Diurnal Spatiotemporal Analysis of TRMM and GPM Satellite Data on Indian Region

As an agrarian nation heavily dependent on rainfall, understanding precipitation patterns is crucial for India’s agriculture, water resource management, and climate resilience. Satellite missions like the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) have provided access to global precipitation data. However, their application to diurnal or hourly rainfall analysis, especially for India, remains underutilized. This research explores the capabilities of TRMM and GPM daily data to fill a critical gap in the diurnal analysis of precipitation trends, using advanced non-parametric methods like the Mann-Kendall test and Sen's slope estimator. These techniques are well-suited for handling the unique challenges posed by satellite-derived datasets, such as non- normal distributions. By applying these methods, the study aims to find significant trends in key precipitation indices, like Maximum 1-day precipitation and offering deeper insights into India’s climate variability. The results also demonstrate the compatibility of these methods with remote sensing data, positioning them as robust tools for enhancing climate models and supporting data-driven climate adaptation strategies.