Verification of GFS Storm-Total Precipitation Forecasts for Landfalling Tropical Cyclones in CONUS Using an Object-Based Framework

Precipitation from landfalling tropical cyclones (TCs) poses a significant risk to life and property in both coastal and inland communities in CONUS. Early warning systems can help mitigate the impacts of TC-induced flooding; however, this requires accurate quantitative precipitation forecasts (QPFs). Forecasting precipitation from landfalling TCs is a challenge, as the intensity, duration, and location of precipitation is dependent on numerous storm-related and environmental factors. A handful of studies have attempted to identify and correct NWP model deficiencies and reduce forecast uncertainty by applying traditional and advanced verification metrics to TC QPF. However, these studies have mostly used gridpoint-to-gridpoint verification metrics, limited samples of forecasts, and older versions of NWP models. This study aims to evaluate the precipitation forecasts of landfalling TCs in CONUS using object-based verification methods to investigate what aspects of the precipitation forecast are driving QPF errors and to better understand the sources of uncertainty in the precipitation forecast. The Model Evaluation Toolkit (MET) is utilized to verify storm-total QPFs from the deterministic GFS model at 24–120-h lead times for TCs that made landfall in CONUS for multiple hurricane seasons using a wide range of traditional and object-based verification metrics. Through this analysis, the skill of storm-total precipitation forecasts from the GFS will be quantified in terms of location, intensity, timing, shape, and size errors.