Comparative analysis of cloud properties over drought- and flood-prone regions of western India using machine learning techniques

ABSTRACT Cloud properties are pivotal in analyzing rainfall patterns in monsoon-dependent countries such as India. The impact of climate change becomes more important in regions susceptible to hydrometeorological events due to different monsoon regimes. To examine regional heterogeneity of cloud properties, this study investigates long-term trends and predictive capabilities for cloud properties in drought- and flood-prone regions of western India, utilizing satellite data and employing machine learning (ML) models to comprehend intricate data patterns and enhance predictive accuracy. The results show higher mean and variability in cloud parameters over the flood-prone area due to favorable rain conditions, reflecting higher cloud microphysical and optical properties. These parameters negatively correlate with some cloud macrophysical properties and aerosol property in the drought-prone area. A moderate correlation exists between certain cloud characteristics of one region and another. Employing ML for regression analysis and comparing them for cloud effective radius across regions shows promising results, with random forest demonstrating high coefficient of determination (0.86, 0.93) and low root mean squared error (0.76, 1.15) due to its robustness and high accuracy. This research enhances the understanding of regional heterogeneity in India and shows that ML can help in predicting future cloud dynamics and climate by suitable model.