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 challenges and quadratic computational complexity associated with the self-attention mechanism in Transformers when dealing with gigabyte-sized atmospheric data.• A novel parallelization scheme called Jigsaw parallelism is proposed, combining both domain parallelism and tensor parallelism to efficiently train multi-billion-parameter models. Jigsaw is optimized for large input data by fully sharding the data, model parameters, and optimizer states across devices, eliminating memory redundancy.
Jigsaw effectively mitigates hardware bottlenecks, particularly I/O-bandwidth limitations frequently encountered in training large scientific AI models. Due to its partitioned data loading (domain parallelism), the scheme achieves superscalar weak scaling in I/O-bandwidth-limited systems.
The method demonstrates excellent scaling behavior on high-performance computing systems, exceeding state-of-the-art performance in strong scaling in computation–communication-limited systems. The training was successfully scaled up to 256 GPUs, reaching peak performances of 9 and 11 PFLOPs.• Beyond hardware efficiency, Jigsaw improves predictive performance: by partitioning the model across more GPUs (model parallelism) instead of relying solely on data parallelism, it naturally enforces smaller global batch sizes, which empirically helps mitigate the problematic large-batch effects observed in AI weather models, leading to lower loss values.
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 Ren
XiChen 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 A100 GPU. This speed represents an acceleration exceeding 400-fold compared to the computational time required by operational Numerical Weather Prediction (NWP) systems.
The system is architected upon a foundation model that is initially pre-trained for weather forecasting and subsequently fine-tuned to function as both observation operators and DA models. Crucially, the integration of four-dimensional variational (4DVar) knowledge ensures that XiChen’s DA and medium-range forecasting accuracy rivals that of operational NWP systems.
XiChen demonstrates high scalability and robustness by employing a cascaded sequential DA framework to effectively assimilate both conventional observations (GDAS prepbufr) and raw satellite observations (AMSU-A and MHS). This design allows for the future integration of new observations simply by fine-tuning the respective observation operators and DA model components, which is critical for operational deployment.
In terms of performance, XiChen achieves a skillful weather forecasting lead time exceeding 8.25 days (with ACC of Z500 > 0.6). This result is comparable to the Global Forecasting System (GFS) and substantially surpasses the performance of other end-to-end AI-based global weather forecasting systems, such as Aardvark (less than 8 days) and GraphDOP (about 5 days).
A dual DA framework is implemented to operationalize XiChen as a continuous forecasting system. This framework utilizes separate 12-hour and 3-hour Data Assimilation Windows (DAW) to circumvent the multi-hour latency characteristic of high-resolution systems (like IFS HRES), thereby enabling the real-time acquisition of medium-range forecast products.
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 to demonstrate continuous cycling operation over a full one-year period.
• The system exhibits superior forecast accuracy in observation-sparse regions, outperforming traditional high-resolution forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF HRES) beyond day one in areas like central Africa and northern South America, despite utilizing substantially fewer observations
.• Globally, FuXi Weather delivers comparable 10-day forecast performance to ECMWF HRES, generating reliable forecasts at a 0.25° resolution and extending the skillful lead times for a number of key meteorological variables
.• FuXi Weather offers a cost-effective and physically consistent alternative to traditional Numerical Weather Prediction (NWP) systems. Its computational efficiency and reduced complexity are valuable for improving operational forecasts and enhancing climate resilience in regions with limited land-based observational infrastructure
.• This development challenges the prevailing view that standalone machine learning-based weather forecasting systems are not viable for operational use, demonstrating a significant step forward in the application of AI to real-world weather prediction.
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 Operator (SFNO) architecture.
• The model demonstrates significant improvements in climate model emulation, achieving near gold-standard performance. It substantially reduces climate biases compared to existing baselines, with errors often closer to the reference simulation’s noise floor. For example, it reduces climate biases to within 50% of the reference model, outperforming the next best baseline (ACE) by more than 2x.
• Spherical DYffusion enables stable and efficient long-term climate simulations, capable of 100-year simulations at 6-hourly timesteps with low computational overhead. It offers significant speed-ups (over 25x) and energy savings compared to the physics-based FV3GFS model it emulates.
• The method is particularly effective for ensemble climate simulations, accurately reproducing climate variability consistent with the reference model and further reducing climate biases through ensemble-averaging. The paper also highlights that short-term weather performance does not necessarily translate to accurate long-term climate statistics.
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 probabilistic nature of weather, resulting in stable spectra and realistic dynamics across multiple scales. The architecture is a purely convolutional neural network tailored for spherical geometry.
• Achieves superior forecasting accuracy and speed, surpassing leading conventional ensemble models and rivaling the best diffusion-based ML methods. FCN3 produces forecasts 8 to 60 times faster than these approaches; for instance, a 60-day global forecast at 0.25°, 6-hourly resolution is generated in under 4 minutes on a single GPU
.• Demonstrates exceptional physical fidelity and long-term stability, maintaining excellent probabilistic calibration and realistic spectra even at extended lead times of up to 60 days. This crucial achievement mitigates issues like blurring and the build-up of small-scale noise, which challenge other machine learning models, paving the way for physically faithful data-driven probabilistic weather models.
