The WWLLN observations for the Atlantic tropical cyclone basin from late 2003 through 2007 are utilized in this study. During this period, the number of stations included in the network was increasing. In addition, the detection efficiency over the tropical Atlantic has spatial variability due to the station locations. The first step in this study is to prepare a lightning climatology for each year of the WWLN data for comparison with that from the Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) climatologies. This comparison will be used to provide a normalization procedure for the WWLLN data. The domain considered in this part of the study includes all of the global tropics and subtropics (45oN to 45oS latitude, 0o to 360o longitude).
A number of studies have shown that the lightning distribution around tropical cyclones is highly variable, and that the azimuthal variability near the storm center is correlated with the environmental vertical wind shear. There is a tendency for a lightning maximum to be located in the direction of and to the left of the vertical shear vector, relative to the storm center. In addition, a few studies have suggested that periods of rapid intensification are preceded by an increase in lightning activity, but, again with large variability from case to case. These results suggest that the lightning data must be viewed in the context of other environmental factors that affect intensity changes. These factors are taken into account by combining the WWLLN data with the data from the Statistical Hurricane Intensity Prediction Scheme (SHIPS). The SHIPS database includes variables related to the environmental vertical shear, sea surface temperature, ocean heat content and other factors. Several predictors from the normalized WWLLN including inner core lightning counts and lightning symmetry factors will be added to the SHIPS database. Then, the predictive content can be evaluated by calculating the additional variance of the observed intensity changes that is explained when the lightning data is added. This analysis will help to determine the improvements in tropical cyclone intensity prediction that might be possible from the GLM on GOES-R. In addition, the combined lightning and SHIPS model database will be used to better understand the relationships between tropical cyclones, their environments and lightning activity.