Earthly Machine Learning

• Artificial intelligence for modeling and understanding extreme weather and climate events

  🌍 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 heatwaves, and plays a growing role in attribution and risk assessment.⚠️ Key challenges include limited data, lack of annotations, and the complexity of defining extremes, all of which demand robust, flexible AI approaches that perform well under novel conditions.🧠 Trustworthy AI is critical for safety-related decisions, requiring transparency, interpretability (XAI), causal inference, and uncertainty quantification.📢 The “last mile” focuses on operational use and risk communication, ensuring AI outputs are accessible, fair, and actionable in early warning systems and public alerts.🤝 Cross-disciplinary collaboration is vital—linking AI developers, climate scientists, field experts, and policymakers to build practical and ethical AI tools that serve real-world needs.💡 Big idea:AI holds powerful promise for extreme climate analysis—but only if it's built to be trustworthy, explainable, and operationally useful in the face of uncertainty.📚 Citation:Camps-Valls, Gustau, et al. "Artificial intelligence for modeling and understanding extreme weather and climate events." Nature Communications 16.1 (2025): 1919.https://doi.org/10.1038/s41467-025-56573-8

  --------  

  20:01
• Fixing the Double Penalty in Data-Driven Weather Forecasting Through a Modified Spherical Harmonic Loss Function

  🎙️ 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, limiting resolution and underestimating extremes.⚙️ The proposed Adjusted Mean Squared Error (AMSE) loss function addresses this by separating decorrelation and amplitude errors, improving spectrum fidelity.📈 Fine-tuning GraphCast with AMSE boosts resolution dramatically (from 1,250km to 160km), enhances ensemble spread, and sharpens forecasts of cyclones and surface winds.🔬 This shows deterministic forecasts can remain sharp and realistic without explicitly modeling ensemble uncertainty.Redefining the loss function in data-driven weather forecasting can drastically sharpen predictions and enhance realism—without adding complexity or parameters.📚 Citation:https://doi.org/10.48550/arXiv.2501.19374🔍 Bullet points summary:💡 Big idea:

  --------  

  16:37
• Climate-invariant machine learning

  🌍 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 this gap, the authors propose a “climate-invariant” ML framework, incorporating knowledge of climate processes into ML algorithms, and show that this approach enhances generalization across different climate regimes.📌 Key Points:Highlights how ML models in climate science struggle to generalize beyond their training data, limiting their utility in future climate projections.Introduces a "climate-invariant" ML framework, embedding physical climate process knowledge into ML models through feature transformations of input and output data.Demonstrates that neural networks with climate-invariant design generalize better across diverse climate conditions in three atmospheric models, outperforming raw-data ML approaches.Utilizes explainable AI methods to show that climate-informed mappings learned by neural networks are more spatially local, improving both interpretability and data efficiency.💡 The Big Idea:Combining machine learning with physical insights through a climate-invariant approach enables models that not only learn from data but also respect the underlying physics—paving the way for more reliable and generalizable climate projections.📖 Citation:Beucler, Tom, et al. "Climate-invariant machine learning." Science Advances 10.6 (2024): eadj7250. DOI: 10.1126/sciadv.adj7250

  --------  

  12:37
• ClimaX: A foundation model for weather and climate

  🎙️ 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 climate datasets but often lack flexibility and generalization. ClimaX introduces a versatile and generalizable deep learning model for weather and climate science, capable of learning from diverse, heterogeneous datasets that cover various variables, time spans, and physical contexts.📌 Bullet points summary:ClimaX is a flexible foundation model for weather and climate, overcoming the rigidity of physics-based models and the narrow focus of traditional ML approaches by training on heterogeneous datasets.The model utilizes Transformer-based architecture with novel variable tokenization and aggregation mechanisms, allowing it to handle diverse climate data efficiently.Pre-trained via a self-supervised randomized forecasting objective on CMIP6-derived datasets, ClimaX learns intricate inter-variable relationships, enhancing its adaptability to various forecasting tasks.Demonstrates strong, often state-of-the-art performance across tasks like multi-scale weather forecasting, climate projections (ClimateBench), and downscaling — sometimes outperforming even operational systems like IFS.The study highlights ClimaX's scalability, showing performance gains with more pretraining data and higher resolutions, underscoring its potential for future developments with increased data and compute resources.💡 Big idea:ClimaX represents a shift toward foundation models in climate science, offering a single, adaptable architecture capable of generalizing across a wide array of weather and climate modeling tasks — setting the stage for more efficient, data-driven climate research.📖 Citation:Nguyen, Tung, et al. "Climax: A foundation model for weather and climate." arXiv preprint arXiv:2301.10343 (2023).

  --------  

  13:25
• AI-empowered Next-Generation Multiscale Climate Modelling for Mitigation and Adaptation

  🎙️ Episode 24: AI-empowered Next-Generation Multiscale Climate Modelling for Mitigation and Adaptation🔗 DOI: https://doi.org/10.1038/s41561-024-01527-w🌐 AbstractDespite decades of progress, Earth system models (ESMs) still face significant gaps in accuracy and uncertainty, largely due to challenges in representing small-scale or poorly understood processes. This episode explores a transformative vision for next-generation climate modeling—one that embeds AI across multiple scales to enhance resolution, improve model fidelity, and better inform climate mitigation and adaptation strategies.📌 Bullet points summaryExisting ESMs struggle with inaccuracies in climate projections due to subgrid-scale and unknown process limitations.A new approach is proposed that blends AI with multiscale modeling, combining fine-resolution simulations with coarser hybrid models that capture key Earth system feedbacks.This strategy is built on four pillars:Higher resolution via advanced computingPhysics-aware machine learning to enhance hybrid modelsSystematic use of Earth observations to constrain modelsModernized scientific infrastructure to operationalize insightsAims to deliver faster, more actionable climate data to support urgent policy needs for both mitigation and adaptation.Envisions hybrid ESMs and interactive Earth digital twins, where AI helps simulate processes more realistically and supports climate decision-making at scale.💡 The Big IdeaIntegrating AI into climate models across scales is not just an upgrade—it’s a shift towards smarter, faster, and more adaptive climate science, essential for responding to the climate crisis with precision and urgency.📖 CitationEyring, Veronika, et al. "AI-empowered next-generation multiscale climate modelling for mitigation and adaptation." Nature Geoscience 17.10 (2024): 963–971.

  --------  

  17:49

Más podcasts de Ciencias

Podcasts a la moda de Ciencias

Acerca de Earthly Machine Learning