Improvement of lightning nowcasting model using convective cell-based radar motion vectors

The Korea Meteorological Administration (KMA) has operated a lightning nowcasting model based on radar-derived motion vectors using MAPLE(McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation) since 2015. This model provides 10-minute interval forecasts with lead times of up to 6 hours for use by both forecasters and the general public.
In this study, we present a newly developed lightning nowcasting model designed to extend lead times and enhance the timeliness and accuracy of lightning risk alerts. Unlike conventional methods that calculate motion vectors across the entire precipitation field, the proposed model automatically identifies convective cell areas with high lightning potential based on the ETOP30 threshold (reflectivity ≥ 30 dBZ). Within these selected regions, sequential Hybrid Surface Rainfall (HSR) radar fields are analyzed using the Variational Echo Tracking (VET) algorithm, which estimates high-resolution motion vectors (1 km, 10 min) by optimizing a cost function that minimizes differences in reflectivity across three consecutive radar images.
To mitigate limitations of convective cell-based motion vector fields, the MAPLE motion vector field at previous 10 minutes in real-time is used as a background field to correct initial estimation errors in the VET algorithm. This hybrid approach enables more accurate tracking of convective cell evolution and movement, while also reducing the delivery time of lightning nowcasting information by approximately 7 minutes compared to the previous model. 
Validation using 16 lightning cases from 2023 to 2024, comparing the nowcasting fields to LINET lightning observations, showed that the new model achieved an average 1-hour forecast CSI of 0.55, POD of 0.57, and FAR of 0.08. A peak CSI of 0.68 was recorded during a band-type lightning event on July 7, 2024.
These results demonstrate the improved performance of the proposed model in operational lightning nowcasting and highlight its potential for enhancing real-time risk assessment and public weather services.

KEYWORD
Convective Cell, HSR, Radar Motion vector, Lightning Nowcasting, ETOP30, VET

Acknowledgements:
This research was supported by the ”Development of Integrated radar analysis and customized radar technology (KMA2021-03021)” of “Development of integrated application technology for Korea weather radar” project funded by the Weather Radar Center, Korea Meteorological Administration.