https://msucerl.org/cmse101/tags/graph-networks/Recent content in Graph-Networks on AI and Society CourseHugoen-usThu, 21 May 2026 00:00:00 +0000https://msucerl.org/cmse101/use-cases/5-1-climate-modeling-weather-prediction/Thu, 21 May 2026 00:00:00 +0000https://msucerl.org/cmse101/use-cases/5-1-climate-modeling-weather-prediction/<h1 id="climate-modeling--weather-prediction">Climate Modeling &amp; Weather Prediction</h1> <h2 id="context--systems-architecture">Context &amp; Systems Architecture</h2> <p>AI-driven atmospheric modeling has emerged as a disruptive paradigm shift threatening to overturn traditional numerical weather prediction (NWP). Historically, weather forecasting relied on massive supercomputers executing complex systems of physical fluid dynamics and thermodynamic differential equations. In 2023, Google DeepMind released <strong>GraphCast</strong>, a deep learning model capable of generating highly accurate 10-day global weather forecasts in under 60 seconds on a single GPU—matching or exceeding the predictive skill of the European Centre for Medium-Range Weather Forecasts (ECMWF), the historic global gold standard.</p>