A statistical/dynamical model for North Atlantic seasonal hurricane prediction

Colorado State University (CSU) has been issuing seasonal hurricane forecasts since 1984, with statistical modeling techniques primarily underpinning these outlooks. CSU has recently begun issuing statistical/dynamical forecasts, using the SEAS5 forecast system from the European Centre for Medium‐Ra...

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Bibliographic Details
Published in:Geophysical Research Letters
Main Authors: Klotzbach, Philip, Caron, Louis-Philippe, Bell, Michael
Other Authors: Barcelona Supercomputing Center
Format: Article in Journal/Newspaper
Language:English
Published: American Geophysical Union 2020
Subjects:
Àrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia
Climatology
Cyclone forecasting
Seasonal climate forecasting
Hurricane
Tropical cyclone
Statistical modeling
Climate modeling
Seasonal forecasting
Climatologia
Ciclons
Huracans
North Atlantic
Online Access:http://hdl.handle.net/2117/330979
https://doi.org/10.1029/2020GL089357
Description
Summary:Colorado State University (CSU) has been issuing seasonal hurricane forecasts since 1984, with statistical modeling techniques primarily underpinning these outlooks. CSU has recently begun issuing statistical/dynamical forecasts, using the SEAS5 forecast system from the European Centre for Medium‐Range Weather Forecasts to forecast the three predictors that currently comprise CSU's early August statistical forecast model. SEAS5 shows skill at forecasting all three of these July predictors from an initialization as early as 1 March. The SEAS5 model forecasts for the three parameters are then regressed against seasonal accumulated cyclone energy. The model has a cross‐validated correlation skill of r = 0.60 with accumulated cyclone energy for a 1 March initialization, improving to r = 0.67 for a 1 June initialization over the period from 1982–2019. The combination of the statistical/dynamical model with the currently existing statistical models shows improved skill over either model individually for the April, June, and July outlooks. Phil Klotzbach would like to acknowledge grants from the Severo Ochoa Mobility Program and the G. Unger Vetlesen Foundation. Michael Bell was funded by the Office of Naval Research award N000141613033. Peer Reviewed Postprint (published version)

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