342
Nowcasting of hail using the lightning jump algorithm and radar

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Monday, 5 January 2015
Alex B. Young, University of Alabama, Huntsville, AL; and T. Chronis, E. V. Schultz, L. Carey, C. J. Schultz, K. M. Calhoun, and K. L. Ortega

The 2σ lightning jump algorithm (LJA) has become a useful tool for diagnosing storm severity using total lightning data. Recent studies have shown that the LJA consistently improves probability of detection (POD) and lowers false alarm ratio (FAR). Recent work has focused on making the LJA operational, thus a real-time demonstration test was performed at NSSL from the 2012-2014 timeframe. One of the foci of the project was to examine multiple verification methods of the algorithm to assess performance.

The Severe Hazards Analysis and Verification Experiment (SHAVE) project was implemented during the lightning jump demonstration for certain cases during the 2012-2014 timeframe. SHAVE strove to create high temporal and spatial resolution storm reports via phone calls to the public, specifically within the Alabama, Oklahoma, and Washington D.C. lightning mapping array (LMA) domains.

The goal of this work is to investigate the utility of the LJA, both alone and fused with radar, to improve situational awareness and warning operations in severe convective weather, especially the nowcasting of hail. Enhancement of the LJA verification process is explored via comparison between the lightning data, SHAVE reports and different radar products. The lightning data are retrieved from Lightning Mapping Arrays (LMAs) in the Oklahoma, Alabama and Washington D.C. domains. Specifically, flash extent density (FED) has shown promise as a precursor for hail development, when applying a time series comparison with maximum expected size of hail (MESH). Other operational radar products, such as vertically integrated liquid (VIL) and reflectivity at different temperature levels, are explored in order to extract all information that could aid in nowcasting and verifying hail events.