Reliable precipitation nowcasting using probabilistic diffusion models

Reliable precipitation nowcasting using probabilistic diffusion models

Blog Article

Precipitation nowcasting is a crucial element in current weather service systems.Data-driven methods have proven highly advantageous, due to their flexibility in utilizing detailed initial hydrometeor observations, and their capability to approximate meteorological dynamics effectively given sufficient training data.However, current data-driven methods often encounter severe approximation/optimization errors, rendering their predictions and associated uncertainty estimates green snowflake japanese maple unreliable.

Here a probabilistic diffusion model-based precipitation nowcasting methodology is introduced, overcoming the notorious blurriness and mode collapse issues in existing practices.Diffusion models learn a sequential of neural networks to reverse a pre-defined diffusion process that generates the probability distribution of future precipitation fields.The precipitation nowcasting based on diffusion model results in a 3.

7% improvement in continuous ranked probability score compared to canadian white blenheim apricot state-of-the-art generative adversarial model-based method.Critically, diffusion model significantly enhance the reliability of forecast uncertainty estimates, evidenced in a 68% gain of spread-skill ratio skill.As a result, diffusion model provides more reliable probabilistic precipitation nowcasting, showing the potential to better support weather-related decision makings.