GenCast
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Gencast
Overview :
GenCast is a new high-resolution (0.25°) AI ensemble model developed by Google DeepMind that is more accurate than the European Centre for Medium-Range Weather Forecasts (ECMWF) ENS system in predicting daily weather and extreme weather events, providing faster and more accurate forecasts up to 15 days in advance. This model is based on diffusion models and represents a type of generative AI model that has recently made rapid progress in image, video, and music generation. GenCast learns global weather patterns by analyzing historical weather data and can precisely generate complex probability distributions for future weather scenarios. The model's code, weights, and prediction results will be publicly released to support a wider weather forecasting community.
Target Users :
GenCast's target audience includes meteorologists, data scientists, renewable energy companies, and organizations focused on food safety and disaster response. These users can benefit from more accurate weather forecasts, such as timely and precise extreme weather alerts that protect more lives, avoid losses, and save costs, or by improving wind power generation predictions to enhance the reliability of wind energy as a sustainable resource.
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Use Cases
GenCast consistently surpasses ENS in predicting extreme high and low temperatures as well as high wind speeds.
For tropical cyclones (hurricanes and typhoons), GenCast provides superior path predictions.
In a principle validation experiment, GenCast was more accurate than ENS in predicting the total wind energy generation from global wind farm clusters.
Features
? Provides high-precision weather forecasts for up to 15 days.
? More accurate than existing top systems like ENS.
? Combines 50 or more forecasts representing potential weather trajectories.
? Adapts to the spherical geometry of the Earth to learn accurate probability distributions for future weather scenarios.
? Trained using forty years of historical weather data from ECMWF's ERA5 dataset.
? Outperform ENS in 97.2% of test targets, especially in forecasts beyond 36 hours.
? Rapidly generates predictions, with a single Google Cloud TPU v5 taking only 8 minutes to produce a 15-day forecast.
How to Use
1. Visit GenCast's GitHub page for code and weights release.
2. Download and install the required software and dependencies to run the model.
3. Configure the model using the provided code and weights according to the documentation.
4. Input the latest weather status data to generate probability distributions for future weather scenarios.
5. Analyze the multiple prediction outputs of the model to gain a comprehensive understanding of possible weather conditions.
6. Make decisions based on the predictions, such as preparing for extreme weather events or planning the use of renewable energy.
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