Forward Flux Sampling applied to a 1-degree neural weather emulator resolves conditional tropical cyclogenesis rates spanning three orders of magnitude across 98 Atlantic initial conditions, with self-consistency ratio 1.03 to direct sampling and computational gains up to 140X.
Bringing statistics to storylines: Rare event sampling for sudden, transient extreme events
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The optimal advance split time for rare event sampling in 1D chaotic maps equals the logarithm of the ratio of target rarity to initial perturbation size, generalized via maximum entropy.
Online conformal prediction post-processing guarantees calibrated uncertainty coverage for GenCast, NeuralGCM, and AIFS-ENS forecasts of temperature and precipitation including extremes.
citing papers explorer
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Conditional Tropical Cyclogenesis Rates via Rare-Event Sampling in a Neural Weather Emulator
Forward Flux Sampling applied to a 1-degree neural weather emulator resolves conditional tropical cyclogenesis rates spanning three orders of magnitude across 98 Atlantic initial conditions, with self-consistency ratio 1.03 to direct sampling and computational gains up to 140X.
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Routes to rare events with optimally timed perturbations: a Tent Map is all you need
The optimal advance split time for rare event sampling in 1D chaotic maps equals the logarithm of the ratio of target rarity to initial perturbation size, generalized via maximum entropy.
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Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction
Online conformal prediction post-processing guarantees calibrated uncertainty coverage for GenCast, NeuralGCM, and AIFS-ENS forecasts of temperature and precipitation including extremes.