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🌦️ AI Climate Forecasting: Neural GCM & GraphCast Simulator

Compare a neural-network weather model (GraphCast / NeuralGCM style) against a classic physics-based GCM on a live icosahedral forecast mesh: same accuracy, orders of magnitude less compute time.

Climate, Ecology & Environment3DModerate60 FPS

Details

CategoryClimate, Ecology & Environment

RenderingThree.js · WebGL

DifficultyModerate

Target FPS60

ControlsModel buttons (AI / Physics GCM) switch the forecast engine; horizon slider (1-10 days) sets how far ahead the run targets; playback-speed slider controls how fast the forecast day advances; mesh-resolution buttons (Coarse / Medium / Fine) subdivide the icosahedral grid (42 / 162 / 642 nodes), changing both visual detail and the physics solver's catch-up speed.

Three.jsAIGraphCastNeuralGCMclimate forecastinggraph neural networkInstancedMesh

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⚙ Under the hood

Compare a neural weather-forecasting model in the style of Google DeepMind's GraphCast/NeuralGCM against a classic physics-based GCM on a live icosahedral forecast mesh. Switch model, extend the horizon, and raise the mesh resolution to see why AI forecasts take seconds while physics solvers need hours.

Three.jsAIGraphCastNeuralGCMclimate forecastinggraph neural networkInstancedMesh

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install