Until today there is no comprehensive theory for formation of tropical cyclones (hurricanes, typhoons). Therefore, it is common to use statistical methods to derive empirical indices as proxies for the probability for genesis. There are also different types of genesis pathways that have been explored in ad-hoc manner. I would like to explore the possibility of using machine learning to explore tropical cyclone genesis, in particular the different pathways in a more comprehensive manner.

This project is eligible for a matching fund stipend from the Data Science Institute. This not a guarantee of payment and the total amount is subject to available funding.

Faculty Advisor

  • Professor: Suzana Camargo
  • Department/School: Lamont-Doherty Earth Observatory
  • My expertise is on extreme weather and climate events, in particular tropical cyclones (hurricanes and typhoons), and how they relate to climate variability and change.

Project Timeline

  • Earliest starting date: 10/2/2020
  • End date: 8/31/2021
  • Number of hours per week of research expected during Fall 2020: ~10

Candidate requirements

  • Skill sets: Programming (python or Matlab), machine learning methods
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: Yes
  • Additional comments: Remote work as long as the pandemic is ongoing