Artificial intelligence is now being used to predict weather to increase accuracy.
Haebom
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The existing Numerical Weather Prediction (NWP) method models the complex physical phenomena of the weather system with mathematical equations and uses supercomputers to predict the weather. For example, it predicts weather phenomena by calculating changes in atmospheric pressure, temperature, and humidity. This method requires sophisticated modeling and calculation processes, and these models go through a complex process of continuous improvement 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).
Is this a replacement for the existing NWP method, or is it used alongside GraphCast?
GraphCast and traditional numerical weather prediction (NWP) methods are complementary. While NWP predicts weather phenomena based on physical models, GraphCast uses machine learning, especially graph neural networks, to learn patterns from past weather data and use them to make predictions. GraphCast is not intended to replace NWP, but rather to be used in conjunction with traditional methods to improve prediction accuracy.
GraphCast’s key difference is its machine learning-based approach. Traditional NWP methods also utilize historical data, but they rely primarily on physical laws and mathematical models. GraphCast, on the other hand, learns directly from data to predict complex weather patterns, and offers fast processing speed and efficiency.
Introducing GraphCast?
Data Preparation: GraphCast requires rich weather data. Collect historical weather data to use for model training.
Model Training: Apply appropriate machine learning algorithms and techniques to effectively train GraphCast models.
Build the infrastructure: Build the compute infrastructure to run GraphCast. This may require high-performance computing systems.
Integration and Application: Integrate GraphCast with your existing weather forecasting system to enable real-time use of forecast results.
Continuous evaluation and improvement: Continuously evaluate the performance of GraphCast models and adjust models when necessary to improve accuracy.
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