Generative emulation of weather forecast ensembles with diffusion models.

Area of Science:

• Meteorology and Climate Science
• Artificial Intelligence and Machine Learning

Background:

• Uncertainty quantification is vital for decision-making, particularly in probabilistic weather forecasting.
• Current methods rely on computationally expensive physics-based simulations to generate forecast ensembles.
• This high computational cost limits the scale and efficiency of generating large ensembles.

Purpose of the Study:

• To develop a computationally efficient method for generating probabilistic weather forecasts.
• To leverage deep generative diffusion models for emulating physics-based weather simulations.
• To improve the accuracy and reliability of weather and climate predictions.

Main Methods:

• Utilized deep generative diffusion models trained on historical weather data.
• Emulated operational ensemble weather forecasts using the learned models.
• Assessed the statistical properties and predictive skill of generated ensembles compared to physics-based ones.
• Evaluated the models' ability to correct biases in operational forecasting systems.

Main Results:

• Generated ensembles exhibit similar statistical properties and predictive skill to traditional physics-based ensembles.
• Emulated forecasts are produced at a significantly lower computational cost, enabling large-scale generation.
• Bias-corrected generated ensembles demonstrate improved probabilistic forecast metrics and reliability.
• Enhanced accuracy in forecasting probabilities of extreme weather events was observed.

Conclusions:

• Deep generative diffusion models offer a scalable and cost-effective alternative for producing high-quality probabilistic weather forecasts.
• This methodology shows promise for generating large climate projection ensembles for climate risk assessment.
• The approach enhances the utility of weather forecasts for critical decision-making processes.