Google DeepMind launched GenCast, an AI-powered weather forecasting model that achieves unprecedented forecasting accuracy up to 15 days in advance. Designed specifically for Earth’s geometry, GenCast generates possible future climate scenarios by analyzing recent climate data and patterns learned from historical data spanning 1979 to 2018.
In tests comparing GenCast to the industry-leading Ensemble Forecast System (ENS), it outperformed ENS with accuracy 97.2% of the time, rising to 99.8% for forecasts longer than 36 hours. Specifically, GenCast he has succeeded in predicting extreme weather events such as tropical cyclones. It also boasts incredible efficiency: generating a 15-day weather forecast in just eight minutes using a single Google Cloud Tensor Processing Unit v5, compared to the hours required by traditional supercomputer-based models.
Despite its achievements, GenCast is not expected to replace meteorologists. The model relies on historical data, which may be less predictive in the context of climate change, and cannot account for all atmospheric variables. Predictions based on traditional physics and expert analysis are always important to ensure reliability.
GenCast joins other AI-driven weather tools, such as Nvidia’s FourCastNet and Huawei’s Pangu-Weather. Potential applications extend beyond meteorology, including renewable energy planning and disaster preparedness, where probability-based scenarios can inform resource allocation.
DeepMind plans to continue refining GenCast and integrating it into broader predictive applications. The model’s open access format will enable real-time and historical forecasts to match existing climate patterns. Although GenCast represents a significant improvement in predictive accuracy and efficiency, its role is viewed as a collaborative tool rather than a stand-alone solution.
Filed . Learn more about AI (Artificial Intelligence), Deepmind and Google.
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