Ongoing Research Projects

1. Hybrid AI–WRF Rainfall Prediction System

We are developing a hybrid rainfall forecasting framework by integrating the WRF model with advanced machine learning techniques. This approach combines physics-based numerical modelling with data-driven corrections to improve prediction accuracy, especially for extreme rainfall events over data-sparse regions.

Key Features

• Integration of numerical weather prediction with machine learning

• Data assimilation-driven model improvement

• Focus on extreme rainfall and convective events

Tools & Data

• WRF Model

• ERA5 / IMD / IMDAA datasets

• Python and MATLAB

Expected Outcomes

• Improved rainfall forecast accuracy

• Better prediction of extreme weather events

• Scalable framework for regional forecasting applications

2. Remote Sensing-Based Extreme Weather Monitoring

This project focuses on the use of satellite observations to detect and analyze extreme weather events such as severe storms, heavy rainfall, and convective systems. The goal is to improve early detection and enhance understanding of storm dynamics.

Key Features

• Satellite-based monitoring of convective systems

• Identification of precursors to extreme weather events

• Integration with ground-based observations

Tools & Data

• INSAT / MODIS / TRMM / GPM datasets

• GIS and remote sensing tools

• Image processing and analysis techniques

Expected Outcomes

• Enhanced early warning capabilities

• Better understanding of storm evolution

• Improved disaster preparedness

3. Energy and Entropy Analysis of Atmospheric Processes

This project explores the thermodynamic aspects of atmospheric systems by analyzing energy transfer and entropy generation. The aim is to quantify the efficiency and irreversibility of atmospheric processes associated with weather systems.

Key Features

• Estimation of energy fluxes and entropy generation

• Analysis of heat exchange processes (sensible, latent, radiative)

• Application to extreme weather events

Tools & Data

• ERA5 / IMDAA datasets

• Thermodynamic analysis frameworks

• MATLAB / Python

Expected Outcomes

• Quantitative understanding of atmospheric energetics

• Insights into system efficiency and dissipation

• Novel metrics for comparing weather events

Future Directions

• AI-driven weather and climate modelling

• High-resolution urban climate simulations

• Coupled atmosphere–land–aerosol interaction studies

• Real-time forecasting and decision-support systems

Collaborations

We actively welcome collaborations with researchers, institutions, and industry partners working in:

• Climate science and atmospheric physics

• Remote sensing and data science

• Numerical modelling and AI applications

For collaboration inquiries, please visit the Contact page.