The utility of total lightning observations in severe weather forecasting
A key aspect of short term weather forecasting is the ability to provide the public with adequate lead time in the event of severe weather. During severe weather events, it is vital that real time information such as radar data and spotter reports are available to aid National Weather Service (NWS) forecasters in the decision making process. An additional source of information that may prove useful in this regard is lightning data. Until very recently, forecasters have had access only to cloud-to-ground (CG) lightning data from the National Lightning Detection Network (NLDN). However, the utility of CG data as a severe weather forecasting tool is limited. Total (CG plus intracloud) lightning observations from very high frequency systems such as the Lightning Mapping Array (LMA; Rison et al. 1999) may be more useful. This thesis utilizes total lightning data from the Oklahoma LMA to assess the effectiveness of total lightning as an indicator of a given thunderstorm’s potential to produce severe weather. Specifically, a dataset of 52 thunderstorms (30 severe, 22 non-severe) within the domain of the Oklahoma LMA is analyzed to determine if severe weather is preceded by two features: a threshold total flash rate value which distinguishes severe thunderstorms from non-severe thunderstorms, and the presence of lightning jumps. A lightning jump algorithm was applied to each thunderstorm in the dataset in consideration of this second objective. Additionally, five thunderstorms are analyzed in greater detail to investigate these trends as they pertain to individual thunderstorms. A threshold flash rate upon which to determine thunderstorm severity is not apparent in the Oklahoma dataset. This is contrary to the results of a study of Florida thunderstorms by Williams et al. (1999), in which a clear threshold value was demonstrated. Lightning jumps are found to often precede the occurrence of severe weather, in good agreement with previous work.