Joanne Camp

Areas of expertise

  • Tropical cyclones
  • Seasonal tropical cyclone prediction
  • Sub-seasonal tropical cyclone prediction

My Publications - Camp, J

Current activities

Joanne's work focuses on the use of dynamical models to provide seasonal predictions of tropical cyclone activity for the public and business communities. Since 2008 she has led the development of seasonal tropical prediction products – forecasts of the number of named storms, number of hurricanes/typhoons and accumulated cyclone energy (ACE) index – for all ocean basins around the world using ensemble predictions from the Met Office seasonal forecast system.

Her latest research has focussed on tropical cyclone landfall variability and predictability for the United States, Caribbean and Southeast Asia. Her research has shown that the Met Office seasonal forecast system exhibits significant skill for predictions of tropical cyclone landfall risk in the Caribbean. In particular, the system predicted the enhanced frequency of observed tropical cyclone tracks across the northeast Caribbean during the extremely active 2017 Atlantic hurricane season.

Joanne also provides research as part of the Climate Science for Services Partnership (CSSP-China) project, which brings together researchers from the Met Office, the wider UK academic community and China. Here Joanne’s research has demonstrated significant skill for seasonal predictions of tropical cyclone landfall risk in Southeast Asia.
Joanne has also recently undertaken a 6-month secondment to the Bureau of Meteorology, Australia, to enhance the sub-seasonal tropical cyclone forecasting capability in the southern hemisphere.

Career background

Joanne has been a member of the Monthly to Decadal Prediction Group in the Met Office Hadley Centre since 2008 after graduating with a first-class honour’s degree in Meteorology from the University of Reading.

External recognition

Joanne is a member of the World Meteorological Organization (WMO) international working groups on seasonal and sub-seasonal tropical cyclone forecasting.