Google AI Introduces NeuralGCM: A New Machine Learning (ML) based Approach to Simulating Earth’s Atmosphere

Practical Solutions and Value

NeuralGCM, a hybrid model, combines differentiable solvers and machine-learning components to enhance stability, accuracy, and computational efficiency in weather and climate prediction.

Key Features

NeuralGCM integrates a differentiable dynamical core with a learned physics module, offering stable and accurate forecasts over various timescales. It gradually increases the rollout length from 6 hours to 5 days, accounting for interactions between learned physics and large-scale dynamics.

Performance Evaluation

NeuralGCM achieves comparable accuracy with best-in-class models for 1- to 15-day weather forecasts. In climate simulations, it accurately tracks climate metrics over multiple decades and simulates emergent phenomena like tropical cyclones, while providing notable computational savings.

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