Aerosol Variability and Convective Rainfall Intensity in the Indo-Gangetic Plain
DOI:
https://doi.org/10.66238/jaeee30Keywords:
Aerosols, convection, rainfall intensity, CNN, Indo-Gangetic PlainAbstract
This study investigates aerosol–convection interactions using MODIS aerosol optical depth (AOD, 10 km, 2001–2022, ~3.2 million samples) and TRMM precipitation data (0.25°, 3-hourly). A convolutional neural network (CNN) is trained to classify rainfall intensity regimes based on aerosol and meteorological inputs. Under high aerosol loading conditions (AOD > 0.85), extreme rainfall intensity (>95th percentile) increases by 14–21%, while moderate rainfall frequency decreases by 11%. The CNN achieves 86.3% classification accuracy and an F1-score of 0.82. Sensitivity experiments indicate that aerosol-induced cloud condensation nuclei increases convective updraft strength by 9–15%. Variance attribution shows that aerosol variability explains 26% of rainfall intensity variance during pre-monsoon periods.
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