• Enables scalable and efficient operations through a novel training paradigm that combines model- and data-parallelism, allowing large-scale training on 1024 GPUs and more. All key components, including training and inference code, are fully open-source, providing transparent and reproducible tools for meteorological forecasting and atmospheric science research.
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. Abbot
Inability 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 weaker storms (Category 1-2) to accurately forecast these stronger, unseen events, often leading to dangerous "false negative" predictions. This limitation persists even if the training data includes strong extratropical cyclones, as their dynamics differ from TCs.
Limited Generalization Across Basins for Dynamically Similar Events: Despite the global extrapolation challenge, FourCastNet can demonstrate some ability to generalize learning across tropical basins for dynamically similar strong storms. This means that if the model has seen strong TCs in one ocean basin, it can apply that learned knowledge to forecast similar strong TCs in another basin, even if those specific events were excluded from the training data for that particular region.
Lack of Physical Consistency and Masked Performance: Current AI weather models, including FourCastNet, fail to reproduce key physical balances like the gradient-wind balance that TCs obey in real-world data, regardless of whether they were trained on full or reduced datasets. Furthermore, common evaluation metrics (e.g., anomaly correlation coefficient or root-mean-square error) can obscure these critical shortcomings by showing similar overall performance for general weather or less extreme events, highlighting the need for specialized tests for gray swans.
Implications and Future Directions: This research suggests that current AI weather models may provide unreliable early warnings for unprecedented extreme weather events, potentially leading to serious societal risks. It also indicates that AI climate emulators might mischaracterize extreme weather statistics for gray swans. The study emphasizes the urgent need for novel learning strategies (such as incorporating physics-based synthetic data or rare-event sampling algorithms) and rigorous testing methodologies to improve and reliably validate AI models for these high-impact, out-of-distribution events.
Probabilistic Emulation of a Global Climate Model with Spherical DYffusionby Salva Rühling Cachay, Brian Henn, Oliver Watt-Meyer, Christopher S. Bretherton, Rose Yu
"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 (FourCastNet, Pangu-Weather, GraphCast, FourCastNet-v2) demonstrate strong capabilities in capturing large-scale dynamical drivers vital for rapid storm development, such as the storm's position relative to upper-level jets. They also accurately reproduce the larger synoptic-scale structure of cyclones like Storm Ciarán, including the cloud head's position and the warm sector's shape.
Despite these strengths, AI models consistently underestimate the peak amplitude of winds, both at the surface and in the free atmosphere, associated with storms. They also struggle to resolve detailed structures crucial for issuing severe weather warnings, such as sharp bent-back warm frontal gradients, and show variable success in capturing warm core seclusion.
The underestimation of strong winds is not a consequence of the AI models' output resolution or their training data. This discrepancy persists even when compared against ERA5 (on which these models were trained) and numerical weather prediction (NWP) models of similar resolution, suggesting a more fundamental limitation in their ability to represent intense wind features.
The case study of Storm Ciarán highlights the pressing need for a more comprehensive assessment of machine learning weather forecasts. Moving beyond isolated error metrics to evaluate all relevant spatio-temporal features of physical phenomena is essential for identifying specific areas for improvement and fostering rapid advancements in this new and potentially transformative forecasting tool.
🧠 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
🧠 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
🌍 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
🎙️ 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:
🌍 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
🎙️ Episode 25: ClimaX: A foundation model for weather and climate
🌀 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).
🎙️ Episode 24: AI-empowered Next-Generation Multiscale Climate Modelling for Mitigation and Adaptation
🔗 DOI: https://doi.org/10.1038/s41561-024-01527-w
🌐 Abstract
Despite 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 summary
Existing 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 computing
Physics-aware machine learning to enhance hybrid models
Systematic use of Earth observations to constrain models
Modernized scientific infrastructure to operationalize insights
Aims 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 Idea
Integrating 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.
📖 Citation
Eyring, Veronika, et al. "AI-empowered next-generation multiscale climate modelling for mitigation and adaptation." Nature Geoscience 17.10 (2024): 963–971.
🎙️ Episode 23: FourCastNet – Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators
🔗 DOI: https://doi.org/10.1145/3592979.3593412
🌍 Abstract
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.
📌 Bullet points summary
FourCastNet outpaces traditional NWP with forecasts that are not only faster by several magnitudes but also comparably accurate, thanks to its data-driven deep learning approach.
Powered by Adaptive Fourier Neural Operators (AFNO), the model efficiently handles high-resolution data, leveraging spectral convolutions, model/data parallelism, and performance optimizations like CUDA graphs and JIT compilation.
Scales excellently across supercomputers such as Selene, Perlmutter, and JUWELS Booster, reaching 140.8 petaFLOPS and enabling rapid training and large-scale ensemble forecasts.
Addresses long-standing challenges in weather and climate modeling, including limits in resolution, complexity, and throughput, paving the way for emulating fine-scale Earth system processes.
