Construction of a Lightning Index Using Integrated Precipitable Water Derived From the Global Positioning System

摘要

A new approach is presented to forecasting the Kennedy Space Center's primary weather challenge, lightning. After examining the first years worth of integrated precipitable water data derived from Global Positioning System (GPS) and surface stations, two periods were chosen to develop a GPS lightning prediction model. Statistical regression methods were used to identify predictors that added skill in forecasting a lightning event. Four predictors proved important in forecasting lightning events; maximum electric field mill values, GPS Integrated Precipitable Water (IPW), nine hour change (V9 - hr) in IPW, and K index. Using the coefficients for these predictors along with a logistic regression equation, a running time series was plotted for the predictand. A common pattern emerged several hours prior to a lightning event. Whenever the predictand log value was 0.7 or below, lightning occurred within the next 12.5 hours. Lightning events were predicted using a logistic threshold value of 0.7 and forecasting time constraints based on the Kennedy Space Center (KSC) criteria. Forecast verification results obtained by using a contingency table revealed a 26.2% decrease from the Cape's previous season false alarm rates for a non-independent period, and a 13.2% decrease in false alarm rates for an independent test season using the GPS lightning model. Additionally, the model improved the KSC's desired lead-time by nearly 10%. Although a lightning strike window of 12 hours is quit lengthy, forecasters will now have an additional forecasting tool that can be implemented in their lightning forecast process. Once the value of the GPS lightning model has been confirmed using data from the 2000 season, it is anticipated that the model will enhance mission readiness and save valuable time and dollars by helping forecasters anticipate and improve forecast lightning events at the KSC.