Atmospheric Science

Atmospheric science research is increasingly leveraging advanced computational methods to improve weather forecasting and climate modeling. Current efforts focus on integrating physical laws into deep learning architectures, such as neural ordinary differential equations and physics-informed neural networks, to enhance accuracy and uncertainty quantification while addressing the limitations of purely data-driven or purely physics-based approaches. This involves developing sophisticated cyberinfrastructure to manage and analyze massive datasets, and refining model evaluation techniques beyond pixel-wise metrics to incorporate spatial verification methods. Improved forecasting and climate modeling have significant implications for disaster preparedness, resource management, and societal adaptation to climate change.