Lightning-Based Tropical Cyclone Rapid Intensification Guidance

Abstract

Abstract With several seasons of Geostationary Lightning Mapper (GLM) data, this work revisits incorporating lightning observations into operational tropical cyclone rapid intensification guidance. GLM provides freely available, real-time lightning data over the central and eastern North Pacific and North Atlantic Oceans. A long-term lightning dataset is needed to use GLM in a statistical–dynamical operational application to capture the relationship between lightning and the rare occurrence of rapid intensification. This work uses the World Wide Lightning Location Network (WWLLN) dataset from 2005 to 2017 to develop lightning-based predictors for rapid intensification guidance models. The models mimic the operational Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index and Rapid Intensification Prediction Aid frameworks. The frameworks are averaged to form a consensus as a means to isolate the impact of the lightning predictors. Two configurations for lightning predictors are assessed: a spatial configuration with 0–100-km inner core and 200–300-km rainband area for the preceding 6-h predictors and a temporal configuration with an inner core only for the preceding 0–1, 0–6, and 6–12 h. When tested on the 2018–21 seasons, the temporal configuration adds skill primarily to the 12–48-h forecasts when compared to the no-lightning version and rapid intensification operational consensus. When WWLLN is replaced with GLM, minor changes to the prediction are observed suggesting that this approach is suitable for operational applications and provides a new baseline for tropical cyclone lightning-based rapid intensification aids. Significance Statement The forecasting of rare, yet critical, tropical cyclone rapid intensification events continues to be challenging. The current operational tools to anticipate rapid intensity changes use a combination of numerical weather prediction–derived environmental conditions and satellite-based cloud top temperature variations of deep convection. Here, we use freely available Geostationary Lightning Mapper data, which provide independent information about convection, in similar intensity guidance frameworks using temporal and spatial aspects of lightning variability. Our results show an improvement in short-term (12–48 h) rapid intensification forecasts by using temporal lightning information, and our investigation highlights that users of Geostationary Lightning Mapper lightning information should be cognizant of the influence and impact of land on these observations.