Using SSCRAM to Identify and Message Conditionally Favorable Severe-Thunderstorm Environments

Given a hypothetical environment, the most likely outcome might be nice sunny skies on a warm and humid day, though with an outside chance that, if a storm were to form, a destructive, violent tornado could occur. In this scenario, sustained storm formation may be highly uncertain, but if that condition were met, then the potentially high-impact outcome would be realized. This highlights the need for fully resolving the spectrum of potential outcomes from a given environment, so we can be prepared for -- and prepare others for -- a reasonable worst case scenario that may be accompanied by a very low total probability. Mesoscale analysis is a vital tool to gauging conditional potential, allowing us to anticipate the variety of possible outcomes, and promote weather readiness. As a result, assessing conditional severe potential is very much a critical tool for determining the reasonable worst case scenarios, analogous to evaluating the 10% exceedance values for storm surge.

The Statistical Severe Convective Risk Assessment Model (SSCRAM) is a statistical model that computes relative frequencies of severe thunderstorm report occurrences within two hours into the future and downstream of a lightning strike occurring in a particular mesoscale environment. Thus, SSCRAM permits the quantitative assessment of both conditional and unconditional severe probabilities. Conditional probabilities reflect the probability of a severe hazard if a lightning strike were to occur within a certain mesoscale environment -- regardless of whether lightning-producing convection is occurring or is simulated by numerical weather prediction guidance. Unconditional, or total, severe-thunderstorm probabilities can be derived by combining the conditional probability with the potential for a lightning strike to occur.

As a result, SSCRAM is a vital tool for resolving convective threats into conditional and unconditional components, which is key for messaging various possible outcomes and accompanying levels of impact certainty. The conditional probability of a significant severe event may be quite high, while the level of certainty and overall unconditional threat of such an event are very low. This presentation will illustrate ways in which SSCRAM can be used as a tool for resolving and messaging spectra of possible convective event outcomes, including all severe hazards types.