Earthly Machine Learning

Amirpasha
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“Earthly Machine Learning (EML)” offers AI-generated insights into cutting-edge machine learning research in weather and climate sciences. Powered by Google NotebookLM, each episode distils the essence of a standout paper, helping you decide if it’s worth a deeper look. Stay updated on the ML innovations shaping our understanding of Earth. It may contain hallucinations.

  1. 19/12/2025

    Differentiable and accelerated spherical harmonic and Wigner transforms

    Differentiable and accelerated spherical harmonic and Wigner transforms Matthew A. Price, Jason D. McEwen *Journal of Computational Physics (2024)* * This work introduces novel algorithmic structures for the **accelerated and differentiable computation** of generalized Fourier transforms on the sphere ($S^2$) and the rotation group ($SO(3)$), specifically spherical harmonic and Wigner transforms. * A key component is a **recursive algorithm for Wigner d-functions** designed to be stable to high harmonic degrees and extremely parallelizable, making the algorithms well-suited for high throughput computing on modern hardware accelerators such as GPUs. * The transforms support efficient computation of gradients, which is critical for machine learning and other differentiable programming tasks, achieved through a **hybrid automatic and manual differentiation approach** to avoid the memory overhead associated with full automatic differentiation. * Implemented in the open-source **S2FFT** software code (within the JAX differentiable programming framework), the algorithms support various sampling schemes, including equiangular samplings that admit exact spherical harmonic transforms. * Benchmarking results demonstrate **up to a 400-fold acceleration** compared to alternative C codes, and the transforms exhibit **very close to optimal linear scaling** when distributed over multiple GPUs, yielding an unprecedented effective linear time complexity (O(L)) given sufficient computational resources.

    13 min

  2. 11/12/2025

    Score-based diffusion nowcasting of GOES imagery

    Score-based diffusion nowcasting of GOES imagery *Randy J. Chase, Katherine Haynes, Lander Ver Hoef, Imme Ebert-Uphoff, a Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, b Electrical and Computer Engineering, Colorado State University, Fort Collins, CO* * The research explored score-based diffusion models to perform short-term forecasts (nowcasting) of GOES geostationary infrared satellite imagery (zero to three hours). This newer machine learning methodology combats the issue of **blurry forecasts** often produced by earlier neural network types, enabling the generation of clearer and more realistic-looking forecasts. * The **residual correction diffusion model (CorrDiff)** proved to be the best-performing model, quantitatively outperforming all other tested diffusion models, a traditional Mean Squared Error trained U-Net, and a persistence forecast by one to two kelvin on root mean squared error. * The diffusion models demonstrated sophisticated predictive capabilities, showing the ability to not only advect existing clouds but also to **generate and decay clouds**, including initiating convection, despite being initialized with only the past 20 minutes of satellite imagery. * A key benefit of the diffusion framework is the capacity for **out-of-the-box ensemble generation**, which enhances pixel-based metrics and provides useful uncertainty quantification where the spread of the ensemble generally correlates well to the forecast error. * However, the diffusion models are computationally intensive, with the Diff and CorrDiff models taking approximately five days to train on specialized hardware and about 10 minutes to generate a 10-member, three-hour forecast, compared to just 10 seconds for the baseline U-Net forecast.

    13 min

  3. 04/12/2025

    FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution

    FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution *Qiusheng Huang, Yuan Niu, Xiaohui Zhong, Anboyu Guo, Lei Chen, Dianjun Zhang, Xuefeng Zhang, Hao Li* --- * **First Data-Driven Sub-Daily Global Forecast:** FuXi-Ocean is the first deep learning-based global ocean forecasting model to achieve six-hour temporal resolution at an eddy-resolving 1/12° spatial resolution, with vertical coverage extending up to 1500 meters. This capability addresses a crucial need for high-frequency predictions that traditional numerical models struggle to deliver efficiently. * **Adaptive Temporal Modeling Innovation:** A key component of the model is the **Mixture-of-Time (MoT) module**, which adaptively integrates predictions from multiple temporal contexts based on variable-specific reliability. This mechanism is crucial for accommodating the diverse temporal dynamics of different ocean variables (e.g., fast-changing surface variables vs. slowly evolving deep-ocean processes) and effectively mitigates the accumulation of forecast errors in sequential prediction. * **Superior Performance and Efficiency:** The model demonstrates superior skill in predicting key variables (temperature, salinity, and currents) compared to state-of-the-art operational numerical forecasting systems (like HYCOM, BLK, and FOAM) at sub-daily intervals. Furthermore, it achieves this high performance with remarkable data efficiency, requiring only approximately 9 years of training data and relying solely on ocean variables (T, S, U, V, SSH) as input, without external data dependencies like atmospheric forcing. * **High-Impact Applications:** By providing accurate, high-resolution, sub-daily forecasts, FuXi-Ocean creates critical opportunities for maritime operations, including improved navigation, search and rescue, oil spill trajectory tracking, and enhanced marine resource management, particularly due to its comprehensive vertical coverage (0-1500 m).

