Earthly Machine Learning

• Finding the Right XAI Method—A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science

  🎙️ Episode 20: Finding the Right XAI Method—Evaluating Explainable AI in Climate Science🔗 DOI: https://doi.org/10.48550/arXiv.2303.00652🧩 AbstractExplainable AI (XAI) methods are increasingly used in climate science, but the lack of ground truth explanations makes it difficult to evaluate and compare them effectively. This episode dives into a new framework for systematically evaluating XAI methods based on key properties tailored to climate research needs.📌 Bullet points summaryIntroduces XAI evaluation for climate science, offering a structured approach to assess and compare explanation methods using key desirable properties.Identifies five critical properties for XAI in this context: robustness, faithfulness, randomization, complexity, and localization.Evaluation shows that different XAI methods perform differently across these properties, with performance also depending on model architecture.Salience methods often score well on faithfulness and complexity but lower on randomization.Sensitivity methods typically do better on randomization but at the expense of other properties.Proposes a framework to guide method selection: assess the importance of each property for the research task, compute skill scores for available methods, and rank or combine methods accordingly.Highlights the role of benchmark datasets and evaluation metrics in supporting transparent and context-specific XAI adoption in climate science.💡 The Big IdeaThis work empowers climate researchers to make informed, task-specific choices in explainable AI, turning a fragmented XAI landscape into a guided, comparative process rooted in scientific needs.📖 CitationBommer, Philine Lou, et al. "Finding the right XAI method—A guide for the evaluation and ranking of explainable AI methods in climate science." Artificial Intelligence for the Earth Systems 3.3 (2024): e230074.

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• Pangu-Weather — Accurate medium-range global weather forecasting with 3D neural networks

  🎧 Abstract:Weather forecasting is essential for both science and society. This episode explores a breakthrough in medium-range global weather forecasting using artificial intelligence. The researchers introduce Pangu-Weather, an AI-powered system that leverages 3D deep networks with Earth-specific priors and a hierarchical temporal aggregation strategy to significantly enhance forecast accuracy and reduce error accumulation over time.📌 Bullet points summary:Pangu-Weather applies 3D deep learning with Earth-specific priors for accurate medium-range global weather forecasts.It utilizes a hierarchical temporal aggregation strategy to minimize accumulation errors.Outperforms ECMWF’s operational Integrated Forecasting System (IFS) in deterministic forecasting and tropical cyclone tracking.Achieves over 10,000× faster performance than IFS, enabling efficient large-member ensemble forecasts.Though trained on reanalysis data and limited in variable scope, Pangu-Weather presents a promising hybrid approach combining AI and traditional numerical weather prediction (NWP).💡 The Big Idea:AI is reshaping how we predict the weather. With Pangu-Weather, deep learning meets atmospheric science—delivering faster, more accurate forecasts that could redefine the future of meteorology.📚 Citation:Bi, K., Xie, L., Zhang, H. et al. Accurate medium-range global weather forecasting with 3D neural networks. Nature 619, 533–538 (2023). https://doi.org/10.1038/s41586-023-06185-3

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• GRAPHDOP — Towards Skillful Data-Driven Medium-Range Weather Forecasts

  🎧 Abstract:In this episode, we dive into GraphDOP, a novel data-driven forecasting system developed by ECMWF. Unlike traditional models, GraphDOP learns directly from Earth System observations—without relying on physics-based reanalysis. By capturing relationships between satellite and conventional observations, it builds a latent representation of Earth’s dynamic systems and delivers accurate weather forecasts up to five days ahead.📌 Bullet points summary:GraphDOP is developed by ECMWF and operates purely on observational data, without physics-based (re)analysis or feedback.Produces skillful forecasts for surface and upper-air parameters up to five days into the future.Competes with ECMWF’s IFS for two-metre temperature (t2m), outperforming it in the Tropics at 5-day lead times.Can generate forecasts at any time and location—even where observational data is sparse—without using gridded ERA5 fields for training.Combines data from various instruments to create accurate joint forecasts of surface and tropospheric temperatures in the Tropics.Learns observation relationships that generalize well to data-sparse regions, with upper-level wind forecasts aligning closely with ERA5 even in low-coverage areas.💡 The Big Idea:GraphDOP reimagines weather forecasting by proving that pure observational data—when paired with intelligent modeling—can rival and even surpass traditional, physics-based systems in both speed and accuracy.📚 Citation:Alexe, Mihai, et al. "GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations." arXiv preprint arXiv:2412.15687 (2024). https://doi.org/10.48550/arXiv.2412.15687

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• DiffDA — A Diffusion Model for Weather-Scale Data Assimilation

  🎧 Abstract:In this episode, we explore DiffDA, a novel data assimilation approach for weather forecasting and climate modeling. Built on the foundations of denoising diffusion models, DiffDA uses the pretrained GraphCast neural network to assimilate atmospheric variables from predicted states and sparse observations—providing a data-driven pathway to generate accurate initial conditions for forecasts.📌 Bullet points summary:Introduces DiffDA, a machine learning-based data assimilation method that leverages predicted states and sparse observations.Utilizes the pretrained GraphCast weather model, repurposed as a denoising diffusion model.Employs a two-phase conditioning strategy: on predicted states (training/inference) and sparse observations (inference only).Capable of generating assimilated global atmospheric data at 0.25° resolution.Demonstrates that initial conditions created via DiffDA retain forecast quality with a lead time degradation of at most 24 hours compared to top-tier assimilation systems.Enables autoregressive reanalysis dataset generation without full observation availability.💡 The Big Idea:DiffDA represents a step forward in data assimilation—merging the strengths of diffusion models and machine learning to produce accurate, observation-consistent initial conditions for future-focused forecasting.📚 Citation:Huang, Langwen, et al. "Diffda: a diffusion model for weather-scale data assimilation." arXiv preprint arXiv:2401.05932 (2024). https://doi.org/10.48550/arXiv.2401.059327

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• ARCHESWEATHER — An Efficient AI Weather Forecasting Model at 1.5º Resolution

  🎙️ Abstract:Embedding physical constraints as inductive priors is key in AI weather forecasting models. Locality—a common prior—relies on local neural interactions like 3D convolutions or attention. ARCHESWEATHER challenges this norm by introducing global vertical interactions, improving efficiency without sacrificing accuracy.📌 Bullet points summary:ARCHESWEATHER is a lightweight, efficient AI model trained at 1.5º resolution with minimal compute (a few GPU-days), offering low-cost inference and strong performance.The Cross-Level Attention (CLA) mechanism enables vertical atmospheric feature interactions, replacing 3D local attention with 2D horizontal attention and column-wise CLA in a 3D Swin U-Net with Earth-specific biases.Ensemble versions (MX4 and LX2) outperform or match IFS HRES and NeuralGCM in RMSE for 1–3 day forecasts on upper-air variables; it gains edge on wind variables at longer lead times.Fine-tuning on post-2007 ERA5 data yields modest gains, pointing to distributional shifts in the dataset.A convolutional head with bilinear upsampling avoids checkerboard artifacts, offering cleaner projections. The code is open-source.💡 Big Idea:ARCHESWEATHER shows that global vertical interactions via cross-level attention can outperform traditional locality-based models, paving a path toward more efficient, physically grounded weather forecasting systems.📚 Citation:Mukkavilli, S. Karthik, et al. "Ai foundation models for weather and climate: Applications, design, and implementation." arXiv preprint arXiv:2309.10808 (2023). DOI: 10.48550/arXiv.2405.14527

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