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Advancements in Hurricane Prediction With NOAA's Next‐Generation Forecast System
Author(s) -
Chen JanHuey,
Lin ShianJiann,
Magnusson Linus,
Bender Morris,
Chen Xi,
Zhou Linjiong,
Xiang Baoqiang,
Rees Shan,
Morin Matthew,
Harris Lucas
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 - 10.1029/2019gl082410
Subject(s) - meteorology , climatology , tropical cyclone forecast model , tropical cyclone , global forecast system , atlantic hurricane , environmental science , numerical weather prediction , track (disk drive) , data assimilation , robustness (evolution) , north american mesoscale model , computer science , geology , geography , biochemistry , chemistry , gene , operating system
We use the fvGFS model developed at the Geophysical Fluid Dynamics Laboratory to demonstrate the potential of the upcoming United States Next‐Generation Global Prediction System for hurricane prediction. The fvGFS retrospective forecasts initialized with the European Centre for Medium‐Range Weather Forecasts (ECMWF) data showed much‐improved track forecasts for the 2017 Atlantic hurricane season compared to the best‐performing ECMWF operational model. The fvGFS greatly improved the ECMWF's poor track forecast for Hurricane Maria (2017). For Hurricane Irma (2017), a well‐predicted case by the ECMWF model, the fvGFS produced even lower five‐day track forecast errors. The fvGFS also showed better intensity prediction than both the United States and the ECMWF operational models, indicating the robustness of its numerical algorithms.