A Spatial-temporal Deep Probabilistic Diffusion Model for Reliable Hail Nowcasting with Radar Echo Extrapolation
Journal:
arXiv
Published Date:
Mar 26, 2025
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.
