Real-time lightning forecasting leveraging spatio-temporal correlations

Music City Center 

Lightning strikes can cause profound damage to property and life. Strikes to the power grid can cause severe damage, and dry lightning can easily start fires. Real time forecasting of lightning provides emergency services a tool to mitigate these risks. Using data from the Worldwide Lightning Location Network (WWLLN), we construct two models to predict future lightning: a generalized linear model (GLM) and a convolutional neural network paired with long short-term memory (CNN-LSTM). We focus on providing forecasts that rely on no external real-time information except the presence of lightning in the recent past. Therefore, both models leverage s patial and temporal correlations as their primary source of predictive power, rather than traditional spatio-temporal covariates. This ensures a robust model that can produce forecasts quickly even if other data streams, such as cloud data, are not readily available. These models provide a global heatmap of forecasted probabilities in addition to forecasted intensities of lightning. Model performances are evaluated against a rolling held out test set to understand performance variations over seasons.

lightning

convolutional neural network

generalized linear model 

Section on Statistics in Defense and National Security