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PODCAST

Earthly Machine Learning

“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.

science earth sciences

All Episodes

00:13:02

Differentiable and accelerated spherical harmonic and...

Earthly Machine Learning ·

2025/12/19

Differentiable and accelerated spherical harmonic and Wigner transformsMatthew 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

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00:12:46

Score-based diffusion nowcasting of GOES imagery

Earthly Machine Learning ·

2025/12/11

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

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00:15:57

FuXi-Ocean: A Global Ocean Forecasting System with...

Earthly Machine Learning ·

2025/12/04

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

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00:15:07

Beyond the Training Data: Confidence-Guided Mixing of...

Earthly Machine Learning ·

2025/11/28

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

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00:13:50

Climate in a Bottle: Towards a Generative Foundation...

Earthly Machine Learning ·

2025/11/23

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

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00:13:09

Probabilistic Measures for Fair AI and NWP Model...

Earthly Machine Learning ·

2025/11/07

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

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00:13:36

Jigsaw: Training Multi-Billion-Parameter AI Weather...

Earthly Machine Learning ·

2025/10/24

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

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00:16:08

XiChen: An observation-scalable fully AI-driven...

Earthly Machine Learning ·

2025/10/18

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 RenXiChen 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

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00:13:52

FuXi Weather : A data-to-forecast machine learning...

Earthly Machine Learning ·

2025/10/03

A data-to-forecast machine learning system for global weather Xiuyu Sun et al. (2025). A data-to-forecast machine learning system for global weather. Nature Communications, https://doi.org/10.1038/s41467-025-62024-1• FuXi Weather is introduced as a groundbreaking end-to-end machine learning system for global weather forecasting. It autonomously performs data assimilation and forecasting in a 6-hour cycle, directly processing raw multi-satellite observations, and notably, it is the first such system

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00:19:53

Probabilistic Emulation of a Global Climate Model...

Earthly Machine Learning ·

2025/09/25

Probabilistic Emulation of a Global Climate Model with Spherical DYffusion Salva Rühling Cachay, Brian Henn, Oliver Watt-Meyer, Christopher S. Bretherton, Rose Yu• This paper introduces Spherical DYffusion, the first conditional generative model designed for the probabilistic emulation of a global climate model. It achieves accurate and physically consistent global climate ensemble simulations by combining the dynamics-informed diffusion framework (DYffusion) with the Spherical Fourier Neural

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00:17:39

FourCastNet 3: A geometric approach to probabilistic...

Earthly Machine Learning ·

2025/09/16

FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale Boris Bonev, Thorsten Kurth, Ankur Mahesh, Mauro Bisson, Jean Kossaifi, Karthik Kashinath, Anima Anandkumar, William D. Collins, Michael S. Pritchard, and Alexander Keller• FourCastNet 3 (FCN3) introduces a pioneering geometric machine learning approach for probabilistic ensemble weather forecasting. It is designed to respect spherical geometry and accurately model the spatially correlated

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00:16:27

Can AI weather models predict out-of-distribution...

Earthly Machine Learning ·

2025/08/16

Can AI weather models predict out-of-distribution gray swan tropical cyclones?by Y. Qiang Sun, Pedram Hassanzadeh, Mohsen Zand, Ashesh Chattopadhyay, Jonathan Weare, and Dorian S. AbbotInability to Extrapolate to Gray Swans Globally: AI weather models like FourCastNet struggle to predict "gray swan" tropical cyclones (TCs), which are rare, strong, and absent from training data. When Category 3-5 TCs are entirely removed from the global training dataset, the model cannot extrapolate from

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00:16:05

Probabilistic Emulation of a Global Climate Model...

