Over the past year, forecasts from a statistical lightning prediction scheme (Bothwell, 2002) have been made available to the meteorologists at the Storm Prediction Center (SPC). This scheme employs a set of climatological as well as meteorological lightning predictors in a "perfect prog" approach. Both the lightning climatologies and forecasts are available to the SPC forecasters. Initially, the scheme was used to produce 3 hour forecasts for one or more lightning flashes per 40 x 40 km grid box, in both the short term (0 to 3 hours), as well as longer term forecasts (out to 60 hours). These predictions were originally designed to provide guidance for the SPC one and two day fire weather forecast products that highlight the chance for dry thunderstorms (those with a tenth of an inch or less of rain). The lightning guidance products can also be useful for the short term forecasting of thunderstorm activity.
The perfect prog forecasts are run on both the RUC and ETA forecasts models as well as an hourly 3 dimensional analysis produced at the SPC. In an evaluation of both a warm and cool season set of forecasts, the perfect prog guidance for one or more lightning flashes using the RUC forecast model, exhibited good reliability out in time through the 12 to 15 hour forecasts. Forecasts between different models and even different runs of the same model (such as the RUC forecasts produced every three hours) can be compared.
Previous attempts by researchers over the years to predict the actual number of lightning flashes have generally shown little skill at predicting the number of lightning flashes. The approach here is to derive equations for events with significant numbers of lightning flashes. Using the perfect prog approach, the methodology is in place to calculate probabilities for areas of intense to severe lightning.
Currently the forecasts are being implemented for areas of 100 or more flashes per three hours, although probabilities for even higher flash rates can be developed. This paper will discuss the accuracy of this method and explore the implications it has for forecasting a large range of significant weather events.