Deep learning for post-processing ensemble weather forecasts.

Area of Science:

• Meteorology and Atmospheric Sciences
• Machine Learning Applications
• Computational Science

Background:

• Accurate quantification of uncertainty in weather forecasts is crucial, particularly for extreme events.
• Ensemble prediction systems (EPS) are standard but computationally expensive, often requiring statistical post-processing.
• Existing post-processing methods may not capture complex non-linear relationships in weather data.

Purpose of the Study:

• To develop a computationally efficient mixed model for weather forecasting.
• To leverage deep neural networks for improved post-processing of ensemble forecasts.
• To enhance the prediction of extreme weather events using machine learning techniques.

Main Methods:

• A mixed model approach using a subset of ensemble weather trajectories.
• Deep neural networks for statistical post-processing of raw ensemble predictions.
• Application and evaluation on global weather data and specific extreme event case studies.

Main Results:

• Achieved over 14% relative improvement in ensemble forecast skill (CRPS) on global data.
• Demonstrated larger improvements for extreme weather events compared to standard methods.
• Showed comparable forecast skill using fewer trajectories, reducing computational burden.

Conclusions:

• The proposed mixed model with deep neural network post-processing offers a significant advancement in weather forecasting accuracy and efficiency.
• Reduced computational cost allows for higher resolution forecasts and improved prediction of critical weather phenomena.
• This machine learning-driven approach holds promise for future weather and climate modeling advancements.