    16 min

  4. 28/11/2025

    Beyond the Training Data: Confidence-Guided Mixing of Parameterizations in a Hybrid AI-Climate Model

    Beyond the Training Data: Confidence-Guided Mixing of Parameterizations in a Hybrid AI-Climate Model *By Helge Heuer, Tom Beucler, Mierk Schwabe, Julien Savre, Manuel Schlund, and Veronika Eyring* * This paper presents a **successful proof-of-concept for transferring a machine learning (ML) convection parameterization**—trained on the ClimSim dataset—to the ICON-A climate model. The resulting hybrid ML-physics model achieved stable and accurate simulations in long-term AMIP-style runs lasting at least 20 years. * A core innovation is the **confidence-guided mixing scheme**, which allows the Neural Network (NN) to predict its own error. When the NN's predicted confidence is low (e.g., in moist, unstable regimes or high-variability areas), its prediction is mixed with the conventional Tiedtke convection scheme. This mechanism improves reliability, prevents unphysical outputs by detecting potential extrapolation beyond the training domain, and makes the hybrid model tunable against observations. * The scheme's robustness and accuracy were further enhanced through the **use of a physics-informed loss function**—which encourages adherence to conservation laws like enthalpy and mass—and **noise-augmented training**. These techniques mitigate stability issues commonly faced by ML parameterizations and significantly improve physical consistency compared to purely data-driven models. * In evaluation against observational data, several hybrid configurations **outperformed the default Tiedtke scheme**, demonstrating improved precipitation statistics and showing a better representation of global climate variables. The confidence-guided approach demonstrated a fundamental change in the model's behavior, with the ML component contributing approximately 67% of the convective tendencies on average.

    15 min

  5. 23/11/2025

    Climate in a Bottle: Towards a Generative Foundation Model for the Kilometer-Scale Global Atmosphere

    Climate in a Bottle: Towards a Generative Foundation Model for the Kilometer-Scale Global Atmosphere (By Noah D. Brenowitz, Tao Ge, Akshay Subramaniam, Peter Manshausen, Aayush Gupta, David M. Hall, Morteza Mardani, Arash Vahdat, Karthik Kashinath, Michael S. Pritchard, NVIDIA * The paper introduces **Climate in a Bottle (cBottle)**, a generative diffusion-based AI framework capable of synthesizing full global atmospheric states at an unprecedented $\mathbf{5 \text{ km resolution}}$ (over 12.5 million pixels per sample). Unlike prevailing auto-regressive paradigms, cBottle samples directly from the full distribution of atmospheric states without requiring a previous time step, thereby avoiding issues like drifts and instabilities inherent to time-stepping models. * cBottle utilizes a **two-stage cascaded diffusion approach**: a global coarse-resolution generator conditioned on minimal climate-controlling inputs (such as monthly sea surface temperature and solar position), followed by a patch-based 16x super-resolution module. * The model demonstrates **foundational versatility** by being trained jointly on multiple data modalities, including ERA5 reanalysis and ICON global cloud-resolving simulations. This enables various zero-shot applications such as climate downscaling, channel infilling for missing or corrupted variables, bias correction between datasets, and translation between these modalities. * cBottle proposes a new form of **interactive climate modeling** through the use of guided diffusion. By training a classifier alongside the generator, users can steer the model to conditionally generate physically plausible **extreme weather events, such as Tropical Cyclones**, at specified locations on demand, circumventing the need to sift through petabytes of output to find rare events. * The model exhibits **high climate faithfulness** across a battery of tests, including reproducing diurnal-to-seasonal scale variability, large-scale modes of variability (like the Northern Annular Mode), and tropical cyclone statistics. Furthermore, it achieves **extreme distillation** by encapsulating massive datasets into a few GB of neural network weights, offering a 256x compression ratio per channel.

    14 min

  6. 07/11/2025

    Probabilistic Measures for Fair AI and NWP Model Comparison

    Probabilistic measures afford fair comparisons of AIWP and NWP model output (Tilmann Gneiting, Tobias Biegert, Kristof Kraus, Eva-Maria Walz, Alexander I. Jordan, Sebastian Lerch, June 10, 2025) Introduction of a New Fair Comparison Metric: The paper introduces the Potential Continuous Ranked Probability Score (PC), a new measure designed to allow fair and meaningful comparisons between single-valued output from data-driven Artificial Intelligence based Weather Prediction (AIWP) models and physics-based Numerical Weather Prediction (NWP) models. This approach addresses concerns that traditional loss functions (like RMSE) may unfairly favor AIWP models, which often optimize their training using these metrics.  Methodology Based on Probabilistic Postprocessing: PC is calculated by applying the same statistical postprocessing technique—specifically Isotonic Distributional Regression (IDR), also known as Easy Uncertainty Quantification (EasyUQ)—to the deterministic output of both AIWP and NWP models. PC is then defined as the mean Continuous Ranked Probability Score (CRPS) of these newly generated probabilistic forecasts.  Measure of Potential Skill and Invariance: PC quantifies potential predictive performance. A key property of PC is that it is invariant under strictly increasing transformations of the model output, treating both forecasts equally and facilitating comparisons where the pre-specification of a loss function might otherwise place competitors on unequal footings.  AIWP Outperformance and Operational Proxy: When applied to WeatherBench 2 data, the PC measure demonstrated that the data-driven GraphCast model outperforms the leading physics-based ECMWF high-resolution (HRES) model. Furthermore, the PC measure for the HRES model was found to align exceptionally well with the mean CRPS of the operational ECMWF ensemble, confirming that PC serves as a reliable proxy for the performance of real-time operational probabilistic products.

