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Abstract This study compares real-time forecasts produced by the Warn-on-Forecast System (WoFS) and a hybrid ensemble and variational data assimilation and prediction system (WoF-Hybrid) for 31 events during 2021. Object-based verification is used to quantify and compare strengths and weaknesses of WoFS ensemble forecasts with 3-km horizontal grid spacing and WoF-Hybrid deterministic forecasts with 1.5-km horizontal grid spacing. The goal of such comparison is to provide evidence as to whether WoF-Hybrid has performance characteristics that complement or improve upon those of WoFS. Results indicate that both systems provide similar accuracy for timing and placement of thunderstorm objects identified using simulated reflectivity. WoF-Hybrid provides more accurate forecasts of updraft helicity tracks. Differences in forecast quality are case dependent; the largest difference in accuracy favoring WoF-Hybrid occurs in eight cases identified as “high-impact” by the quantity of National Weather Service Local Storm Reports, while WoFS performance is favored at short lead times for 10 “moderate-” and 13 “low-impact” events. WoF-Hybrid reflectivity objects are closer in size and location to observed objects. However, a higher thunderstorm overprediction bias is identified in WoF-Hybrid, particularly early in the forecast. Two severe weather events are selected for detailed investigation. In the case of 26 May, both systems had similar skill; however, for 10 December, WoF-Hybrid forecasts significantly outperformed WoFS forecasts. These results show improved performance for WoF-Hybrid over WoFS under certain regimes that warrants further investigation. To understand reasons for these differences will help incorporate higher-resolution modeling into Warn-on-Forecast systems. Significance StatementThe NOAA Warn-on-Forecast (WoF) project uses advanced data assimilation for rapidly updating numerical weather prediction systems to provide forecasts of individual thunderstorms. Forecasts show promise for enabling greater warning lead time for some storms. The flagship Warn-on-Forecast System (WoFS) is a 36-member analysis and 18-member forecast system at 3-km grid spacing. The project also produced a single member system that employs variational analysis and produces a deterministic forecast at 1.5-km grid spacing (WoF-Hybrid). This study seeks to evaluate and compare the performance of WoFS and WoF-Hybrid for 31 severe weather events that occurred during 2021. Results found that WoF-Hybrid predicts storm rotation particularly well compared to WoFS, and several other strengths and limitations of both systems are identified. This research may help us understand the complementary nature of two systems and improve our ability to provide more reliable 0–6-h forecasts in the future. 

Abstract Hail forecasts produced by the CAM-HAILCAST pseudo-Lagrangian hail size forecasting model were evaluated during the 2019, 2020, and 2021 NOAA HazardousWeather Testbed Spring Forecasting Experiments. As part of this evaluation, HWT SFE participants were polled about their definition of a “good” hail forecast. Participants were presented with two different verification methods conducted over three different spatiotemporal scales, and were then asked to subjectively evaluate the hail forecast as well as the different verificaiton methods themselves. Results recommended use of multiple verification methods tailored to the type of forecast expected by the end-user interpreting and applying the forecast. The hail forecasts evaluated during this period included an implementation of CAM-HAILCAST in the Limited Area Model of the Unified Forecast System with the Finite Volume 3 (FV3) dynamical core. Evaluation of FV3-HAILCAST over both 1-h and 24-h periods found continued improvement from 2019 to 2021. The improvement was largely a result of wide intervariability among FV3 ensemble members with different microphysics parameterizations in 2019 lessening significantly during 2020 and 2021. Overprediction throughout the diurnal cycle also lessened by 2021. A combination of both upscaling neighborhood verification and an object-based technique that only retained matched convective objects was necessary to understand the improvement., agreeing with the HWT SFE participants’ recommendations for multiple verification methods.