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Skillful Prediction of Monthly Major Hurricane Activity in the North Atlantic with Two‐way Nesting
Author(s) -
Gao Kun,
Chen JanHuey,
Harris Lucas,
Sun Yongqiang,
Lin ShianJiann
Publication year - 2019
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2019gl083526
Subject(s) - tropical cyclone , climatology , atlantic hurricane , initialization , environmental science , meteorology , sea surface temperature , nesting (process) , geology , geography , materials science , computer science , metallurgy , programming language
We investigate the monthly prediction of North Atlantic hurricane and especially major hurricane activity based on the Geophysical Fluid Dynamics Laboratory High‐Resolution Atmospheric Model (HiRAM). We compare the performance of two grid configurations: a globally uniform 25‐km grid and the other with an 8‐km interactive nest over the tropical North Atlantic. Both grid configurations show skills in predicting anomalous monthly hurricane frequency and accumulated cyclone energy. Particularly, the 8‐km nested model shows improved skills in predicting major hurricane frequency and accumulated cyclone energy. The skill in anomalous monthly hurricane occurrence prediction arises from the accurate prediction of zonal wind shear anomalies in the Main Development Region, which in turn arises from the sea surface temperature anomalies persisted from the initialization time. The enhanced resolution on the nested grid permits a better representation of hurricanes and especially intense hurricanes, thereby showing the ability and the potential for prediction of major hurricanes on subseasonal timescales.