Research at STORM Lab
At STORM Lab, we tackle one of the most pressing scientific challenges of our time: improving the prediction and understanding of weather, climate, and energy systems in a rapidly changing environment.
Our research brings together numerical modelling, observations, remote sensing, and artificial intelligence to decode complex atmospheric processes and translate them into actionable insights. We focus particularly on extreme events such as heavy rainfall and heat waves, where accurate prediction can significantly impact disaster preparedness and societal resilience.
A defining strength of our work is the integration of:
Physics-based models (WRF and related frameworks)
Multi-source observations (radar, satellite, and ground data)
Data-driven and machine learning approaches
This fusion allows us to move beyond traditional methods and develop next-generation forecasting and analysis systems that are both physically consistent and data-adaptive.
Our research is organized into three core thrust areas:
Understanding the fundamental processes governing heat transfer, energy balance, and atmospheric dynamics, with applications ranging from climate systems to sustainable energy.
Leveraging satellite and radar observations to quantify precipitation processes, improve rainfall estimation, and bridge observational gaps in weather monitoring.
Developing intelligent, physics-informed models that enhance prediction accuracy, enable data assimilation, and uncover patterns in complex environmental datasets.
🚀 Explore each research theme to discover our projects, methodologies, and key contributions.