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Enhancing weather prediction accuracy with artificial intelligence

Haebom
The traditional Numerical Weather Prediction (NWP) method models the complex physical phenomena of weather systems with mathematical equations, and predicts the weather using supercomputers. For example, it predicts weather phenomena by calculating changes in atmospheric pressure, temperature, and humidity. This approach requires elaborate modeling and calculations, and the models undergo a continual and complex improvement process carried out by experts.
In contrast, 'GraphCast' is a method utilizing machine learning and graph neural networks (GNNs) to build a model by directly training on historical weather data. This is an innovative approach that predicts weather by learning patterns from data, rather than relying on conventional complex numerical calculations. GraphCast offers faster prediction speeds and can be more accurate than traditional NWP methods. This is referred to as Machine learning-based weather prediction (MLWP).

Does this replace traditional NWP methods? Or is GraphCast used together with them?

GraphCast and conventional numerical weather prediction (NWP) play complementary roles. While NWP relies on physical models to forecast weather phenomena, GraphCast utilizes machine learning—particularly graph neural networks—to learn patterns from past weather data for its predictions. Rather than replacing NWP, GraphCast is used alongside traditional methods to enhance forecasting accuracy.
GraphCast’s key distinction is its machine learning-based approach. Although traditional NWP methods also use historical data, they predominantly rely on physical laws and mathematical models. By contrast, GraphCast learns directly from the data to predict complex weather patterns, delivering faster processing and improved efficiency.

Adopting GraphCast?

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Data preparation: GraphCast requires a wealth of weather data. Gather historical data to be used for model training.
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Model training: Apply suitable machine learning algorithms and techniques to train your GraphCast model effectively.
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Infrastructure setup: Establish the computing infrastructure necessary to run GraphCast. High-performance computing systems may be required.
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Integration and application: Integrate GraphCast with your existing weather forecasting system so that prediction results can be put to use in real time.
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Continuous evaluation and improvement: Continuously assess the performance of your GraphCast model, and adjust it when needed to improve accuracy.
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Haebom
최근 기상예보에 골탕 먹은적 있는데 이제 그런 일 없었으면 좋겠네요. :)
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