Uncovering mechanisms behind Chennai's deluges during north-east monsoon season 2015: An observational and modeling analysis

The present study delves into the underlying processes responsible for the Chennai deluge during 9th, 15th of November, and 1st December, 2015 by employing both observational data and modeling approaches. The Chennai rainfall, as observed from the GPM satellite data, was substantially higher than the accumulated normal of ∼79 cm for the October-December period. These extreme events coincided with the strongest El Niño event of the century, which persisted from 2014 to early 2016. Further, it is found that the anomalies in Sea Surface Temperature (SST) during this period were more than 1° K above the climatological value and prevailing strong low-level easterly waves over the Indian Oceanic region aided the intensification of previously developing synoptic systems. Soil moisture analysis indicated saturation values nearing 70 %, resulting in increased surface runoff during rainfall events in the backdrop of rapid urban expansion from 1995 to 2015 and aggravated water logging issues. Calculation of thermodynamic indices revealed favourable conditions for the development and intensification of severe convective systems, leading to the catastrophic rainfall events over the Chennai region. A high resolution regional model NCUM (resolution ∼1.5 Km) was utilized to simulate various synoptic features and dynamics of the event over Chennai. Moisture transport at 700 hPa and integrated precipitable water up to 300 hPa were examined, revealing a strong convergence of moisture along the Chennai coast for all cases, with high values of precipitable water observed. Simulations of 3-hourly accumulated rainfall from model generally align with corresponding GPM satellite estimates, despite the model tending to underestimate the intensity of rainfall in all cases. The model simulated location specific rainfall is reasonably well matched with the in-situ observations around Chennai region. However, the model is underestimated the peak rainfall while compare with the observations in all the cases. Further, it successfully depicts the dynamics and structure of extreme rainfall events, including key features such as wind patterns and moisture convergence, demonstrating its utility for forecasting extreme weather events in the Chennai region.