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A Paradigm Shift in Weather Forecasting with : How GraphCast Could Save Lives
By Alessandra Mazz March 18, 2024
has transformed the landscape of medium-range weather forecasting by providing faster and more accurate predictions across global scales using fewer computational resources. With Remi Lam leading the charge from Google DeepMind, GraphCast - an open-source -learning model - demonstrates howcan revolutionize our understanding of Earth's climate dynamics.
GraphCast employs and Graph Neural Networks GNNs to offer 10-day weather forecasts that take under a minute to compute using specialized hardware such as Google Cloud Tensor Processing Units v4 TPU v4. This breakthrough capability stands in stark contrast to the traditional Numerical Weather Predictions NWP, which rely on complex mathematicaland require vast computational resources.
In recent years, advancements in forecasting accuracy have enabled the prediction of extreme events well ahead of time. GraphCast enhances this predictive power by analyzing historical weather data from ECMWF's ERA5 dataset over several decades, allowing it to learn cause-and-effect relationships that govern how Earth's climate evolves into future scenarios. This approach not only improves forecast accuracy but also optimizes computational resources.
According to CMCC Scientific Director Giulio Boccaletti, Deep learning represents a paradigm shift in Earth Systems Science and Meteorology by leveraging empirical,techniques, , and neural networks. This new era of forecasting promises to revolutionize how we manage climate-related risks and adapt strategies that mitigate economic losses and potentially save lives.
Remarkably, GraphCast outperforms the industry-standard High Resolution Forecast HRES produced by ECMWF across nearly 1,300 verification targets. It has achieved accuracy scores higher than 90 for medium-range forecasts up to 10 days ahead with high-resolution data at a scale of 28 x 28 km globally.
GraphCast's capabilities ext beyond providing timely warnings, says Lee, noting that its potential applications include tracking cyclones with precision, identifying atmospheric rivers associated with flood risks, and predicting extreme temperature onset. These advancements have the potential to save lives through proactive preparedness measures.
While GraphCast does not yet fully quantify prediction uncertnty like ECMWF’s ensemble forecasting system, which provides probabilities for extreme events, it is a significant step towards achieving more reliable operational forecasts. The research team behind GraphCast ext their study of broader climate patterns with the ultimate goal thatempowers global communities in addressing environmental challenges.
To further explore this innovative approach and its implications, join the CMCC webinar titled GraphCast: Learning Skillful Medium-Range Global Weather Forecasting featuring Remi Lam from Google DeepMind. This interactive session offers insights into howcan provide a paradigm shift for the future of weather forecasting.
For more information:
Read about GraphCast in this blog post by Remi Lam, highlighting its development and potential impact.
Discover the scientific paper published in Science magazine titled Learning Skillful Medium-Range Global Weather Forecasting authored by Remi et al., which outlines their findings and .
Explore the Github repository for GraphCast to access source code and documentation.
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AI Revolutionizes Weather Forecasting GraphCast: Accelerating Predictive Accuracy Machine Learning for Climate Dynamics Understanding Paradigm Shift in Numerical Weather Prediction Global Scale Weather Forecasting Efficiency Boost Deep Learning Enhances Extreme Event Prediction Timing