To explore this idea, we examined a collection of model fields produced from the 4-km 36-hour WRF-ARW simulation run daily at 00 UTC at the National Severe Storms Laboratory (NSSL) during Spring 2008. Fields based on simulated model reflectivity, vertical velocity, 10 meter wind speed, and updraft helicity (a diagnostic field to identify supercellular storms) were chosen, as preliminary assessments suggested that they were likely to be useful indicators of convective intensity in the model. For each field, surrogate severe storm reports (SSRs) were placed at grid points where a parameter threshold was exceeded at any point during a 24-hour period corresponding to the 12 to 36 h forecast period (12 UTC to 12 UTC). To produce a guidance product akin to SPC probabilistic Convective Outlooks, a Gaussian smoother was applied to the grid of SSRs to create a density (or probability). This product is referred to as a surrogate severe probability forecast (SSPF).
Sobash et al. (2009) established a proof-of-concept and highlighted some of the challenges associated with this procedure. In this work, an objective and quantitative comparison between the SSPF guidance and observed storm reports was undertaken. First, the ability of the SSRs to reproduce the observed spatial behavior of observed storm reports was documented. Additional research questions included 1) what threshold yields the most skillful SSPF guidance for each surrogate field and 2) what surrogate field provides the most skillful guidance overall? Traditional verification measures for probabilistic forecasts, including forecast reliability and resolution, were used to investigate these questions. Although a deterministic CAM was used in this work, the procedure can be extended to an ensemble of CAMs. An ensemble-based application of this work will be highlighted in another presentation at the conference.