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.
On the other hand, 'GraphCast' is a method that uses machine learning and graph neural networks (GNN) to directly learn past weather data and build a model. This is an innovative approach that predicts the weather by learning patterns from data instead of the existing complex numerical calculation method. GraphCast has a fast prediction speed and can be more accurate than the existing NWP method. This is called 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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I post articles related to IT 💻, economy 💰, and humanities 🎭.
If you are curious about my thoughts, perspectives or interests, please subscribe.
haebom@kakao.com
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Haebom
최근 기상예보에 골탕 먹은적 있는데 이제 그런 일 없었으면 좋겠네요. :)
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