Speaker
Description
This presentation explores the convergence of Artificial Intelligence (AI) and climate modeling for high-resolution (km-scale) climate prediction.
We will discuss the use of accelerated km-scale simulations to generate synthetic climate data, enabling the training of even more sophisticated climate models. We'll delve into the integration of observational data from diverse sources – weather stations, satellites, and airborne platforms – through diffusion models, a technique commonly used for image generation.
To address the exascale challenge posed by climate data volume, we will present an AI-based compression method utilizing deep neural networks for efficient data representation. This approach allows for near-lossless reconstruction of the original data for subsequent analysis. We posit that this synergistic approach, combining high-fidelity data generation with advanced data assimilation techniques, has the potential to significantly improve the accuracy of climate predictions. This, in turn, can inform policy decisions and guide societal responses to climate change.
