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 unreliable.Here a probabilistic diffusion model-based precipitation 1st marine division hoodie 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 shiro neon zero probability score compared to 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.