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.

The initialization of ocean conditions is essential to coupled tropical cyclone (TC) forecasts. This study investigates the impact of ocean observation assimilation, particularly underwater glider data, on high-resolution coupled TC forecasts. Using the coupled Hurricane Weather Research and Forecasting (HWRF) Model–Hybrid Coordinate Ocean Model (HYCOM) system, numerical experiments are performed by assimilating underwater glider observations alone and with other standard ocean observations for the forecast of Hurricane Gonzalo (2014). The glider observations are able to provide valuable information on subsurface ocean thermal and saline structure, even with their limited spatial coverage along the storm track and the relatively small amount of data assimilated. Through the assimilation of underwater glider observations, the prestorm thermal and saline structures of initial upper-ocean conditions are significantly improved near the location of glider observations, though the impact is localized because of the limited coverage of glider data. The ocean initial conditions are best represented when both the standard ocean observations and the underwater glider data are assimilated together. The barrier layer and the associated sharp density gradient in the upper ocean are successfully represented in the ocean initial conditions only with the use of underwater glider observations. The upper-ocean temperature and salinity forecasts in the first 48 h are improved by assimilating both underwater glider and standard ocean observations. The assimilation of glider observations alone does not make a large impact on the intensity forecast due to their limited coverage along the storm track. The 126-h intensity forecast of Hurricane Gonzalo is improved moderately through assimilating both underwater glider data and standard ocean observations.

The National Oceanic and Atmospheric Administration’s (NOAA) cloud-permitting high-resolution operational Hurricane Weather and Research Forecasting (HWRF) model includes the sophisticated hybrid grid-point statistical interpolation (GSI) and Ensemble Kalman Filter (EnKF) data assimilation (DA) system, which allows assimilating high-resolution aircraft observations in tropical cyclone (TC) inner core regions. In the operational HWRF DA system, the flow-dependent background error covariance matrix is calculated from the HWRF self-cycled 40-member ensemble. This DA system has proved to provide improved initial TC structure and therefore improved TC track and intensity forecasts. However, the uncertainties from the model physics are not taken into account in the FY2017 version of the HWRF DA system. In order to further improve the HWRF DA system, the stochastic physics perturbations are introduced in the HWRF DA, including the cumulus convection scheme, the planetary boundary layer (PBL) scheme, and model surface physics (drag coefficient), for HWRF-based ensembles. This study shows that both TC initial conditions and TC track and intensity forecast skills are improved by adding stochastic model physics in the HWRF self-cycled DA system. It was found that the improvements in the TC initial conditions and forecasts are the results of ensemble spread increases which realistically represent the model background error covariance matrix in HWRF DA. For all 2016 Atlantic storms, the TC track and intensity forecast skills are improved by about ~3% and 6%, respectively, compared to the control experiment. The case study shows that the stochastic physics in HWRF DA is especially helpful for those TCs that have inner-core high-resolution aircraft observations, such as tail Doppler radar (TDR) data.