Automated Storm Tracking and the Lightning Jump Algorithm Using GOES-R Geostationary Lightning Mapper (GLM) Proxy...

Area of Science:

• Atmospheric Science
• Meteorology
• Remote Sensing

Background:

• The lightning jump algorithm (LJA) shows promise for severe weather forecasting.
• Transitioning LJA from research to operational use requires robust, automated systems.
• Existing methods for storm tracking and lightning analysis need integration for enhanced prediction.

Purpose of the Study:

• To develop a fully automated lightning jump system for operational forecasting.
• To evaluate the impact of storm tracking spatial scale on system performance.
• To refine the lightning jump algorithm (LJA) parameters for improved applicability.

Main Methods:

• Developed an automated system combining objective storm tracking, Geostationary Lightning Mapper (GLM) proxy data, and the LJA.
• Storm cluster tracking utilized vertically integrated liquid (VIL) and flash rate density.
• System performance evaluated using Probability of Detection (POD) and False Alarm Ratio (FAR) with parameter sensitivity adjustments.

Main Results:

• Increasing the spatial scale of storm clusters decreased the dynamic range of system performance.
• Sigma-level and flash rate threshold were the most influential LJA parameters.
• Verification methodology significantly impacts the evaluation of the lightning jump system's performance.

Conclusions:

• The developed automated lightning jump system provides a framework for operational forecasting.
• Optimizing spatial scale and algorithm parameters is crucial for system effectiveness.
• Standardized verification methodologies are essential for reliable LJA evaluation.