Earthly Machine Learning

• Early Warning of Complex Climate Risk with Integrated Artificial Intelligence

  🧠 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 communities.Integrated AI and Foundation Models (FMs) enhance EWS by improving forecast precision, offering impact-specific alerts, and utilizing diverse data sources—from weather to social media.Foundation Models for geospatial and meteorological data, combined with natural language processing, pave the way for user-adaptive, intuitive warning systems, including chatbots and realistic visualizations.Ensuring equity and effectiveness in AI-driven EWS requires addressing data bias, robustness, ownership issues, and power dynamics—guided by FATES principles and supported by open-source tools, global cooperation, and digital inclusivity.💡 The Big Idea:Integrated AI holds the key to transforming climate early warning—from hazard alerts to adaptive, inclusive, and impact-driven systems that empower communities worldwide.📖 Citation:Reichstein, Markus, et al. "Early warning of complex climate risk with integrated artificial intelligence." Nature Communications 16.1 (2025): 2564. https://doi.org/10.1038/s41467-025-57640-w

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• On Some Limitations of Current Machine Learning Weather Prediction Models

  🧠 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 phenomena with adequate fidelity, producing forecasts that become overly smooth over time.Their energy spectra diverge significantly from traditional models and reanalysis data, leading to poor representation of features below 300–400 km scales.They lack accurate representation of key physical balances in the atmosphere, such as geostrophic wind balance and the divergent-rotational wind ratio, affecting the realism of weather diagnostics.Though computationally efficient and strong in certain metrics, these models should be seen as forecast refiners rather than full-fledged atmospheric simulators or "digital twins," as they still rely heavily on traditional models for training and input.💡 The Big Idea:While ML models mark a significant advancement, their current limitations highlight the indispensable role of physical principles and traditional modeling in weather prediction.📖 Citation:Bonavita, Massimo. "On some limitations of current machine learning weather prediction models." Geophysical Research Letters 51.12 (2024): e2023GL107377. https://doi.org/10.1029/2023GL107377

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• 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

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• 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:

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• 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

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