A generative adversarial network approach to (ensemble) weather prediction

Area of Science:

• Meteorology
• Artificial Intelligence
• Machine Learning

Background:

• Accurate short-term weather forecasting is crucial for various applications.
• Traditional numerical weather prediction models are computationally intensive.
• Deep learning offers a potential alternative for faster and efficient weather prediction.

Purpose of the Study:

• To develop and evaluate deep learning models for 24-hour weather prediction over Europe.
• To assess the performance of generative adversarial networks for meteorological field prediction.
• To implement a deep learning-based ensemble system for uncertainty quantification.

Main Methods:

• Conditional deep convolutional generative adversarial networks (GANs) were employed.
• Models were trained on four years of ERA5 reanalysis data.
• Monte-Carlo dropout was utilized to create an ensemble forecasting system.

Main Results:

• The models demonstrated good agreement with reanalysis data for geopotential height and two-meter temperature.
• Performance for total precipitation prediction was unsatisfactory.
• The deep learning ensemble system improved forecast skill and quantified prediction uncertainty.

Conclusions:

• Data-driven weather forecasting is feasible for specific meteorological parameters like geopotential height and temperature.
• Deep learning, particularly ensemble methods, shows promise for computationally efficient and skillful weather prediction.
• Further research is needed to improve precipitation forecasting accuracy using deep learning.