AI Weather Model BEATS Meteorologists – Higher Accuracy But 10,000x Faster!

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Huawei’s Pangu-Weather AI model represents a significant advancement in weather forecasting.

This model is the first AI prediction model to outperform traditional numerical weather forecast methods in terms of accuracy and speed. It processes data 10,000 times faster than conventional methods, reducing the global weather prediction time to just seconds.

Note that this is not against a strawman benchmark but against the world’s best weather forecasting NWP system:

πŸ’‘ "By training the models on 39 years of global weather data, Pangu-Weather produces better deterministic forecast results on reanalysis data than the world’s best NWP system, the operational IFS of ECMWF, while also being much faster. In addition, Pangu-Weather is excellent at forecasting extreme weather events and performing ensemble weather forecasts. Pangu-Weather reveals the potential of using large pre-trained models for various downstream applications, showing the same trend as other AI scopes, such as computer vision, natural language processing, cross-modal understanding and beyond." -- Nature

Let’s have a look at some screenshots from the Nature paper:

πŸ”— Source: Nature
πŸ”— Source: Nature
πŸ”— Source: Nature

You can watch the interview with one of the coauthors here:

Pangu Weather Model: Clearing up weather prediction uncertainties

The Pangu-Weather model employs a unique 3D Earth-Specific Transformer (3DEST) architecture to effectively process complex, non-uniform 3D meteorological data. It has been trained using different forecast intervals (1 hour, 3-hour, 6-hour, and 24-hour) to minimize prediction errors and reduce the number of iterations needed for accurate forecasts.

The model was trained using 43 years of weather data, with each sub-model requiring significant computational resources and time for training.

This model has been particularly effective in predicting fine-grained meteorological features such as humidity, wind speed, temperature, and sea level pressure. Its efficacy has been demonstrated in several real-world scenarios, including the prediction of extreme weather events like typhoons.

Moreover, the model has been made available to the public on the European Centre for Medium-Range Weather Forecasts (ECMWF) and the GitHub websites, allowing broader access to its advanced forecasting capabilities.

Pangu-Weather’s development is part of Huawei’s broader initiative with their Pangu 3.0 AI model, which is designed to foster digital transformation across various sectors, including weather forecasting.

This initiative involves a three-layered architecture: a foundational large language model (L0), industry-specific models (L1), and scenario-specific models (L2), making it versatile for different applications.

πŸ”— Source: Nature

The publication of a paper on the Pangu-Weather AI model in the prestigious journal ‘Nature‘ is a notable achievement, marking the first time employees of a Chinese technology company have authored a paper in this journal.

There are also other Nature papers, such as FuXi and Precipitation Forecasts, on the same topic. All use AI to improve forecasting capabilities against traditional benchmarks.

πŸ§‘β€πŸ’» What Does It Mean for Us? This shows an instance of “machine learning eats everything” if you need another example. As a human collective, we have already tightly integrated AI into research, progress, and human flourishment. The future is bright – incredible efficiency gains and scientific breakthroughs will shake up every industry. If we want to stay relevant, we need to become disruptors and build on top of AI to scale up our output by 10,000x – as shown in this impressive Nature research.


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