Multi-model ensemble forecasting of North Atlantic tropical cyclone activity

Gabriele Villarini, Beda Luitel, Gabriel A. Vecchi, Joyee Ghosh

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


North Atlantic tropical cyclones (TCs) and hurricanes are responsible for a large number of fatalities and economic damage. Skillful seasonal predictions of the North Atlantic TC activity can provide basic information critical to our improved preparedness. This study focuses on the development of statistical–dynamical seasonal forecasting systems for different quantities related to the frequency and intensity of North Atlantic TCs. These models use only tropical Atlantic and tropical mean sea surface temperatures (SSTs) to describe the variability exhibited by the observational records because they reflect the importance of both local and non-local effects on the genesis and development of TCs in the North Atlantic basin. A set of retrospective forecasts of SSTs by six experimental seasonal-to-interannual prediction systems from the North American Multi-Model Ensemble are used as covariates. The retrospective forecasts are performed over the period 1982–2015. The skill of these statistical–dynamical models is quantified for different quantities (basin-wide number of tropical storms and hurricanes, power dissipation index and accumulated cyclone energy) for forecasts initialized as early as November of the year prior to the season to forecast. The results of this work show that it is possible to obtain skillful retrospective forecasts of North Atlantic TC activity with a long lead time. Moreover, probabilistic forecasts of North Atlantic TC activity for the 2016 season are provided.

Original languageEnglish (US)
Pages (from-to)7461-7477
Number of pages17
JournalClimate Dynamics
Issue number12
StatePublished - Dec 1 2019

All Science Journal Classification (ASJC) codes

  • Atmospheric Science


Dive into the research topics of 'Multi-model ensemble forecasting of North Atlantic tropical cyclone activity'. Together they form a unique fingerprint.

Cite this