Earthly Machine Learning ·

2025/08/09

Probabilistic Emulation of a Global Climate Model with Spherical DYffusionby Salva Rühling Cachay, Brian Henn, Oliver Watt-Meyer, Christopher S. Bretherton, Rose YuThe paper introduces Spherical DYffusion, the first conditional generative model for probabilistic emulation of a realistic global climate model, offering efficient and accurate climate ensemble simulations.It demonstrates that weather forecasting performance is not a strong indicator of long-term climate performance, a crucial insight

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00:14:28

Do AI models produce better weather forecasts than...

Earthly Machine Learning ·

2025/08/03

"Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán" By Andrew J. Charlton-Perez, Helen F. Dacre, Simon Driscoll, Suzanne L. Gray, Ben Harvey, Natalie J. Harvey, Kieran M. R. Hunt, Robert W. Lee, Ranjini Swaminathan, Remy Vandaele & Ambrogio Volonté. Published in partnership with CECCR at King Abdulaziz University, Nature, DOI: 10.1038/s41612-024-00638-w.Here are the main takeaways from the paper:• AI models

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00:16:34

Early Warning of Complex Climate Risk with Integrated...

Earthly Machine Learning ·

2025/07/04

🧠 Abstract:Climate change is increasing the frequency and severity of disasters, demanding more effective Early Warning Systems (EWS). While current systems face hurdles in forecasting, communication, and decision-making, this episode examines how integrated Artificial Intelligence (AI) can revolutionize risk detection and response.📌 Bullet points summary:Current EWS struggle with forecasting accuracy, impact prediction across diverse contexts, and effective communication with affected

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00:20:29

On Some Limitations of Current Machine Learning...

Earthly Machine Learning ·

2025/06/27

🧠 Abstract:Machine Learning (ML) is increasingly influential in weather and climate prediction. Recent advances have led to fully data-driven ML models that often claim to outperform traditional physics-based systems. This episode evaluates forecasts from three leading ML models—Pangu-Weather, FourCastNet, and GraphCast—focusing on their accuracy and physical realism.📌 Bullet points summary:ML models like Pangu-Weather, FourCastNet, and GraphCast fail to capture sub-synoptic and mesoscale

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00:20:01

Artificial intelligence for modeling and...

Earthly Machine Learning ·

2025/06/15

🌍 Abstract:Artificial intelligence (AI) is transforming Earth system science, especially in modeling and understanding extreme weather and climate events. This episode explores how AI tackles the challenges of analyzing rare, high-impact phenomena using limited, noisy data—and the push to make AI models more transparent, interpretable, and actionable.📌 Bullet points summary:🌪️ AI is revolutionizing how we model, detect, and forecast extreme climate events like floods, droughts, wildfires, and

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00:16:37

Fixing the Double Penalty in Data-Driven Weather...

Earthly Machine Learning ·

2025/06/08

🎙️ Abstract:Recent progress in data-driven weather forecasting has surpassed traditional physics-based systems. Yet, the common use of mean squared error (MSE) loss functions introduces a “double penalty,” smoothing out fine-scale structures. This episode discusses a simple, parameter-free fix to this issue by modifying the loss to disentangle decorrelation errors from spectral amplitude errors.🌪️ Data-driven weather models like GraphCast often produce overly smooth outputs due to MSE loss,

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00:12:37

Climate-invariant machine learning

Earthly Machine Learning ·

2025/05/09

🌍 Abstract:Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than the model grid size, which remain the main source of projection uncertainty. Recent machine learning (ML) algorithms offer promise for improving these process representations but often extrapolate poorly outside their training climates. To bridge

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00:13:25

ClimaX: A foundation model for weather and climate

Earthly Machine Learning ·

2025/05/02

🎙️ Episode 25: ClimaX: A foundation model for weather and climateDOI: https://doi.org/10.48550/arXiv.2301.10343🌀 Abstract:Most cutting-edge approaches for weather and climate modeling rely on physics-informed numerical models to simulate the atmosphere's complex dynamics. These methods, while accurate, are often computationally demanding, especially at high spatial and temporal resolutions. In contrast, recent machine learning methods seek to learn data-driven mappings directly from curated

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44 results

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