Research Projects

This research aims to investigate the role of Mesoscale Convective Systems (MCSs) in the Indian monsoon season, focusing on how land-atmosphere interactions influence their formation, evolution, and intensity. The Indian monsoon is complex, impacted by various atmospheric and land surface processes, and recent climate changes and land-use alterations add uncertainty to its predictability. Further, MCSs are organized clusters of thunderstorms responsible for intense, short-duration rainfall spells, contributing significantly (50-60%) to the total monsoonal rainfall in India. Thus, understanding MCS dynamics is vital for improving rainfall forecasts. MCS variability spans from diurnal to seasonal timescales and is influenced by factors such as soil moisture, boundary layer dynamics, and large-scale climate drivers like El Niño and the Indian Ocean Dipole. Increasing trends in MCS frequency and intensity, partly due to climate change and anthropogenic factors, have increased extreme rainfall events, posing challenges for forecasting and climate resilience planning. This study seeks to improve MCS simulations in models, which currently have low skills in forecasting extreme precipitation. The study's main objectives include- first analysing MCS dynamics, examining aspects such as spatial structure, lifespan, rainfall rates, and atmospheric profiles. Second, quantifying land-atmosphere coupling mechanisms that influence MCSs, particularly the effects of soil moisture, surface energy and moisture fluxes, and planetary boundary layer; and further investigating moisture transport sources and moisture recycling during MCS activity. Finally, we aim to improve model skills for MCS forecasts by recommending best fit for land surface schemes. The hypothesis is that land-atmosphere interactions significantly affect MCS initiation, lifecycle, and intensity, with local moisture recycling playing a key role in MCS formation. To test this, we will use observational data, reanalysis datasets, and the Weather Research and Forecasting (WRF) model, coupled with a land-surface model (CLM or NOAH). High-resolution simulations and sensitivity experiments will use different land-surface conditions and surface parameterization schemes. Expected outcomes include quantifying how land-atmosphere feedbacks influence MCS characteristics, contributions from moisture transport versus local moisture recycling components, and improving model accuracy for MCS forecasts. This research contributes to fundamental meteorological knowledge and has practical applications in disaster preparedness and water management. By advancing understanding of MCS dynamics and land-atmosphere interactions, the study supports improved resilience strategies and highlights the need for collaborative efforts among observation scientists, meteorologists and hydrologists to accelerate model development and field campaign studies in inter-disciplinary projects.