LAUSR

The purpose of this study is to demonstrate the capability of an experimental, weather‐adaptive, high‐resolution, deterministic Warn‐on‐Forecast (WoF) analysis and forecast system (WoF3DVAR‐AFS) for predicting high‐impact severe weather events that occurred during the Hazardous Weather Testbed 2019 Spring Forecast Experiments. WoF3DVAR‐AFS uses a three‐dimensional variational (3DVAR) method as its core data assimilation system and the Advanced Research Version of the Weather Research and Forecasting (WRF‐ARW) model as its forward model. Surface measurements provided in meteorological aviation reports and the Oklahoma Mesonet, Doppler radar data, and spaceborne total lightning observations provided by the Geostationary Lightning Mapper are assimilated at 15‐min frequency over a target domain determined by the “Day 1” Convective Outlook product from the Storm Prediction Center. The chief goal of this system is to complement probabilistic forecasts generated by ensemble analysis and forecast systems, such as the experimental Warn‐on‐Forecast System (WoFS) with a higher‐resolution deterministic member to aid forecasters' decision‐making. We performed both qualitative and quantitative evaluations on 0–6 hr forecasts launched hourly from 1900 to 0300 UTC the next day for each of the 12 cases. Aggregated subjective forecast evaluation metrics from each individual case, as well as detailed comparison against available verification datasets, suggest that the forecasts are generally skillful in terms of composite reflectivity fields, quantitative precipitation forecasts, and the strength and location of rotation tracks and damaging winds. This study presents initial efforts to assess the performance of WoF3DVAR‐AFS and provides possible directions for further improvements, including the development of a weather‐adaptive, dual‐resolution analysis and forecast system hybrid with an ensemble system, such as the experimental Warn‐on‐Forecast system.