Conditional Tropical Cyclogenesis Rates via Rare-Event Sampling in a Neural Weather Emulator

2026-06-29

AI Summary

This study addresses the challenge of efficiently estimating tropical cyclone (TC) genesis rates under diverse meteorological conditions—a rare-event problem poorly resolved by conventional ensemble forecasting. For the first time, the nonequilibrium rare-event sampling method Forward Flux Sampling (FFS) is integrated into the high-resolution neural weather model SDL-WXFormer (1° resolution), enabling accurate estimation of conditional genesis rates spanning nearly three orders of magnitude without altering the model’s dynamics. By decomposing TC development into sequential interface-crossing probabilities and incorporating a stochastic calibration layer, the approach achieves excellent agreement with direct sampling across 98 North Atlantic initial conditions (mean ratio 1.03 ± 0.15) while accelerating computations by 3–140× (geometric mean 14×). The framework further identifies environment-dependent rate-limiting steps, offering physically interpretable diagnostic insights.

Abstract

We couple Forward Flux Sampling (FFS), a non-equilibrium rare-event technique from statistical mechanics, to a neural weather emulator (SDL-WXFormer, 1° 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 mature cyclone intensification path into a flux through an initial interface pressure and a product of conditional crossing probabilities across four intermediate interface pressures. We use the 1° emulator because FFS requires O(10^4) model trajectories per initial condition, and because the model's calibrated stochastic layers provide the necessary exploratory spread. Applied to 98 Atlantic basin initial conditions spanning 21 August - 8 October 2022, FFS resolves genesis rates spanning nearly three orders of magnitude, capturing a seasonal cycle qualitatively consistent with observations. A self-consistency check comparing FFS rates to independent direct-sampling rates yields a mean ratio of 1.03 +/- 0.15 across all initial conditions. Computational enhancement factors range from 3X (most active environment) to 140X (most suppressed), with a geometric mean of 14X. Three case studies illustrate the physical diagnostics the method provides: the rate-limiting step is initial tropical organization for the Earl environment, uniformly high crossing probabilities for the Fiona precursor environment, and a compound barrier at the final intensification stages for the Ian environment. More efficient emulators would enable application of FFS to finer resolutions.

Problem

• tropical cyclogenesis
• rare-event sampling
• hurricane intensification
• ensemble simulation
• conditional rates

Innovation

• Forward Flux Sampling
• neural weather emulator
• tropical cyclogenesis
• rare-event sampling
• conditional crossing probabilities