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Forecasts of tropical cyclone (TC) tornadoes are less skillful than their non‐TC counterparts at all lead times. The development of a convection‐allowing regional ensemble, known as the Warn‐on‐Forecast System (WoFS), may help improve short‐fused TC tornado forecasts. As a first step, this study investigates the fidelity of convective‐scale kinematic and thermodynamic environments to a preliminary set of soundings from WoFS forecasts for comparison with radiosondes for selected 2020 landfalling TCs. Our study shows reasonable agreement between TC convective‐scale kinematic environments in WoFS versus observed soundings at all forecast lead times. Nonetheless, WoFS is biased toward weaker than observed TC‐relative radial winds, and stronger than observed near‐surface tangential winds with weaker winds aloft, during the forecast. Analysis of storm‐relative helicity (SRH) shows that WoFS underestimates extreme observed values. Convective‐scale thermodynamic environments are well simulated for both temperature and dewpoint at all lead times. However, WoFS is biased moister with steeper lapse rates compared to observations during the forecast. Both CAPE and, to a lesser extent, 0–3‐km CAPE distributions are narrower in WoFS than in radiosondes, with an underestimation of higher CAPE values. Together, these results suggest that WoFS may have utility for forecasting convective‐scale environments in landfalling TCs with lead times of several hours.
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This content will become publicly available on March 1, 2026
Evaluation of 0–6-Hour Forecasts from the Experimental Warn-on-Forecast System and the Hybrid Analysis and Forecast System for Real-Time Cases in 2021
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.
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- Award ID(s):
- 2136161
- PAR ID:
- 10656708
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Weather and Forecasting
- Volume:
- 40
- Issue:
- 3
- ISSN:
- 0882-8156
- Page Range / eLocation ID:
- 451 to 469
- Subject(s) / Keyword(s):
- Forecasting Short-range prediction Data assimilation Model evaluation/performance Numerical weather prediction/forecasting
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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