A Nowcasting Method for Severe Convective Weather Based on Array Radar and Lightning Jumps

Convective weather, often associated with heavy precipitation, hail, lightning, and other hazardous phenomena, is highly unpredictable, short-lived, and localized, making forecasting and early warning particularly challenging. The formation of lightning is closely tied to the thermodynamic and microphysical processes within severe convective weather systems (e.g., Qie et al., 2021). Not only does it pose a significant threat to human life and properties, but it has also been recognized by the International Electrotechnical Commission (IEC) as a major hazard to power systems, communication networks, buildings, and electronic devices.

Since the mid-20th century, Doppler weather radars have been widely used to monitor hazardous weather by identifying precipitation, storm structures, and movement. Advances in radar technology, especially the introduction of array weather radar, have further enhanced the precision and timeliness of severe weather nowcasting. Unlike traditional single-antenna radars, array radars use multiple small antennas to form a large, flexible antenna array for rapid and precise beam control. This distributed phased-array system excels in detecting fine-scale flow and intensity fields, offering powerful tools for studying small-scale convective phenomena (e.g., Adachi et al., 2016).

This study utilizes array radar data from Foshan, Guangdong, China, high-precision lightning location data, and ground-based meteorological observation data to identify, track, and forecast severe convective weather. Based on a radar dual-threshold convective storm tracking and identification algorithm (e.g., Tian et al., 2019), combined with a lightning jump algorithm (e.g., Schultz et al., 2017), this nowcasting method monitors the lightning variation characteristics within strong convective cells (CCs), providing indices for severe convective weather. By comparing results with observations and optimizing algorithm parameters, the method improves hit rates, reduces false alarms, and achieves an average lead time of ~22 minutes with a hit rate over 80%, as demonstrated by case studies. This method can be effectively applied to enhance the monitoring and early warning capabilities for severe convective weather, thereby mitigating the impact of lightning and reducing lightning-related disasters for critical infrastructure, particularly power systems.

 

Acknowledgment

This work was jointly supported by the KERAUNIC project (ref: NIA2_NGET0055, National Grid Electricity Transmission, 2024) under the Network Innovation Allowance (NIA), the Arctic Pavilion Open Research Fund of Nanjing Joint Institute for Atmospheric Sciences under Grant BJG202410 and the China Scholarship Council program under Grant 202305330027.

 

References

Adachi, T., Kusunoki, K., Yoshida, S., et al. (2016). High-speed volumetric observation of a wet microburst using X-band phased array weather radar in Japan. Monthly Weather Review, 144(10), 3749-3765.

National Grid Electricity Transmission. (2024). Knowledge Elicitation of Risks to Assets Under LightNing Impulse Conditions (KERAUnIC). https://smarter.energynetworks.org/projects/nia2_nget0055

Qie, X., Yuan, S., Chen, Z., et al. (2021). Understanding the dynamical-microphysical-electrical processes associated with severe thunderstorms over the Beijing metropolitan region. Science China Earth Sciences, 64, 10-26.

Schultz, C. J., Carey, L. D., Schultz, E. V., & Blakeslee, R. J. (2017). Kinematic and microphysical significance of lightning jumps versus nonjump increases in total flash rate. Weather and forecasting, 32(1), 275-288.

Tian, Y., Qie, X., Sun, Y., et al. (2019). Total lightning signatures of thunderstorms and lightning jumps in hailfall nowcasting in the Beijing area. Atmospheric Research, 230, 104646.