Continuous Weather

Continuous weather forecasting aims to accurately predict weather patterns over time, improving upon traditional methods' limitations in temporal resolution and accuracy. Current research heavily utilizes deep learning, employing transformer-based architectures and neural ordinary differential equations (ODEs) to model complex spatiotemporal dynamics, often incorporating physical laws into the models to enhance accuracy and interpretability. These advancements are leading to more efficient and accurate global weather predictions, with significant implications for various sectors including agriculture, disaster preparedness, and energy management.