This research develops a machine-learning and data-assimilation framework that combines idealized and operational Earth systems models into a high-resolution, physically realistic “bridging model.” Applied to the El Niño–Southern Oscillation, the approach improves climate simulation accuracy while enabling exploration of alternative climate regimes and physically consistent what-if scenarios.

This research improves weather and climate forecasting by studying how dry air mixes into thunderstorm clouds, a process called entrainment. Using satellite observations, radar data, and interpretable machine learning, the work refines outdated cloud physics models, helping scientists better predict severe weather, hurricanes, and long-term climate behavior.

Inspired by biological reproduction, this research uses evolutionary algorithms to evolve mathematical equations that describe physical systems. Unlike black-box AI, these models are transparent and adaptable. By combining evolution with graph neural networks, the approach improves simulations for applications such as traffic control, robotics, and engineering design.