Seasonal prediction of North Atlantic accumulated cyclone energy and major hurricane activity

Kyle Davis, Xubin Zeng

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)221-232
Number of pages12
JournalWeather and Forecasting
Volume34
Issue number1
DOIs
StatePublished - Feb 1 2019

Fingerprint

cyclone
hurricane
prediction
energy
Atlantic Multidecadal Oscillation
zonal wind
wind stress
El Nino-Southern Oscillation
sea surface temperature
basin

Keywords

  • Hurricanes/typhoons
  • Seasonal forecasting
  • Statistical forecasting

ASJC Scopus subject areas

  • Atmospheric Science

Cite this

Seasonal prediction of North Atlantic accumulated cyclone energy and major hurricane activity. / Davis, Kyle; Zeng, Xubin.

In: Weather and Forecasting, Vol. 34, No. 1, 01.02.2019, p. 221-232.

Research output: Contribution to journalArticle

@article{e51d554f222f4d6bae22a11c81653e7a,
title = "Seasonal prediction of North Atlantic accumulated cyclone energy and major hurricane activity",
abstract = "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.",
keywords = "Hurricanes/typhoons, Seasonal forecasting, Statistical forecasting",
author = "Kyle Davis and Xubin Zeng",
year = "2019",
month = "2",
day = "1",
doi = "10.1175/WAF-D-18-0125.1",
language = "English (US)",
volume = "34",
pages = "221--232",
journal = "Weather and Forecasting",
issn = "0882-8156",
publisher = "American Meteorological Society",
number = "1",

}

TY - JOUR

T1 - Seasonal prediction of North Atlantic accumulated cyclone energy and major hurricane activity

AU - Davis, Kyle

AU - Zeng, Xubin

PY - 2019/2/1

Y1 - 2019/2/1

N2 - 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.

AB - 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.

KW - Hurricanes/typhoons

KW - Seasonal forecasting

KW - Statistical forecasting

UR - http://www.scopus.com/inward/record.url?scp=85063581421&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85063581421&partnerID=8YFLogxK

U2 - 10.1175/WAF-D-18-0125.1

DO - 10.1175/WAF-D-18-0125.1

M3 - Article

VL - 34

SP - 221

EP - 232

JO - Weather and Forecasting

JF - Weather and Forecasting

SN - 0882-8156

IS - 1

ER -