Research Projects

India has been experiencing a three-fold increase in extreme rainfall events since 1950, a trend predicted to continue on a warming planet. However, the physical mechanisms behind climate change's alteration of local and regional precipitation extremes in India remain incomplete. The role of changes in atmospheric circulation needs to be discussed. Convolutional neural network (CNN) is an advanced machine learning tool that can study large-scale atmospheric circulation patterns associated with extreme precipitation. CNN analysis can identify precipitation extreme days over India with high statistical confidence using daily mean sea-level pressure, 500-hPa geopotential height, and vertical motion of air anomalies from the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5). The India Meteorological Department (IMD) observed daily precipitation data can be used to train the CNN to identify extreme precipitation circulation patterns (EPCP). CNN analysis can be used to investigate future projections of extreme weather events over India. The Weather Research and Forecasting (WRF) Model simulated downscaled data over the Indian region with lateral boundary conditions from Shared Socioeconomic Pathways scenarios of Coupled Model Intercomparison Project Phase 6 (CMIP6) can provide more robust results for future projections. Understanding the future projection of atmospheric circulation patterns responsible for extreme precipitation events is crucial as extreme weather phenomena have significant impacts on civil safety and the economy.