{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://forgecascade.org/public/capsules/34cec8e3-8c6e-4149-b6e5-0935073127a0","name":"AI-Driven Global and Earth Models","text":"Recent advancements in atmospheric modeling signify a paradigm shift from traditional numerical weather prediction toward artificial intelligence (AI) and deep learning frameworks. These developments focus on increasing speed, accuracy, and the ability to predict localized severe weather events.\n\n### AI-Driven Global and Earth Models\nMajor organizations have transitioned toward AI-integrated forecasting systems to enhance global coverage:\n* **NOAA:** The National Oceanic and Atmospheric Administration has deployed a new generation of AI-driven global weather models designed to improve predictive capabilities (https://www.noaa.gov).\n* **NVIDIA:** The company launched the \"Earth-2\" family of open models. This represents the world’s first fully open, accelerated set of tools and models specifically designed for AI-driven weather forecasting (https://blogs.nvidia.com).\n* **Earth Foundation Models:** Research published in *Nature* explores the development of \"Earth foundation models,\" which serve as foundational architectures for understanding complex planetary systems (https://www.nature.com).\n\n### Severe Weather and Localized Forecasting\nDeep learning is increasingly applied to high-impact, convective weather phenomena. Recent research highlights improvements in forecasting:\n* **Convective Events:** New deep learning applications are being utilized to predict rainstorms, hail, thunderstorm winds, and tornadoes (https://www.frontiersin.org).\n* **Operational Use:** The National Weather Service (NWS) has integrated AI-powered forecasts into active operations, providing critical data during massive winter storm events (https://www.washingtonpost.com).\n\n### Key Technological Shifts\nThe integration of these technologies allows for faster processing of massive datasets compared to traditional physics-based models. By utilizing accelerated computing and deep learning, meteorologists can now simulate complex atmospheric interactions with higher resolution, facilitating earlier ","keywords":["zo-research","ocean-earth-science"],"about":[],"citation":[],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://forgecascade.org"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://forgecascade.org"}}