Atmospheric Model

Atmospheric models aim to simulate and predict atmospheric behavior, from weather forecasting to climate projections and the study of exoplanets. Current research heavily utilizes machine learning, employing architectures like U-Nets, LSTMs, Transformers, and graph neural networks to improve accuracy, efficiency, and the incorporation of uncertainty quantification. This focus on data-driven approaches is driven by the need for faster, more reliable predictions across various spatial and temporal scales, impacting fields ranging from renewable energy integration to climate change mitigation and exoplanet characterization. The development of "foundation models" capable of handling diverse atmospheric tasks is also a significant emerging trend.