LightNet

Abstract

Lightning as a natural phenomenon poses serious threats to human life, aviation and electrical infrastructures. Lightning prediction plays a vital role in lightning disaster reduction. Existing prediction methods, usually based on numerical weather models, rely on lightning parameterization schemes for forecasting. These methods, however, have two drawbacks. Firstly, simulations of the numerical weather models usually have deviations in space and time domains, which introduces irreparable biases to subsequent parameterization processes. Secondly, the lightning parameterization schemes are designed manually by experts in meteorology, which means these schemes can hardly benefit from abundant historical data. In this work, we propose a data-driven model based on neural networks, referred to as LightNet, for lightning prediction. Unlike the conventional prediction methods which are fully based on numerical weather models, LightNet introduces recent lightning observations in an attempt to calibrate the simulations and assist the prediction. LightNet first extracts spatiotemporal features of the simulations and observations via dual encoders. These features are then combined by a fusion module. Finally, the fused features are fed into a spatiotemporal decoder to make forecasts. We conduct experimental evaluations on a real-world North China lightning dataset, which shows that LightNet achieves a threefold improvement in equitable threat score for six-hour prediction compared with three established forecast methods.