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Climate Dynamics

, Volume 53, Issue 12, pp 7461–7477 | Cite as

Multi-model ensemble forecasting of North Atlantic tropical cyclone activity

  • Gabriele VillariniEmail author
  • Beda Luitel
  • Gabriel A. Vecchi
  • Joyee Ghosh
Article

Abstract

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.

Notes

Acknowledgments

The authors thank the NMME program partners and acknowledge the help of NCEP, IRI and NCAR personnel in creating, updating and maintaining the NMME archive, with the support of NOAA, NSF, NASA and DOE. The first three authors acknowledge funding from the National Science Foundation under Grant No. AGS-1262099, and Award NA14OAR4830101 from the National Oceanic and Atmospheric Administration, U.S. Department of Commerce. Gabriele Villarini also acknowledges financial support from the USACE Institute for Water Resources.

Supplementary material

382_2016_3369_MOESM1_ESM.doc (35.3 mb)
Supplementary material 1 (DOC 36148 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.306 C. Maxwell Stanley Hydraulics Laboratory, IIHR-Hydroscience & EngineeringThe University of IowaIowa CityUSA
  2. 2.NOAA/Geophysical Fluid Dynamics LaboratoryPrincetonUSA
  3. 3.Department of Statistics and Actuarial ScienceThe University of IowaIowa CityUSA

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