Conditional Tropical Cyclogenesis Rates via Rare-Event Sampling in a Neural Weather Emulator
arXiv:2606.30920v1 Announce Type: cross Abstract: We couple Forward Flux Sampling (FFS), a non-equilibrium rare-event technique from statistical mechanics, to a neural weather emulator (SDL-WXFormer, 1{\deg} grid spacing) to estimate conditional tropical cyclogenesis rates, or how often a tropical cyclone achieves a hurricane-level central pressure, without modifying model dynamics. Tropical cyclogenesis rates vary by orders of magnitude across regimes, yet direct ensemble sampling cannot resolve this variability at operationally feasible ensemble sizes. FFS decomposes the rare disturbance to
The convergence of advanced AI, specifically neural weather emulators, with sophisticated statistical mechanics techniques like FFS, enables breakthroughs in complex climate modeling at a time of increasing climate volatility.
This development allows for more accurate and computationally feasible prediction of rare, high-impact weather events like hurricanes, offering critical foresight for risk management, infrastructure planning, and humanitarian response.
The ability to accurately predict conditional tropical cyclogenesis rates with neural emulators replaces reliance on computationally expensive direct ensemble sampling, making high-resolution climate forecasting more accessible and efficient.
- · Insurance and Reinsurance Industry
- · Emergency Management Agencies
- · Climate Modeling Research
- · Renewable Energy Sector
- · Traditional Weather Forecasting Models
- · Coastal Real Estate Developers (unprepared)
- · Agriculture (lack of foresight)
Improved early warning systems and disaster preparedness for hurricane-prone regions.
More precise risk assessments leading to optimized infrastructure investments and potentially lower insurance premiums in some areas.
Enhanced understanding of climate change feedback loops and regional climate dynamics, informing policy and mitigation strategies.
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Read at arXiv cs.LG