Performance of 2020 Real-Time Atlantic Hurricane Forecasts from High-Resolution Global-Nested Hurricane Models: HAFS-globalnest and GFDL T-SHiELD

Andrew Hazelton, Kun Gao, Morris Bender, Levi Cowan, Ghassan J. Alaka, Alex Kaltenbaugh, Lew Gramer, Xuejin Zhang, Lucas Harris, Timothy Marchok, Matt Morin, Avichal Mehra, Zhan Zhang, Bin Liu, Frank Marks

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The global-nested Hurricane Analysis and Forecast System (HAFS-globalnest) is one piece of NOAA’s Unified Forecast System (UFS) application for hurricanes. In this study, results are analyzed from 2020 real-time forecasts by HAFS-globalnest and a similar global-nested model, the Tropical Atlantic version of GFDL’s System for High-resolution prediction on Earth-to-Local Domains (T-SHiELD). HAFS-globalnest produced the highest track forecast skill compared to several operational and experimental models, while T-SHiELD showed promising track skills as well. The intensity forecasts from HAFS-globalnest generally had a positive bias at longer lead times primarily due to the lack of ocean coupling, while T-SHiELD had a much smaller intensity bias particularly at longer forecast lead times. With the introduction of a modified planetary boundary layer scheme and an increased number of vertical levels, particularly in the boundary layer, HAFS forecasts of storm size had a smaller positive bias than occurred in the 2019 version of HAFS-globalnest. Despite track forecasts that were comparable to the operational GFS and HWRF, both HAFS-globalnest and T-SHiELD suffered from a persistent right-of-track bias in several cases at the 4–5-day forecast lead times. The reasons for this bias were related to the strength of the subtropical ridge over the western North Atlantic and are continuing to be investigated and diagnosed. A few key case studies from this very active hurricane season, including Hurricanes Laura and Delta, were examined.

Original languageEnglish (US)
Pages (from-to)143-161
Number of pages19
JournalWeather and Forecasting
Volume37
Issue number1
DOIs
StatePublished - Jan 2022
Externally publishedYes

Keywords

  • Forecast verification/skill
  • Hurricanes/typhoons
  • Numerical analysis/modeling
  • S: Tropical cyclones

ASJC Scopus subject areas

  • Atmospheric Science

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