9.2 Probabilistic and Temporal Relationships between Lightning Jump Occurrence and Radar-Derived Thunderstorm Intensification

Wednesday, 9 November 2016: 9:15 AM
Pavilion Ballroom (Hilton Portland )
Christopher J. Schultz, NASA/MSFC, Huntsville, AL; and T. Chronis, S. M. Stough, L. D. Carey, and D. J. Cecil

The fusion of datasets and algorithms into single products is the current trend in operational forecasting.  One such algorithm is the NOAA CIMSS ProbSevere algorithm.  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, a joint effort by NOAA CIMSS and CIMMS has been made to incorporate the total lightning jump into the ProbSevere algorithm as part of the data fusion process.  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.  According to NOAA CIMSS, azimuthal shear will be incorporated into the ProbSevere algorithm in the near future.  

The goals of this study are to:

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) in order 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 and total lightning jump information are present is used to understand the temporal probabilistic relationships in both jump and non-jump thunderstorms.  A smaller sample of approximately 100 tornadic and non-tornadic supercells will be analyzed in a similar manner to understand the temporal probabilistic relationships for azimuthal shear.

The outcomes of this study will be useful in guiding and 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 Multi-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.

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