Advancements in Hurricane Prediction With NOAA's Next‐Generation Forecast System. Issue 8 (24th April 2019)

• Record Type: Journal Article
• Authors: Chen, Jan‐Huey; Lin, Shian‐Jiann; Magnusson, Linus; Bender, Morris; Chen, Xi; Zhou, Linjiong; Xiang, Baoqiang; Rees, Shannon; Morin, Matthew; Harris, Lucas
• Is Part Of: Geophysical research letters. Volume 46:Issue 8(2019)
• Page Start: 4495
• Page End: 4501
• Publication Date: 2019-04-24
• Subjects: Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2019GL082410
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: 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. Plain Language Summary: When using European Centre for Medium‐Range Weather Forecasts initial conditions, a new global weather model built at NOAA's Geophysical Fluid Dynamics Laboratory produces better hurricane forecast skill than the world‐leading European model. Key Points: The GFDL fvGFS model shows superior hurricane track forecasts than the world‐leading ECMWF model when using ECMWF's initial condition The poor tropical cyclone intensity forecasts shown in both the operational GFS and the ECWMF model are significantly improved in the fvGFS