Very short-term weather prediction is essential for effective reduction of impacts of severe convective weather and proved valuable for sectors such as aviation, wind energy and shipping.
The need for very fast computations and the use of recent and remote sensing data makes nowcasting challenging for traditional Numerical Weather Prediction models. Recent advances in scientific machine learning (SciML) provide a new tool for weather prediction which, coupled with numerical methods, perfectly suits nowcasting needs.
My fascination with weather and meteorology started in high school and led me to my Master's degree in Meteorology and Oceanography.
During my undergraduate studies, I used numerical models (such as WRF) to study the interactions of thunderstorms with land and developed my own model to research the development of convective clouds. I am also a member of the forecasting team at the severe convective weather prediction group operating in Poland.
- Development and morphology of convective clouds
- Severe convective weather forecasting
- Numerical weather prediction
- Applications of machine learning in weather prediction
- MSci, Meteorology and Oceanography, University of East Anglia
Research groups and institutes
- Institute for Climate and Atmospheric Science
- Atmospheric and Cloud Dynamics