arXiv:2605.24067v1 Announce Type: cross Abstract: Most nowcasting systems, built on radar reflectivity, focus on current precipitation, ignoring the atmospheric precursors -- such as low-level convergence, turbulent eddies, and latent heating -- that offer a fleeting window to foresee storm birth. We introduce MeteoLogist, a physics-inspired radar intelligence framework that models the full life cycle of convection -- from its precursors to organized storm evolution. However, exploiting these precursors is non-trivial: they originate from multiple meteorological drivers -- thermodynamic, kinem
Advances in AI, particularly physics-informed machine learning, are enabling more sophisticated models to integrate complex meteorological data for improved weather prediction.
Improved nowcasting directly impacts disaster preparedness, agricultural planning, and infrastructure management, offering significant economic and societal benefits.
The ability to anticipate storm formation earlier and with greater accuracy, moving beyond reactive precipitation-based systems to proactive, precursor-driven forecasts.
- · Meteorological services
- · Insurance industry
- · Agriculture sector
- · Logistics and transportation
- · Regions unprepared for extreme weather
- · Outdated weather prediction models
Reduced economic impact from severe weather events due to earlier warnings.
Increased urban resilience planning and infrastructure investment in response to more precise storm predictions.
Potential for new climate adaptation technologies integrating real-time, AI-powered nowcasting capabilities.
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Read at arXiv cs.LG