UT Dallas Engineers Model Tropical Storms with Machine Learning

Researchers led by Dr. Kianoosh Yousefi at The University of Texas at Dallas are developing a machine learning model to improve predictions of tropical storm intensity and evolution. The research, funded by a three-year Office of Naval Research Young Investigator Program award of up to $742,345, focuses on the poorly understood impact of sea spray – specifically spume droplets – on these meteorological phenomena. The study aims to address current limitations in accurately measuring spray concentrations, a key factor influencing hurricane behaviour.

Engineers are developing a machine learning model to improve tropical storm predictions, focusing specifically on the factors influencing hurricane intensity and evolution. Dr. Kianoosh Yousefi, assistant professor of mechanical engineering at The University of Texas at Dallas, leads the research effort. The project investigates the impact of sea spray, particularly spume – defined as foam droplets – on these meteorological phenomena, an area currently hampered by challenges in accurately measuring spray concentrations.

The research receives support from a grant awarded by the Office of Naval Research 2025 Young Investigator Program. This award provides up to $742,345 in funding over a three-year period, enabling detailed investigation into the role of sea spray in hurricane intensity prediction. The difficulty in quantifying spray concentrations remains a key impediment to fully understanding its effects on storm development.