No abstract

Dry thunderstorms (those that occur without significant rainfall at the ground) are common in the interior western United States. Moisture drawn into the area from the Gulfs of Mexico and California is sufficient to form high-based thunderstorms. Rain often evaporates before reaching the ground, and cloud-to-ground lightning generated by these storms strikes dry fuels. Fire weather forecasters at the National Weather Service and the National Interagency Coordination Center try to anticipate days with widespread dry thunderstorms because they result in multiple fire ignitions, often in remote areas. The probability of the occurrence of dry thunderstorms that produce fire-igniting lightning strikes was found to be greater on days with high instability and a deficit of moisture at low levels of the atmosphere. Based on these upper-air variables, an algorithm was developed to estimate the potential of dry lightning (lightning that strikes the ground with little or no rainfall at the surface) when convective storms are expected. In the current study, this algorithm has been applied throughout the western United States, with modeled meteorological variables rather than the observed soundings that have previously been used, to develop a predictive scheme for estimating the risk of dry thunderstorms. Predictions of the risk of dry thunderstorms were generated from real-time forecasts using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) for the summers of 2004 and 2005. During that period, 240 large lightning-caused fires were ignited in the model domain. Of those fires, 40% occurred where the probability of dry lightning was predicted to be equal to or greater than 90% and 58% occurred where the probability was 75% or greater.

No abstract

No abstract

Previous studies of model performance at varying resolutions have focused on winter storms or isolated convective events. Little attention has been given to the static high pressure situations that may lead to severe wildfire outbreaks. This study focuses on such an event so as to evaluate the value of increased model resolution for prediction of fire danger. The results are intended to lay the groundwork for using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) as input to the National Fire Danger Rating System to provide gridded predictions of fire danger indices. Predicted weather parameters were derived from MM5 and evaluated at three different resolutions (36, 12, and 4 km). Model output was compared with observations during the 2000 fire season in western Montana and northern Idaho to help to determine the model's skill in predicting fire danger. For application in fire danger rating, little significant improvement was found in skill with increased model resolution using standard forecast verification techniques. Diurnal bias of modeled temperature and relative humidity resulted in errors larger than the differences between resolutions. Significant timing and magnitude errors at all resolutions could jeopardize accurate prediction of fire danger.