Year: 2025
Type: conference-paper
Abstract: Accurate and timely lightning nowcasting has important application value in preventing meteorological disasters and reducing economic losses. We conducted research on the technical difficulties in the nowcasting of small-scale thunderstorm lightning. Considering that most lightning processes in sparse lightning data are associated with small-scale thunderstorms, which have a small spatial scope and evolve rapidly, we proposed an optimized method for small-scale thunderstorm lightning forecasting. A small-scale thunderstorm lightning dataset was constructed through the analysis of the DBSCAN spatio-temporal clustering algorithm. To address the issue of the imbalanced ratio between lightning and non-lightning samples in lightning data, a class-weight adaptive cross-entropy loss function was designed to dynamically adjust the weights and alleviate the class imbalance. A three-dimensional multi-scale refinement module was designed to establish multi-scale spatio-temporal feature associations, enhance the ability to capture lightning activities of small-scale cells, and achieve refined nowcasting of small-scale thunderstorms in sparse lightning data. Experiments have proven that the systematic optimization of the small-scale thunderstorm forecasting model significantly improves the refined forecasting ability for small-scale thunderstorms.
Authors: Bao Wang, Gang Li, Xiaodong Liu, Jianan Xie, Wenjin Pan, Ling Zhang
Institution: Taiyuan University of Technology
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414931857