Enables "Interactivity at Scale"—supporting digital Earth twins and empowering users to explore future climate scenarios interactively, aiding science, policy, and public understanding.
💡 The Big Idea
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.
📖 Citation
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.
🎙️ Episode 22: Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems
🔗 DOI: https://doi.org/10.1038/s41467-023-43860-5
🧠 Abstract
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.
📌 Bullet points summary
Introduces KGML-ag-Carbon, a hybrid model combining process-based simulation (ecosys), remote sensing, and ML to improve carbon cycle modeling in agroecosystems.
Outperforms traditional models in capturing spatial and temporal carbon dynamics across the U.S. Corn Belt, especially under data-scarce conditions.
Delivers high-resolution (250m daily) estimates for critical carbon metrics such as GPP, Ra, Rh, NEE, and crop yield, with field-level precision.
Benefits from pre-training with synthetic data, remote sensing assimilation, and a hierarchical architecture with knowledge-guided loss functions for better accuracy and interpretability.
Shows promise for broader applications including nutrient cycle modeling, large-scale carbon assessment, and scenario testing under various management and climate conditions.
💡 The Big Idea
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.
📖 Citation
Liu, Licheng, et al. "Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems." Nature Communications 15.1 (2024): 357.
🎙️ Episode 21 — AtmoRep: A Stochastic Model of Atmospheric Dynamics Using Large-Scale Representation Learning
This week, we explore AtmoRep, 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.
🔍 Highlights from the episode:
Introduction to AtmoRep, a stochastic computer model leveraging AI to simulate the atmosphere.
Zero-shot capabilities for nowcasting, temporal interpolation, model correction, and generating counterfactuals.
Outperforms or matches state-of-the-art models like Pangu-Weather and even ECMWF's IFS at short forecast horizons.
Fine-tuning with additional data, like radar observations, enhances performance—especially for precipitation forecasts.
Offers a computationally efficient alternative to traditional numerical models, with potential for broader scientific and societal applications.
📚 Read the paper: https://doi.org/10.48550/arXiv.2308.13280
✍️ Citation:
Lessig, Christian, et al. "AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning." arXiv:2308.13280 (2023)
- **DOI:**
https://doi.org/10.48550/arXiv.2303.00652
**Abstract:**
Explainable artificial intelligence (XAI) methods shed light on the predictions of machine learning algorithms. Several different approaches exist and have already been applied in climate science. However, usually missing ground truth explanations complicate their evaluation and comparison, subsequently impeding the choice of the XAI method. Therefore, in this work, we introduce XAI evaluation in the climate context and discuss different desired explanation properties, namely robustness, faithfulness, randomization, complexity, and localization....
**Bullet points summary:**
XAI evaluation is introduced to climate science to compare and assess the performance of explanation methods based on desirable properties, which can help climate researchers choose suitable XAI methods.The paper identifies and discusses five desirable properties of XAI methods: robustness, faithfulness, randomization, complexity, and localization. These properties are evaluated in the context of climate science using an established classification task.Different XAI methods exhibit varying strengths and weaknesses with respect to the five evaluation properties, and their performance can be architecture-dependent. For example, salience methods tend to show improvements in faithfulness and complexity but reduced randomization skill. Sensitivity methods, on the other hand, tend to have higher randomization skill scores, but sacrifice faithfulness and complexity skills.The paper proposes a framework for selecting an appropriate XAI method for a specific research task. This framework involves identifying essential XAI properties, calculating evaluation skill scores across these properties for different XAI methods, and then ranking or comparing the skill scores to determine the best-performing method or combination of methods.XAI evaluation can support researchers in choosing an explanation method, independent of the network structure and targeted to their specific research problem. The use of evaluation metrics alongside benchmark datasets contributes to the benchmarking of explanation methods.
**Citation:**
Bommer, 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.
- **DOI:**
https://doi.org/10.1038/s41586-023-06185-3
**Abstract:**
Weather forecasting is important for science and society. ... Here we introduce an artificial-intelligence-based method for accurate, medium-range global weather forecasting. We show that three-dimensional deep networks equipped with Earth-specific priors are effective at dealing with complex patterns in weather data, and that a hierarchical temporal aggregation strategy reduces accumulation errors in medium-range forecasting...
**Bullet points summary:**
Pangu-Weather, an AI-based weather forecasting system, uses 3D deep networks with Earth-specific priors to achieve accurate medium-range global weather forecasts.Pangu-Weather uses a hierarchical temporal aggregation strategy to reduce accumulation errors in medium-range forecasting.Pangu-Weather demonstrates stronger deterministic forecast results compared to the operational Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) on tested weather variables. It also shows improved accuracy in tracking tropical cyclones compared to ECMWF-HRES.The AI-based method of Pangu-Weather is more than 10,000 times faster than the operational IFS, offering opportunities for large-member ensemble forecasts with reduced computational costs.Pangu-Weather was trained and tested on reanalysis data and showed limitations, such as omitting certain weather variables and producing smoother forecast results. However, it shows the potential for combining AI-based and NWP methods for improved performance.
**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
