Understanding AI Models for Global Weather Forecasting
Understanding AI Models for Global Weather Forecasting involves exploring how advanced machine learning architectures—such as graph neural networks, transformers, and Fourier operators—are revolutionizing the way we predict atmospheric conditions. Unlike traditional numerical weather prediction (NWP) models that rely heavily on physics-based simulations, AI models like GraphCast and FourCastNet learn directly from vast historical datasets to produce fast, accurate forecasts. These models can predict global weather patterns days in advance, often with lower computational costs and competitive or superior accuracy, making them powerful tools for enhancing resilience to climate extremes and improving operational forecasting.