『The AIGS Pod』のカバーアート

The AIGS Pod

The AIGS Pod

著者： The AIGS
無料で聴く

The AIGS podcast tackling AI in earth system modeling and more. 博物学 科学 自然・生態学
エピソード
  • Why AI weather models fail climate emulation

    Why AI weather models fail climate emulation
    2026/05/26
    A look at the growing body of work questioning where AI weather and climate models fall short, and why physics-based simulation remains essential. Papers discussed: Zhang et al. (2026), Physics-based models outperform AI weather forecasts of record-breaking extremes, Science Advances, https://www.science.org/doi/full/10.1126/sciadv.aec1433 — Smith & Thorpe (2026), The Primacy of Physical Simulation in the Age of AI: A Critique of ML for Weather Forecasting, Bulletin of the American Meteorological Society, https://journals.ametsoc.org/view/journals/bams/aop/BAMS-D-25-0214.1/BAMS-D-25-0214.1.xml — Scaife (2026), Successes and failures of current AI climate models, Geophysical Research Letters, https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2026GL122615 — Shaw & Stevens (2025), The other climate crisis, Nature, https://www.nature.com/articles/s41586-025-08680-1
    続きを読む 一部表示
    20 分
  • AI fixes climate model blind spots

    AI fixes climate model blind spots
    2026/04/28
    Climate simulations, at all grid resolutions, rely on approximations that encapsulate the forcing due to unresolved processes on resolved variables, known as parameterizations. Parameterizations often lead to inaccuracies in climate models, with significant biases in the physics of key climate phenomena. Advances in artificial intelligence (AI) are now directly enabling the learning of unresolved processes from data to improve the physics of climate simulations. This research introduces a flexible framework for developing and implementing physics- and scale-aware machine learning parameterizations within climate models, focusing on the ocean and sea-ice components of a state-of-the-art climate model by implementing a spectrum of data-driven parameterizations, ranging from complex deep learning models to more interpretable equation-based models. The results showcase the viability of AI-driven parameterizations in operational models, advancing the capabilities of a new generation of hybrid simulations, and include prototypes of fully coupled atmosphere-ocean-sea-ice hybrid simulations. The tools developed are open source, accessible, and available to all. Paper: https://arxiv.org/abs/2510.22676
    続きを読む 一部表示
    1 時間 2 分
  • AI fixes systematic climate model bias

    AI fixes systematic climate model bias
    2025/04/05
    Coarse resolution, imperfect parameterizations, and uncertain initial states and forcings limit Earth-system model (ESM) predictions. Traditional bias correction via data assimilation improves constrained simulations but offers limited benefit once models run freely. This research introduces an operator-learning framework that maps instantaneous model states to bias-correction tendencies and applies them online during integration. Building on a U-Net backbone, two operator architectures—Inception U-Net (IUNet) and a multi-scale network (M&M)—combine diverse upsampling and receptive fields to capture multiscale nonlinear features under Energy Exascale Earth System Model (E3SM) runtime constraints. Trained on two years of E3SM simulations nudged toward ERA5 reanalysis, the operators generalize across height levels and seasons. Both architectures outperform standard U-Net baselines in offline tests, indicating that functional richness rather than parameter count drives performance. In online hybrid E3SM runs, M&M delivers the most consistent bias reductions across variables and vertical levels. The ML-augmented configurations remain stable and computationally feasible in multi-year simulations, providing a practical pathway for scalable hybrid modeling. Paper: https://arxiv.org/abs/2512.03309v1
    続きを読む 一部表示
    23 分
adbl_web_anon_alc_button_suppression_t1
まだレビューはありません