for financial risk management, particularly in the context of weather derivatives. It emphasizes the necessity for a more comprehensive and geographically-specific index that considers a wider range of weather indicators. By adopting a multidisciplinary approach, this study offers valuable perspectives for urban planners, policymakers, and environmental scientists in establishing tailored approaches for mitigating weather-related risks and effectively managing urban environments. [ABSTRACT FROM AUTHOR]

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