Uncertainty in Model-Generated Fire Weather Values: How Does Model Variability Influence the Reliability of Dry Thunderstorm Risk and Ignition Potential Predictions?

Every year lightning causes numerous wildfires throughout the conterminous U.S. and Alaska. We have previously developed an algorithm to determine the risk of dry thunderstorms, and applied this methodology using output variables from the Penn State/NCAR mesoscale model (MM5) to produce a predictive scheme for estimating the risk of “dry” lightning in the western US. We found a significant correlation between high probabilities of dry lightning risk and fire ignitions. We are currently working on a project to include a measure of uncertainty in the model-generated meteorological variables used to assess fire danger, and to expand the dry lightning algorithm to incorporate fuels information into our predictions of dry lightning outbreaks. We are also expanding the predictions to use the latest regional- and national-scale models. This will enable us to produce new forecast products that predict the risk of sustained fire ignitions from dry thunderstorm outbreaks. The latest results from these efforts will be presented.