Deep learning for twelve hour precipitation forecasts.

Area of Science:

• Meteorology
• Artificial Intelligence
• Machine Learning

Background:

• Traditional weather forecasting relies on complex physics-based models and supercomputers.
• Improving physics-based models or increasing simulation resolution presents significant development and computational challenges.

Purpose of the Study:

• To introduce a novel neural network model for high-resolution precipitation forecasting.
• To evaluate the performance of this AI-driven model against state-of-the-art physics-based models.

Main Methods:

• Development of a neural network trained on data to learn atmospheric transformations.
• Prediction of raw precipitation targets at high resolution.
• Comparison of model performance against existing operational models for lead times up to 12 hours.

Main Results:

• The neural network model achieves superior precipitation prediction accuracy up to 12 hours ahead.
• The AI model outperforms current state-of-the-art physics-based models in the Continental United States.
• The model demonstrates efficient parallel processing capabilities.

Conclusions:

• Neural weather models offer a promising alternative to traditional physics-based forecasting methods.
• This study validates the efficacy of AI in advancing weather prediction capabilities.
• The developed model represents a significant step towards practical AI-driven weather forecasting.