Artificial intelligence is transforming weather forecasting, a field long reliant on expensive supercomputers, complex physics-based models, and large teams of experts. The traditional method, known as numerical weather prediction, involves collecting data from weather stations, balloons, ships, and aircraft, feeding it into atmospheric models, and refining outputs with human input. This process is resource-intensive, costly, and energy-heavy. AI is now revolutionizing it by enabling faster, cheaper, and more accurate forecasts—sometimes running on laptops instead of massive systems. The global AI-powered weather forecasting market is projected to grow from $165.7 million in 2022 to $926.3 million by 2033. Major players like Google (with GenCast and WeatherNext), Microsoft (Aurora), The Weather Company (GRAF), and specialized institutes such as The Turing Institute (Aardvark) are leading the charge. Nvidia has emerged as a key force, launching Earth-2, a climate digital twin platform designed to simulate and visualize global weather and climate at scale. Earth-2 integrates forecasting, downscaling, and accuracy diagnostics, drawing from models like those from the European Centre for Medium-Range Weather Forecasts and Microsoft. At the American Meteorological Society’s 2024 annual meeting in Houston, Nvidia unveiled a new suite of open-source AI tools, models, and frameworks under its Earth-2 Studio. The goal is to democratize access to advanced forecasting, empowering countries and organizations to build sovereign, high-resolution weather systems without relying on centralized or expensive infrastructure. This shift is critical as extreme weather events cost the U.S. alone $182.7 billion in 2024 and over $1.4 trillion since 2015. Nvidia introduced three new models based on innovative architectures. Earth-2 Medium Range, powered by the “Atlas” architecture, forecasts up to 15 days ahead with high accuracy across over 70 weather variables. It outperforms Google’s GenCast in multiple metrics. Atlas reflects a move toward simpler, scalable transformer models—similar to those driving advances in drug discovery and robotics—replacing niche, hand-tuned AI systems. The Earth-2 Nowcasting model, built with the “StormScope” architecture, delivers high-resolution, kilometer-scale predictions for the next zero to six hours. This “critical window” is vital for emergency response, storm warnings, and disaster preparedness. Unlike traditional models, it learns directly from global satellite observations, enabling any nation with satellite coverage to create its own local forecasting system—without needing proprietary data archives. Another breakthrough is Earth-2 Global Data Assimilation, powered by the “HealDA” model. This system creates accurate snapshots of current atmospheric conditions—temperature, wind, pressure, humidity—at thousands of points worldwide, including gaps between observation sites. By running on GPUs, HealDA completes this task in minutes, compared to hours for supercomputers. This is significant because data assimilation traditionally consumes about half of all supercomputing resources in weather modeling, and it has been a major challenge for AI. These tools are already being tested by organizations including Israel’s Meteorological Service, The Weather Company, and financial firms like S&P Global Energy, which uses models for risk assessment. The open availability of these models on GitHub and Hugging Face ensures broad adoption across commercial and non-commercial sectors. AI is not just improving forecasts—it’s making them accessible, affordable, and adaptable. As climate change intensifies extreme weather, the ability to predict it accurately and quickly has never been more vital. With AI, the future of weather forecasting is faster, smarter, and more inclusive.