ABSTRACT
Cloud properties are pivotal in analyzing rainfall patterns globally, especially 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-prone and flood-prone regions of western India, utilizing satellite data and employing various 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 along with the aerosol property in the drought-prone area. Additionally, a moderate correlation exists between certain cloud characteristics of one region and another. Employing ML algorithms for regression analysis and comparing them for cloud effective radius across regions shows promising results, with random forest (RF) 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 be helpful in predicting future cloud dynamics and climate variables identifying the most suitable model.
HIGHLIGHTS
The correlation among cloud properties of drought- and flood-prone regions showed significant mutual dependence.
Higher mean and variability were observed for most of the cloud variables in the flood-prone area than in the drought-prone region.
Among all the utilized ML models, RF performed the best for the flood-prone and the drought-prone regions.
Using ML techniques, it is observed that cloud top temperature is the most influential parameter for predicting cloud effective particle radius.