Diffusion-Based Refinement for Kilometer-Scale Probabilistic Precipitation Nowcasting

2026-08-30

AI Summary

为解决局部极端降水预测难题，本文提出exPreCast-ENS方法，通过条件残差扩散框架将4公里分辨率的确定性预报转换为1公里分辨率的概率集合预报，同时校正系统性误差。

Abstract

Localized extreme precipitation is a major trigger of urban flash floods and landslides, yet producing nowcasts that combine fine spatial detail with probabilistic uncertainty remains challenging. Here we introduce exPreCast-ENS, a conditional residual diffusion framework that transforms the deterministic 4 km radar nowcaster exPreCast into a 1 km probabilistic ensemble while correcting systematic forecast errors. Conditioning on both the forecast and preceding radar observations lets the ensemble-mean correct the baseline rather than perturb it, while members represent unresolved fine-scale variability. Over the Korean Peninsula, skill improves with ensemble size. In two high-impact events in 2023, a 30-member ensemble recovers 38-47% of heavy-rain pixels missed by exPreCast while retaining approximately 95% of its correct detections and alarming on under 1% of the pixels it correctly left clear. The method generates a 1-h forecast in 3.4 s on a single GPU and yields consistent improvements on the French regional MeteoNet radar dataset.

Problem

• Probabilistic Precipitation Nowcasting
• Fine Spatial Detail
• Forecast Errors
• Ensemble Forecasting
• Extreme Precipitation

Innovation

• conditional residual diffusion
• probabilistic ensemble
• forecast error correction
• high-resolution precipitation nowcasting