by Amirpasha
“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.
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Publishing Since
1/2/2025
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April 18, 2025
<p>🎙️ <strong>Episode 23: FourCastNet – Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators</strong><br>🔗 DOI: <a href="https://doi.org/10.1145/3592979.3593412">https://doi.org/10.1145/3592979.3593412</a></p><p>🌍 <strong>Abstract</strong><br>As climate change intensifies extreme weather events, traditional numerical weather prediction (NWP) struggles to keep pace due to computational limits. This episode explores FourCastNet, a deep learning Earth system emulator that delivers high-resolution, medium-range global forecasts at unprecedented speed—up to five orders of magnitude faster than NWP—while maintaining near state-of-the-art accuracy.</p><p>📌 <strong>Bullet points summary</strong></p><ul><li><p><strong>FourCastNet outpaces traditional NWP</strong> with forecasts that are not only faster by several magnitudes but also comparably accurate, thanks to its data-driven deep learning approach.</p></li><li><p><strong>Powered by Adaptive Fourier Neural Operators (AFNO)</strong>, the model efficiently handles high-resolution data, leveraging spectral convolutions, model/data parallelism, and performance optimizations like CUDA graphs and JIT compilation.</p></li><li><p><strong>Scales excellently across supercomputers</strong> such as Selene, Perlmutter, and JUWELS Booster, reaching 140.8 petaFLOPS and enabling rapid training and large-scale ensemble forecasts.</p></li><li><p><strong>Addresses long-standing challenges</strong> in weather and climate modeling, including limits in resolution, complexity, and throughput, paving the way for emulating fine-scale Earth system processes.</p></li><li><p><strong>Enables "Interactivity at Scale"</strong>—supporting digital Earth twins and empowering users to explore future climate scenarios interactively, aiding science, policy, and public understanding.</p></li></ul><p>💡 <strong>The Big Idea</strong><br>FourCastNet revolutionizes weather forecasting by merging the power of deep learning and spectral methods, unlocking interactive, ultra-fast, and high-fidelity Earth system simulations for a changing world.</p><p>📖 <strong>Citation</strong><br>Kurth, Thorsten, et al. "Fourcastnet: Accelerating global high-resolution weather forecasting using adaptive fourier neural operators." Proceedings of the Platform for Advanced Scientific Computing Conference. 2023.</p><p><br></p>
April 11, 2025
<p>🎙️ <strong>Episode 22: Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems</strong><br>🔗 DOI: <a href="https://doi.org/10.1038/s41467-023-43860-5">https://doi.org/10.1038/s41467-023-43860-5</a></p><p>🧠 <strong>Abstract</strong><br>Improving the accuracy and scalability of carbon cycle quantification in agroecosystems is essential for climate mitigation and sustainable agriculture. This episode discusses a new Knowledge-Guided Machine Learning (KGML) framework that integrates process-based models, high-resolution remote sensing, and machine learning to address key limitations in conventional approaches.</p><p>📌 <strong>Bullet points summary</strong></p><ul><li><p>Introduces KGML-ag-Carbon, a hybrid model combining process-based simulation (ecosys), remote sensing, and ML to improve carbon cycle modeling in agroecosystems.</p></li><li><p>Outperforms traditional models in capturing spatial and temporal carbon dynamics across the U.S. Corn Belt, especially under data-scarce conditions.</p></li><li><p>Delivers high-resolution (250m daily) estimates for critical carbon metrics such as GPP, Ra, Rh, NEE, and crop yield, with field-level precision.</p></li><li><p>Benefits from pre-training with synthetic data, remote sensing assimilation, and a hierarchical architecture with knowledge-guided loss functions for better accuracy and interpretability.</p></li><li><p>Shows promise for broader applications including nutrient cycle modeling, large-scale carbon assessment, and scenario testing under various management and climate conditions.</p></li></ul><p>💡 <strong>The Big Idea</strong><br>KGML-ag-Carbon represents a leap in modeling agroecosystem carbon cycles, blending scientific knowledge with data-driven insights to unlock precision and scalability in climate-smart agriculture.</p><p>📖 <strong>Citation</strong><br>Liu, Licheng, et al. "Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems." Nature Communications 15.1 (2024): 357.</p><p><br></p>
April 4, 2025
<p>🎙️ <strong>Episode 21 — AtmoRep: A Stochastic Model of Atmospheric Dynamics Using Large-Scale Representation Learning</strong></p><p>This week, we explore <strong>AtmoRep</strong>, a novel task-independent AI model for simulating atmospheric dynamics. Built on large-scale representation learning and trained on ERA5 reanalysis data, AtmoRep delivers strong performance across a variety of tasks—without needing task-specific training.</p><p>🔍 Highlights from the episode:</p><ul><li><p>Introduction to <strong>AtmoRep</strong>, a stochastic computer model leveraging AI to simulate the atmosphere.</p></li><li><p><strong>Zero-shot capabilities</strong> for nowcasting, temporal interpolation, model correction, and generating counterfactuals.</p></li><li><p><strong>Outperforms or matches</strong> state-of-the-art models like Pangu-Weather and even ECMWF's IFS at short forecast horizons.</p></li><li><p><strong>Fine-tuning with additional data</strong>, like radar observations, enhances performance—especially for precipitation forecasts.</p></li><li><p>Offers a <strong>computationally efficient alternative</strong> to traditional numerical models, with potential for broader scientific and societal applications.</p></li></ul><p>📚 Read the paper: <a href="https://doi.org/10.48550/arXiv.2308.13280" target="_blank" rel="ugc noopener noreferrer">https://doi.org/10.48550/arXiv.2308.13280</a></p><p>✍️ Citation:<br />Lessig, Christian, et al. "AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning." arXiv:2308.13280 (2023)</p>
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