The AIGS Pod

The AIGS
  • 5.0 (1)
  • Natural Sciences
  • Updated Biweekly

The AIGS podcast tackling AI in earth system modeling and more.

  1. Jul 26

    Aurora AI beats traditional weather models

    Aurora is a pioneering AI foundation model created by Microsoft to revolutionize Earth system forecasting by delivering high-resolution predictions with unprecedented speed and efficiency. Developed using a 3D Swin Transformer architecture, the model was pretrained on over one million hours of diverse geophysical data to learn the complex dynamics of the atmosphere. Through a process of fine-tuning, Aurora can be specialized for diverse tasks, including weather forecasting, air pollution tracking, ocean wave modeling, and tropical cyclone tracking. Research indicates that it consistently matches or outperforms state-of-the-art numerical models, such as those from the ECMWF, while operating at a computational cost orders of magnitude lower than traditional simulations. The most recent iteration, Aurora 1.5, introduces expanded variables and probabilistic ensemble forecasting to better quantify atmospheric uncertainty. By providing an open-source framework via GitHub, Microsoft aims to democratize access to accurate environmental data and support global efforts in disaster mitigation and climate science. Link: https://www.microsoft.com/en-us/research/project/aurora-forecasting/

    Aurora AI beats traditional weather models

  2. Jul 26

    Microsoft Aurora outperforms traditional weather models

    Aurora is a pioneering AI foundation model created by Microsoft to revolutionize Earth system forecasting by delivering high-resolution predictions with unprecedented speed and efficiency. Developed using a 3D Swin Transformer architecture, the model was pretrained on over one million hours of diverse geophysical data to learn the complex dynamics of the atmosphere. Through a process of fine-tuning, Aurora can be specialized for diverse tasks, including weather forecasting, air pollution tracking, ocean wave modeling, and tropical cyclone tracking. Research indicates that it consistently matches or outperforms state-of-the-art numerical models, such as those from the ECMWF, while operating at a computational cost orders of magnitude lower than traditional simulations. The most recent iteration, Aurora 1.5, introduces expanded variables and probabilistic ensemble forecasting to better quantify atmospheric uncertainty. By providing an open-source framework via GitHub, Microsoft aims to democratize access to accurate environmental data and support global efforts in disaster mitigation and climate science. Link: https://www.microsoft.com/en-us/research/project/aurora-forecasting/

    Microsoft Aurora outperforms traditional weather models

  3. Jul 26

    Aurora AI beats multi-billion dollar supercomputers

    Aurora is a pioneering AI foundation model created by Microsoft to revolutionize Earth system forecasting by delivering high-resolution predictions with unprecedented speed and efficiency. Developed using a 3D Swin Transformer architecture, the model was pretrained on over one million hours of diverse geophysical data to learn the complex dynamics of the atmosphere. Through a process of fine-tuning, Aurora can be specialized for diverse tasks, including weather forecasting, air pollution tracking, ocean wave modeling, and tropical cyclone tracking. Research indicates that it consistently matches or outperforms state-of-the-art numerical models, such as those from the ECMWF, while operating at a computational cost orders of magnitude lower than traditional simulations. The most recent iteration, Aurora 1.5, introduces expanded variables and probabilistic ensemble forecasting to better quantify atmospheric uncertainty. By providing an open-source framework via GitHub, Microsoft aims to democratize access to accurate environmental data and support global efforts in disaster mitigation and climate science. Link: https://www.microsoft.com/en-us/research/project/aurora-forecasting/

    Aurora AI beats multi-billion dollar supercomputers

  4. Jul 21

    NVIDIA ATLAS beats supercomputers on one GPU

    The recent revolution in data-driven methods for weather forecasting has lead to a fragmented landscape of complex, bespoke architectures and training strategies, obscuring the fundamental drivers of forecast accuracy. Here, we demonstrate that state-of-the-art probabilistic skill requires neither intricate architectural constraints nor specialized training heuristics. We introduce a scalable framework for learning multi-scale atmospheric dynamics by combining a directly downsampled latent space with a history-conditioned local projector that resolves high-resolution physics. We find that our framework design is robust to the choice of probabilistic estimator, seamlessly supporting stochastic interpolants, diffusion models, and CRPS-based ensemble training. Validated against the Integrated Forecasting System and the deep learning probabilistic model GenCast, our framework achieves statistically significant improvements on most of the variables. These results suggest scaling a general-purpose model is sufficient for state-of-the-art medium-range prediction, eliminating the need for tailored training recipes and proving effective across the full spectrum of probabilistic frameworks. Preprint: https://arxiv.org/abs/2601.18111

