2019 Atlantic hurricane forecasts from the global-nested hurricane analysis and forecast system: Composite statistics and key events

Andrew Hazelton, Zhan Zhang, Bin Liu, Jili Dong, Ghassan Alaka, Weiguo Wang, Tim Marchok, Avichal Mehra, Sundararaman Gopalakrishnan, Xuejin Zhang, Morris Bender, Vijay Tallapragada, Frank Marks

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

NOAA’s Hurricane Analysis and Forecast System (HAFS) is an evolving FV3-based hurricane modeling system that is expected to replace the operational hurricane models at the National Weather Service. Supported by the Hurricane Forecast Improvement Program (HFIP), global-nested and regional versions of HAFS were run in real time in 2019 to create the first baseline for the HAFS advancement. In this study, forecasts from the global-nested configuration of HAFS (HAFS-globalnest) are evaluated and compared with other operational and experimental models. The forecasts by HAFS-globalnest covered the period from July through October during the 2019 hurricane season. Tropical cyclone (TC) track, intensity, and structure forecast verifications are examined. HAFS-globalnest showed track skill superior to several operational hurricane models and comparable intensity and structure skill, although the skill in predicting rapid intensification was slightly inferior to the operational model skill. HAFS-globalnest correctly predicted that Hurricane Dorian would slow and turn north in the Bahamas and also correctly predicted structural features in other TCs such as a sting jet in HurricaneHumberto during extratropical transition. Humberto was also a case whereHAFSglobalnest had better track forecasts than a regional version of HAFS (HAFS-SAR) due to a better representation of the large-scale flow. These examples and others are examined through comparisons with airborne tail Doppler radar from the NOAAWP-3D to provide a more detailed evaluation of TC structure prediction. The results from this realtime experiment motivate several future model improvements, and highlight the promise of HAFS-globalnest for improved TC prediction.

Original languageEnglish (US)
Pages (from-to)519-538
Number of pages20
JournalWeather and Forecasting
Volume36
Issue number2
DOIs
StatePublished - Apr 1 2021

Keywords

  • Hurricanes/typhoons
  • Numerical weather prediction/forecasting
  • Tropical cyclones

ASJC Scopus subject areas

  • Atmospheric Science

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