The Evaluation of Real-Time Hurricane Analysis and Forecast System (HAFS) Stand-Alone Regional (SAR) Model Performance for the 2019 Atlantic Hurricane Season

• Published in: Atmosphere
• Main Authors: Jili Dong, Bin Liu, Zhan Zhang, Weiguo Wang, Avichal Mehra, Andrew T. Hazelton, Henry R. Winterbottom, Lin Zhu, Keqin Wu, Chunxi Zhang, Vijay Tallapragada, Xuejin Zhang, Sundararaman Gopalakrishnan, Frank Marks
• Format: Text
• Language: English
• Published: Multidisciplinary Digital Publishing Institute 2020
• Subjects: tropical cyclones; numerical weather prediction; high resolution tropical cyclone forecasts; finite-volume cubed sphere (FV3); Hurricane Analysis and Forecast System (HAFS); Stand-Alone Regional (SAR) models; North Atlantic
• Online Access: https://doi.org/10.3390/atmos11060617

The next generation Hurricane Analysis and Forecast System (HAFS) has been developed recently in the National Oceanic and Atmospheric Administration (NOAA) to accelerate the improvement of tropical cyclone (TC) forecasts within the Unified Forecast System (UFS) framework. The finite-volume cubed sphere (FV3) based convection-allowing HAFS Stand-Alone Regional model (HAFS-SAR) was successfully implemented during Hurricane Forecast Improvement Project (HFIP) real-time experiments for the 2019 Atlantic TC season. HAFS-SAR has a single large 3-km horizontal resolution regional domain covering the North Atlantic basin. A total of 273 cases during the 2019 TC season are systematically evaluated against the best track and compared with three operational forecasting systems: Global Forecast System (GFS), Hurricane Weather Research and Forecasting model (HWRF), and Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic model (HMON). HAFS-SAR has the best performance in track forecasts among the models presented in this study. The intensity forecasts are improved over GFS, but show less skill compared to HWRF and HMON. The radius of gale force wind is over-predicted in HAFS-SAR, while the hurricane force wind radius has lower error than other models.