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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A comprehesive survey about foundation models for weather and cliamte data understanding.
| Date | Stars |
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| 2026-07-31 | 294 |
| 2026-08-06 | 294 |
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<div align="center"> # 🌍 Awesome Large Foundation Models/Task-Specific Models for Weather and Climate [](https://awesome.re)   A professionally curated list of **Large Foundation Models/Task-Specific Models for Weather and Climate Data Understanding (e.g., time-series, spatio-temporal series, video streams, graphs, and text)** with awesome resources (paper, code, data, etc.), which aims to comprehensively and systematically summarize the recent advances to the best of our knowledge. [Paper](#paper) • [Resources](#resources) • [Models](#models) • [Contributing](#contributing) </div> ## 📢 Updates - **[2025-01]** 📄 Looking for contributors to help reshape our survey paper. Join us as a co-author! [Contact us](#[email protected]) - **[2024-09]** 🎉 Our paper ["Personalized Adapter for Large Meteorology Model on Devices"](https://arxiv.org/abs/2405.20348) has been accepted by **NeurIPS 2024**! - **[2023-12]** 📚 Released our comprehensive survey: ["Foundation Models for Weather and Climate Data Understanding"](https://arxiv.org/pdf/2312.03014.pdf) >**Abstract**: Recent advances in deep learning (DL) have significantly enhanced our capability to analyze and interpret weather and climate data, especially at fine spatio-temporal scales, helping unravel the chaotic and nonlinear patterns of Earth's systems. The emergence of Foundation Models, particularly Large Language Models (LLMs), has catalyzed advances in Artificial General Intelligence, delivering outstanding outcomes across various tasks through fine-tuning. The success of LLMs presents a novel opportunity to rethink the task of weather and climate data understanding: **Is it possible to utilize or evolve Foundation Models for weather and climate data to enhance the accuracy of task completion?** This survey evaluates the potential of adapting Foundation Models to enhance weather and climate data analysis. We present a concise, up-to-date review of cutting-edge AI techniques tailored for this domain, concentrating on time series and textual information. We cover four key areas: data types, model architectures, application scopes, and task-specific datasets. Furthermore, we address prevailing challenges, provide insights, and outline future research directions, empowering practitioners to advance the field. The survey distills the latest innovations in data-driven models, underscoring foundational strength, progress, applications, resources, and research frontiers, thus offering a roadmap for transformative advancements in weather and climate data understanding. ## 🌟 Highlights - **Comprehensive Coverage**: Time series, textual data, model architectures, applications - **Up-to-date Resources**: Latest papers, code implementations, datasets - **Practical Insights**: Challenges, opportunities, future directions - **Community Driven**: Open for contributions and collaborations ## 📚 Resources ### Large Foundation Models for Weather and Climate >**Definition**: *Pre-trained from large-scale weather/climate dataset and able to perform various weather/cliamte-related tasks.* | Publication | Venue | Year | Resource | |:------|:----:|---------:|---------:| | Neural general circulation models for weather and climate | *Nature* | 2024 | [\[paper\]](https://www.nature.com/articles/s41586-024-07744-y) [\[code\]](https://arxiv.org/abs/2411.05420)| | Prithvi WxC: Foundation Model for Weather and Climate | *arXiv* | 2024 | [\[paper\]](https://arxiv.org/abs/2411.05420) [\[code\]](https://github.com/NASA-IMPACT/Prithvi-WxC)| | WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning | *NeurIPS* | 2024 | [\[paper\]](https://arxiv.org/abs/2409.13598) [\[code\]](https://github.com/NASA-IMPACT/Prithvi-WxC)| | Aurora: A Foundation Model
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matched fp:bbc955381c7171e7, topic:large-language-models, topic:foundation-models
matched fp:bbc955381c7171e7, topic:deep-learning
matched fp:bbc955381c7171e7, topic:representation-learning
matched fp:bbc955381c7171e7, topic:dataset