arXiv:2512.08974v2 Announce Type: replace-cross Abstract: Severe convection produces localized hazards that often require warnings before radar echoes fully reveal storm development. Convective initiation and the maintenance of intense convection remain challenging for radar-only nowcasting because pre-convective signals may be absent from recent radar observations and strong echoes often decay rapidly in forecasts. Here we present FuXi-Nowcast, an environment-conditioned deep learning system that combines high-resolution observations with three-dimensional atmospheric forecasts to predict com
The continuous advancements in deep learning and increasing demand for accurate, immediate hazard predictions are driving innovation in nowcasting technologies.
Improved severe weather prediction can mitigate economic losses, save lives, and enhance preparedness for climate-related events globally.
Nowcasting capabilities are extended beyond traditional radar methods by integrating deep learning with atmospheric forecasts, enabling earlier and more accurate localized hazard warnings.
- · Meteorological agencies
- · Insurance companies
- · Logistics and transportation sectors
- · Agriculture
More timely and accurate severe weather warnings will become available to the public and industries.
Reduced economic impact from severe weather events due to improved preparedness and response capabilities.
Potential for integration into autonomous systems that adapt operations based on real-time weather predictions.
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