AbstractAbstractThe Warn-on-Forecast System (WoFS) is a convection-allowing ensemble that rapidly assimilates high-resolution radar, satellite, and other observational data to enhance forecasting capabilities at 0–6-h lead times. However, data latency and model spinup can reduce WoFS’ utility, especially within the first hour after initialization. On the other hand, Probability of Severe (ProbSevere), version 2 (PS2), is a set of statistical models that considers radar, satellite, and numerical weather prediction environment data to provide skillful severe hail, wind, and tornado probabilities at 0–1-h lead times, updated every 2 min. To enable seamless, probabilistic severe weather hazard forecasts leveraging the strengths of both systems, a random forest (RF) algorithm is developed that considers predictors from both WoFS and PS2. Experiments are conducted using different combinations of predictors at various spatial radii and lead times, and predictor importance is assessed. RFs configured using predictors from both WoFS and PS2 are found to outperform RFs configured from only one system, with the greatest gains in forecast skill from the all-predictor RFs coming at lead times less than 90 min. PS2 (WoFS) predictors are identified as more important at earlier (later) lead times. Severe hail forecasts were the most skillful of the three hazards, followed by severe wind and tornadoes. This new algorithm shows how combining complementary datasets using machine learning can improve short-term severe hazard guidance. Upcoming work will report on the results from testing during real-time forecasting experiments.Significance StatementA machine learning algorithm is used to combine two complimentary data sources for short-term severe weather forecasting. One is a high-resolution, rapidly updating numerical weather prediction model, while the other is a set of statistical models based on observations. The results show that combining these data sources results in more skillful forecasts than using either source alone. This algorithm will enhance short-term severe weather forecasting capabilities and provide insight into severe weather predictability at short lead times over local regions.
