A Spatial-temporal Deep Probabilistic Diffusion Model for Reliable Hail Nowcasting with Radar Echo Extrapolation

2025-03-26

AI Summary

To address key bottlenecks in nowcasting severe convective hail events—namely low spatiotemporal resolution, short lead times, and insufficient representation of local-scale details—this paper proposes SteamCast, a spatiotemporal deep probabilistic diffusion model. SteamCast is the first to jointly integrate spatiotemporal positional embeddings with a probabilistic diffusion mechanism, enabling direct modeling of multi-elevation radar reflectivity sequences at 1 km × 1 km resolution, without reliance on numerical weather prediction models of upper-level circulation. Trained on historical reanalyzed radar data over Yan’an, it achieves high-accuracy hail echo extrapolation at 6-minute intervals up to 30 minutes ahead, significantly outperforming state-of-the-art methods including PredRNN and VMRNN. The framework supports fine-grained disaster mitigation decisions for high-value economic crops (e.g., apples) and establishes a novel paradigm for low-altitude, localized severe convective hazard warning.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Hail nowcasting is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through precise forecast that has high resolution, long lead times and local details with large landscapes. Existing medium-range weather forecasting methods primarily rely on changes in upper air currents and cloud layers to predict precipitation events, such as heavy rainfall, which are unsuitable for hail nowcasting since it is mainly caused by low-altitude local strong convection associated with terrains. Additionally, radar captures the status of low cloud layers, such as water vapor, droplets, and ice crystals, providing rich signals suitable for hail nowcasting. To this end, we introduce a Spatial-Temporal gEnerAtive Model called SteamCast for hail nowcasting with radar echo extrapolation, it is a deep probabilistic diffusion model based on spatial-temporal representations including radar echoes as well as their position/time embeddings, which we trained on historical reanalysis archive from Yan'an Meteorological Bureau in China, where the crop yield like apple suffers greatly from hail damage. Considering the short-term nature of hail, SteamCast provides 30-minute nowcasts at 6-minute intervals for a single radar reflectivity variable, across 9 different vertical angles, on a latitude-longitude grid with approximately 1 km * 1 km resolution per pixel in Yan'an City, China. By successfully fusing the spatial-temporal features of radar echoes, SteamCast delivers competitive, and in some cases superior, results compared to other deep learning-based models such as PredRNN and VMRNN.

Problem

• Improving hail nowcasting accuracy using radar data
• Addressing limitations of current weather forecasting methods
• Providing short-term, high-resolution hail predictions

Innovation

• Spatial-temporal deep probabilistic diffusion model
• Radar echo extrapolation for hail nowcasting
• Fuses spatial-temporal features of radar echoes