The Temporal and Probabilistic Relationship between Lightning Jump Occurrence and Radar-Derived Thunderstorm Intensification

The fusion of datasets and algorithms into single products is the current trend in operational forecasting. One such algorithm is the ProbSevere algorithm outlined by Cintineo et al. (2012). The ProbSevere algorithm combines cloud top cooling from satellite observations, maximum expected size of hail (MESH) information from radar and environmental parameters like convective available potential energy from model output in the near storm environment. Recently, an effort has been made to incorporate the total lightning jump algorithm into the ProbSevere algorithm as part of the data fusion process (K. Calhoun, personal communication). However, little has been done in the way that illustrates the temporal relationship between the lightning jump and individual components in ProbSevere. Azimuthal shear is another radar derived product that indicates low-level rotation and potential for tornadoes. Azimuthal shear will be incorporated into the ProbSevere algorithm in the near future (J. Cintineo, personal communication).

The goals of this study are:

1) Examine trends in MESH and azimuthal shear prior to and after lightning jump (or peak change in the total flash rate in non-jump thunderstorms) to provide a basic understanding of the temporal relationship between the radar derived intensity products and lightning jump occurrence.

2) Provide probabilistic guidance on hail size and the potential for severe hail and tornadoes based on lightning jump and total flash rate information for operational weather forecasting. MESH and azimuthal shear are used as objective proxies for severe weather potential in the development of the lightning-based probabilistic guidance.

A sample of 1500+ thunderstorms in which MESH, azimuthal shear and total lightning jump information are present is used to understand the temporal probabilistic relationships in both jump and non-jump thunderstorms.

The outcomes of this study will be useful in validating the future contribution of the lightning jump into the calculation of the probability of severe weather within the ProbSevere algorithm. Furthermore, the comparison between lightning jump and radar derived intensity metrics, which are used in National Weather Service warning forecast operations in the Multi-Radar Mulit-Sensor dataset, will provide more confidence in warning decisions because a conceptual model can be developed using temporal relationship between radar and lightning intensity metrics.