    NVIDIA ATLAS beats supercomputers on one GPU

  5. Jul 21

    NVIDIA ATLAS beats global weather supercomputers

    The recent revolution in data-driven methods for weather forecasting has lead to a fragmented landscape of complex, bespoke architectures and training strategies, obscuring the fundamental drivers of forecast accuracy. Here, we demonstrate that state-of-the-art probabilistic skill requires neither intricate architectural constraints nor specialized training heuristics. We introduce a scalable framework for learning multi-scale atmospheric dynamics by combining a directly downsampled latent space with a history-conditioned local projector that resolves high-resolution physics. We find that our framework design is robust to the choice of probabilistic estimator, seamlessly supporting stochastic interpolants, diffusion models, and CRPS-based ensemble training. Validated against the Integrated Forecasting System and the deep learning probabilistic model GenCast, our framework achieves statistically significant improvements on most of the variables. These results suggest scaling a general-purpose model is sufficient for state-of-the-art medium-range prediction, eliminating the need for tailored training recipes and proving effective across the full spectrum of probabilistic frameworks. Preprint: https://arxiv.org/abs/2601.18111

    NVIDIA ATLAS beats global weather supercomputers

  6. Jul 15

    Why climate AI needs synthetic data

    As deep learning for physical systems continues to grow in popularity, efforts to improve generalizability have primarily focused on designing architectures that embed physical constraints. However, for machine-learning surrogate climate models (emulators), research shows that the low structural diversity in existing scenarios commonly used to generate training data places a ceiling on predictive skill. This work examines whether training datasets themselves can be optimized to improve generalization. The authors introduce a method to create datasets that produce emulators capable of generalizing to new, structurally different scenarios absent from the training data. They use a differentiable Simple Climate Model (SCM) to calculate the sensitivity of emulator loss to perturbations in the training data, iteratively updating the training data to maximize emulator skill. Training on one scenario optimized in this fashion outperforms an emulator trained on six standard ScenarioMIP pathways while using a smaller dataset. The emulator successfully isolates distinct physical behaviors of different climate forcing agents (e.g., greenhouse gases vs. aerosols) without single-forcing runs. Results suggest that generating a small number of dynamically rich scenarios provides greater marginal value for emulation and characterizing system responses than expanding the suite of traditional emissions pathways. Paper: https://arxiv.org/abs/2606.19302v1

    Why climate AI needs synthetic data

  7. Jul 6

    The Tebaldi Test For Climate Emulators

    A comprehensive review of climate model emulation methods and their validation. This episode discusses the latest survey from Claudia Tebaldi, Noelle E. Selin, Rafael Ferrari, and Glenn Flierl, published in the Annual Review of Environment and Resources (2025). The paper surveys modern approaches to climate model emulation—surrogate models that replicate Earth System Model behavior at a fraction of the computational cost. Key topics include: statistical and machine learning emulation methods; trade-offs between model simplicity and accuracy; handling climate system internal variability; applications to anthropogenic climate forcing scenarios; and validation approaches for high-dimensional climate outputs. The Tebaldi Test represents a critical benchmark for evaluating whether emulators can generalize beyond their training data and maintain physical consistency under extreme climate scenarios. Paper: https://www.annualreviews.org/content/journals/10.1146/annurev-environ-012125-085838

    The Tebaldi Test For Climate Emulators

  8. Jun 30

    Solving climate emulator physics with random CO2

    Machine-learning weather-climate emulators like the Ai2 Climate Emulator (ACE) can reproduce the climate of recent decades, but they break down in unfamiliar conditions, producing unphysical results when asked to simulate a world with sea surface temperatures warmed by +4 K or an abrupt quadrupling of CO2. This research traces the problem to training data in which sea surface temperature (SST) and CO2 are tightly correlated, leaving the models unable to learn their separate effects. To fix this, the authors introduce a new class of "random-CO2" reference simulations: deliberately stitched-together climates in which SST and CO2 are prescribed to vary independently. By training on a balance of AMIP, equilibrium-climate, and random-CO2 data, and adding a total energy conservation constraint for improved interpretability, they build a more data-efficient emulator that not only matches earlier models where they already excelled, but also handles previously failing scenarios like AMIP +4 K and slab-ocean-coupled abrupt 4xCO2. The model still relies on simplified or prescribed ocean, land, and sea-ice components and inherits biases from its physics-based training data, each pointing to clear avenues for future work. Paper: https://arxiv.org/abs/2606.07928

    Solving climate emulator physics with random CO2
See All (14)

About

The AIGS podcast tackling AI in earth system modeling and more.

Information

  • Creator
    The AIGS
  • Years Active
    2026
  • Episodes
    14
  • Rating
    Clean
  • Show Website
    The AIGS Pod