Tornado Prediction

Accurate tornado prediction remains a significant challenge, with research focusing on improving forecast accuracy and reducing false alarms. Current efforts leverage advanced machine learning techniques, including deep learning architectures like convolutional neural networks and recurrent neural networks (e.g., BiLSTMs), often combined with ensemble methods and generative models to better capture the complex spatiotemporal dynamics of tornadic storms and enhance probabilistic forecasting. These models are trained on large datasets of radar data, incorporating polarimetric information and integrating data from multiple sources to improve prediction skill at various lead times, from near real-time to several days in advance. Improved prediction capabilities offer substantial benefits for public safety and disaster preparedness.