Validation of HWRF-Based Probabilistic TC Wind and Precipitation Forecasts

Tropical cyclones are associated with a variety of significant social hazards, including wind, rain, and storm surge. Despite this, most of the model validation effort has been directed toward track and intensity forecasts. In contrast, few studies have investigated the skill of state-of-the-art, high-resolution ensemble prediction systems in predicting associated TC hazards, which is crucial since TC position and intensity do not always correlate with the TC-related hazards, and can result in impacts far from the actual TC center. Furthermore, dynamic models can provide flow-dependent uncertainty estimates, which in turn can provide more specific guidance to forecasters than statistical uncertainty estimates based on past errors. This study validates probabilistic forecasts of wind speed and precipitation hazards derived from the HWRF ensemble prediction system and compares its skill to forecasts by the stochastically based operational Monte Carlo Model (NHC), the IFS (ECMWF), and the GEFS (NOAA) in use in 2017-19. Wind and precipitation forecasts are validated against NHC best track wind radii information and the National Stage IV QPE Product. The HWRF 34-kt (1 kt approximate to 0.51 m s(-1)) wind forecasts have comparable skill to the global models up to 60-h lead time before HWRF skill decreases, possibly due to detrimental impacts of large track errors. In contrast, HWRF has comparable quality to its competitors for higher thresholds of 50 and 64 kt throughout 120-h lead time. In terms of precipitation hazards, HWRF performs similar or better than global models, but depicts higher, although not perfect, reliability, especially for events over 5 in. (120 h)(-1). Postprocessing, like quantile mapping, improves forecast skill for all models significantly and can alleviate reliability issues of the global models.