2024-12-02 カリフォルニア大学サンディエゴ校(UCSD)
＜関連情報＞
- https://today.ucsd.edu/story/accelerating-climate-modeling-with-generative-ai
- https://arxiv.org/abs/2406.14798
球面DYffusionを用いた全球気候モデルの確率論的エミュレーション Probabilistic Emulation of a Global Climate Model with Spherical DYffusion
Salva Rühling Cachay, Brian Henn, Oliver Watt-Meyer, Christopher S. Bretherton, Rose Yu
arXiv last revised 13 Nov 2024 (this version, v2
DOI:https://doi.org/10.48550/arXiv.2406.14798
Abstract
Data-driven deep learning models are transforming global weather forecasting. It is an open question if this success can extend to climate modeling, where the complexity of the data and long inference rollouts pose significant challenges. Here, we present the first conditional generative model that produces accurate and physically consistent global climate ensemble simulations by emulating a coarse version of the United States’ primary operational global forecast model, FV3GFS. Our model integrates the dynamics-informed diffusion framework (DYffusion) with the Spherical Fourier Neural Operator (SFNO) architecture, enabling stable 100-year simulations at 6-hourly timesteps while maintaining low computational overhead compared to single-step deterministic baselines. The model achieves near gold-standard performance for climate model emulation, outperforming existing approaches and demonstrating promising ensemble skill. This work represents a significant advance towards efficient, data-driven climate simulations that can enhance our understanding of the climate system and inform adaptation strategies.
