Seasonal Prediction of North Atlantic Accumulated Cyclone Energy and Major Hurricane Activity

Building upon our previous seasonal hurricane prediction model, here we develop two statistical models to predict the number of major hurricanes (MHs) and accumulated cyclone energy (ACE) in the North Atlantic basin using monthly data from March to May for an early June forecast. The input data incl...

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Published in:Weather and Forecasting
Main Authors: Davis, Kyle, Zeng, Xubin
Other Authors: Univ Arizona, Dept Hydrol & Atmospher Sci
Format: Article in Journal/Newspaper
Language:English
Published: AMER METEOROLOGICAL SOC 2019
Subjects:
Online Access:http://hdl.handle.net/10150/632896
https://doi.org/10.1175/WAF-D-18-0125.1
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author Davis, Kyle
Zeng, Xubin
author2 Univ Arizona, Dept Hydrol & Atmospher Sci
author_facet Davis, Kyle
Zeng, Xubin
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collection The University of Arizona: UA Campus Repository
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description Building upon our previous seasonal hurricane prediction model, here we develop two statistical models to predict the number of major hurricanes (MHs) and accumulated cyclone energy (ACE) in the North Atlantic basin using monthly data from March to May for an early June forecast. The input data include zonal pseudo-wind stress to the 3/2 power, sea surface temperature in the North Atlantic, and, depending on the magnitude of the Atlantic multidecadal oscillation index, the multivariate ENSO index. From 1968 to 2017, these models have a mean absolute error of 0.96 storms for MHs and 30 units for ACE. When tested over an independent period from 1958 to 1967, the models show a 22% improvement for MHs and 16% for ACE over a no-skill metric based on a 5-yr running average. Both the MH and ACE results show consistent improvements over those produced by three other centers using statistical-dynamical hybrid models and a 5-yr running average prediction over the period 2000-17 for MHs (2003-17 for ACE) in a simulated real-time prediction. These improvements vary from 25% to 37% for MHs and from 15% to 37% for ACE. While most forecasting centers called for a slightly above-average hurricane season in May/June 2017, our models predicted in June 2017 a very active season, in much better agreement with observations. Agnese Nelms Haury Program in Environment and Social Justice; NASA MAP program [NNX14AM02G] 6 month embargo; published online: 11 February 2019 This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
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spelling ftunivarizona:oai:repository.arizona.edu:10150/632896 2025-06-08T14:04:59+00:00 Seasonal Prediction of North Atlantic Accumulated Cyclone Energy and Major Hurricane Activity Davis, Kyle Zeng, Xubin Univ Arizona, Dept Hydrol & Atmospher Sci 2019-02 http://hdl.handle.net/10150/632896 https://doi.org/10.1175/WAF-D-18-0125.1 en eng AMER METEOROLOGICAL SOC http://journals.ametsoc.org/doi/10.1175/WAF-D-18-0125.1 http://hdl.handle.net/10150/632896 WEATHER AND FORECASTING © 2019 American Meteorological Society. http://rightsstatements.org/vocab/InC/1.0/ Weather and Forecasting 34 1 221 232 Hurricanes/typhoons Seasonal forecasting Statistical forecasting Article 2019 ftunivarizona https://doi.org/10.1175/WAF-D-18-0125.1 2025-05-15T04:39:26Z Building upon our previous seasonal hurricane prediction model, here we develop two statistical models to predict the number of major hurricanes (MHs) and accumulated cyclone energy (ACE) in the North Atlantic basin using monthly data from March to May for an early June forecast. The input data include zonal pseudo-wind stress to the 3/2 power, sea surface temperature in the North Atlantic, and, depending on the magnitude of the Atlantic multidecadal oscillation index, the multivariate ENSO index. From 1968 to 2017, these models have a mean absolute error of 0.96 storms for MHs and 30 units for ACE. When tested over an independent period from 1958 to 1967, the models show a 22% improvement for MHs and 16% for ACE over a no-skill metric based on a 5-yr running average. Both the MH and ACE results show consistent improvements over those produced by three other centers using statistical-dynamical hybrid models and a 5-yr running average prediction over the period 2000-17 for MHs (2003-17 for ACE) in a simulated real-time prediction. These improvements vary from 25% to 37% for MHs and from 15% to 37% for ACE. While most forecasting centers called for a slightly above-average hurricane season in May/June 2017, our models predicted in June 2017 a very active season, in much better agreement with observations. Agnese Nelms Haury Program in Environment and Social Justice; NASA MAP program [NNX14AM02G] 6 month embargo; published online: 11 February 2019 This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu. Article in Journal/Newspaper North Atlantic The University of Arizona: UA Campus Repository Agnese ENVELOPE(-58.527,-58.527,-61.967,-61.967) Weather and Forecasting 34 1 221 232
spellingShingle Hurricanes/typhoons
Seasonal forecasting
Statistical forecasting
Davis, Kyle
Zeng, Xubin
Seasonal Prediction of North Atlantic Accumulated Cyclone Energy and Major Hurricane Activity
title Seasonal Prediction of North Atlantic Accumulated Cyclone Energy and Major Hurricane Activity
title_full Seasonal Prediction of North Atlantic Accumulated Cyclone Energy and Major Hurricane Activity
title_fullStr Seasonal Prediction of North Atlantic Accumulated Cyclone Energy and Major Hurricane Activity
title_full_unstemmed Seasonal Prediction of North Atlantic Accumulated Cyclone Energy and Major Hurricane Activity
title_short Seasonal Prediction of North Atlantic Accumulated Cyclone Energy and Major Hurricane Activity
title_sort seasonal prediction of north atlantic accumulated cyclone energy and major hurricane activity
topic Hurricanes/typhoons
Seasonal forecasting
Statistical forecasting
topic_facet Hurricanes/typhoons
Seasonal forecasting
Statistical forecasting
url http://hdl.handle.net/10150/632896
https://doi.org/10.1175/WAF-D-18-0125.1