A vision transformer for lightning intensity estimation using 3D weather radar

Abstract

Lightning has strong destructive powers; its blast wave, high temperature, and high voltage can pose a great threat to human production, life, and personal safety. The destructive power of high-intensity lightning is much greater than that of low-intensity lightning. The estimation of lightning intensity can provide an important reference for determining the lightning protection level and lightning disaster risk assessment. Lightning is a type of small-scale severe convective weather phenomenon. Weather radar is one of the best monitoring systems that can frequently sample the detailed three-dimensional (3D) structures of convective storms, with a small spatial scale and short lifetime at high temporal and spatial resolutions. Therefore, it is possible to extract the 3D spatial feature strongly correlated with lightning from 3D weather radar for estimating lightning intensity. This paper proposes a Vision Transformer model for lightning intensity estimation that can automatically estimate lightning intensity from 3D weather radar data. In an experiment, we transferred the task of estimating lightning intensity into a multicategory classification task. A framework was designed to produce lightning feature samples for model input from 3D weather radar and lightning location data. Then, the Synthetic Minority Over-Sampling Technique (SMOTE) algorithm was used to balance and optimize the sample distribution. Finally, samples were input into the proposed lightning intensity estimation model based on Vision Transformer for training and evaluation. Experimental results show that the proposed model based on Vision Transformers performs well with lightning intensity estimation.

Keywords: 3D weather radar; Lightning intensity estimation; Multicategory classification; SMOTE; Vision transformer.