    13 min

  7. 24/10/2025

    Jigsaw: Training Multi-Billion-Parameter AI Weather Models With Optimized Model Parallelism

    Jigsaw: Training Multi-Billion-Parameter AI Weather Models With Optimized Model ParallelismAuthors: Deifilia Kieckhefen, Markus Götz, Lars H. Heyen, Achim Streit, and Charlotte Debus (Karlsruhe Institute of Technology, Helmholtz AI) The paper introduces WeatherMixer (WM), a multi-layer perceptron (MLP)-based architecture designed for atmospheric forecasting, which serves as a competitive alternative to Transformer-based models. WM's workload scales linearly with input size, addressing the scaling challenges and quadratic computational complexity associated with the self-attention mechanism in Transformers when dealing with gigabyte-sized atmospheric data.• A novel parallelization scheme called Jigsaw parallelism is proposed, combining both domain parallelism and tensor parallelism to efficiently train multi-billion-parameter models. Jigsaw is optimized for large input data by fully sharding the data, model parameters, and optimizer states across devices, eliminating memory redundancy.  Jigsaw effectively mitigates hardware bottlenecks, particularly I/O-bandwidth limitations frequently encountered in training large scientific AI models. Due to its partitioned data loading (domain parallelism), the scheme achieves superscalar weak scaling in I/O-bandwidth-limited systems.  The method demonstrates excellent scaling behavior on high-performance computing systems, exceeding state-of-the-art performance in strong scaling in computation–communication-limited systems. The training was successfully scaled up to 256 GPUs, reaching peak performances of 9 and 11 PFLOPs.• Beyond hardware efficiency, Jigsaw improves predictive performance: by partitioning the model across more GPUs (model parallelism) instead of relying solely on data parallelism, it naturally enforces smaller global batch sizes, which empirically helps mitigate the problematic large-batch effects observed in AI weather models, leading to lower loss values.

    14 min

  8. 18/10/2025

    XiChen: An observation-scalable fully AI-driven global weather forecasting system with 4D variational knowledge

    XiChen: An observation-scalable fully AI-driven global weather forecasting system with 4D variational knowledgeAuthors: Wuxin Wang, Weicheng Ni, Lilan Huang, Tao Han, Ben Fei, Shuo Ma, Taikang Yuan, Yanlai Zhao, Kefeng Deng, Xiaoyong Li, Boheng Duan, Lei Bai, Kaijun Ren XiChen is the first observation-scalable fully AI-driven global weather forecasting system. Its entire pipeline, from Data Assimilation (DA) to 10-day medium-range forecasting, can be accomplished within only 17 seconds using a single A100 GPU. This speed represents an acceleration exceeding 400-fold compared to the computational time required by operational Numerical Weather Prediction (NWP) systems.  The system is architected upon a foundation model that is initially pre-trained for weather forecasting and subsequently fine-tuned to function as both observation operators and DA models. Crucially, the integration of four-dimensional variational (4DVar) knowledge ensures that XiChen’s DA and medium-range forecasting accuracy rivals that of operational NWP systems.  XiChen demonstrates high scalability and robustness by employing a cascaded sequential DA framework to effectively assimilate both conventional observations (GDAS prepbufr) and raw satellite observations (AMSU-A and MHS). This design allows for the future integration of new observations simply by fine-tuning the respective observation operators and DA model components, which is critical for operational deployment.  In terms of performance, XiChen achieves a skillful weather forecasting lead time exceeding 8.25 days (with ACC of Z500 > 0.6). This result is comparable to the Global Forecasting System (GFS) and substantially surpasses the performance of other end-to-end AI-based global weather forecasting systems, such as Aardvark (less than 8 days) and GraphDOP (about 5 days).  A dual DA framework is implemented to operationalize XiChen as a continuous forecasting system. This framework utilizes separate 12-hour and 3-hour Data Assimilation Windows (DAW) to circumvent the multi-hour latency characteristic of high-resolution systems (like IFS HRES), thereby enabling the real-time acquisition of medium-range forecast products.

    16 min
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Información

“Earthly Machine Learning (EML)” offers AI-generated insights into cutting-edge machine learning research in weather and climate sciences. Powered by Google NotebookLM, each episode distils the essence of a standout paper, helping you decide if it’s worth a deeper look. Stay updated on the ML innovations shaping our understanding of Earth. It may contain hallucinations.

Ficha técnica

  • Creado por
    Amirpasha
  • Años en activo
    2 mil
  • Episodios
    44
  • Clasificación
    Apto
  • Derechos de autor
    © Amirpasha
  • Sitio web del podcast
    Earthly Machine Learning