Forecasters’ Use of Warn-on-Forecast System Probabilistic Hazard Information (WoFS-PHI) during the 2023 HWT Watch-to-Warning Experiment

In NOAA HWT experiments and operations, NWS forecasters at National Centers and Weather Forecast Offices (WFOs) have found the prototype Warn-on-Forecast System (WoFS) useful for their prediction and messaging of weather hazards across the watch-to-warning time frame (0–6 h). There are, however, forecast performance limitations for storms early in their development cycle due to WoFS dependence on data assimilation. To improve probabilistic severe weather forecasts of these storms a new machine learning product, called WoFS-PHI, was developed and led by the second author, Eric Loken. WoFS-PHI integrates information from the WoFS ensemble with observational-based information from ProbSevere Version 2 (PSv2) to provide rapidly-updating probabilistic spatial severe weather hazard forecasts at lead times between 30 minutes and 3 hours.

The 2023 HWT Watch-to-Warning Experiment (HWT W2W) explored how both SPC and WFO forecasters might use WoFS-PHI, in combination with WoFS, PSv2, and NSSL’s PHI tool, to aid their prediction and communication of severe weather threats. Each week of the three-week experiment, 1–2 SPC forecasters and three WFO forecasters worked four cases in displaced real time using both new and traditional forecasting tools. The three WFO forecasters took on the responsibilities of Mesoanalyst, Warning forecaster and Communications focal point. This presentation will share results from the analysis of survey and interview questions focused on forecaster use of WoFS and WoFS-PHI. These questions explored challenges of using these tools, what communication between SPC and WFO forecasters might look like when using these tools, and how these forecast tools aid in the prediction and communication of severe weather threats up to three hours in advance of severe weather occurrence.