## TL;DR
AI is revolutionizing weather forecasting -- models like GraphCast, Pangu-Weather, and FourCastNet can produce 10-day global forecasts in seconds that rival the accuracy of traditional supercomputer-based physics simulations that took hours. By 2025, the European Centre for Medium-Range Weather Forecasts is running AI forecasts operationally alongside classical models, marking the beginning of the data-driven weather prediction era.

## Core Explanation
Traditional Numerical Weather Prediction (NWP): solve the Navier-Stokes equations and atmospheric physics PDEs on a 3D grid covering the Earth using finite-difference or spectral methods. ECMWF IFS runs on ~1 million CPU cores, producing a 10-day forecast in 1-2 hours. It works well but is computationally expensive and limited by our incomplete understanding of sub-grid physics (cloud formation, turbulence). Data-driven approach: train a neural network on decades of historical weather data (ERA5 reanalysis -- 39 years, 0.25-degree resolution, hourly). The model learns the mapping from current atmospheric state to future state directly from data, bypassing explicit physics. Key architectural insight (GraphCast): the Earth is represented as a multi-scale icosahedral mesh (graph), with nodes at 0.25-degree resolution. The processor applies learned message-passing across edges to simulate atmospheric dynamics. Training: multi-step -- predict 6-hour steps, backpropagate through 6 steps to learn longer-range dynamics. Inference: autoregressive rollout.

## Detailed Analysis
Architecture comparison: GraphCast (Google) -- GNN on icosahedral mesh, 36.7M parameters, 1-minute forecast on TPUv4. Pangu-Weather (Huawei) -- 3D Earth-Specific Transformer (3DEST), processes atmosphere as 3D grid with 13 pressure levels, 64M parameters. FourCastNet (NVIDIA) -- Adaptive Fourier Neural Operator (AFNO), learns in frequency domain via FFT, 74M parameters. Performance: GraphCast outperforms IFS on 90% of targets for deterministic forecasts. For extreme events: GraphCast predicted Hurricane Lee making landfall in Nova Scotia 9 days in advance (vs. 6 days for IFS). ECMWF AIFS (2024-2026): the operational transition. ECMWF now runs GraphCast and other AI models as part of its ensemble prediction system, producing 50 perturbed forecasts for probabilistic prediction. AI models excel at medium-range (1-10 day) deterministic forecasts; physics-based models remain essential for ensembles, uncertainty quantification, and rare events. The future is hybrid -- AI for fast, accurate deterministic prediction + physics for calibration and extremes.