arXiv Machine Learning July 7, 2026 By Jennifer Nakamura, Upmanu Lall

A 10,000-Year Global Stochastic Tropical Cyclone Catalog with Wind-Dependent Track Transitions (WHITS)

Read the original on arXiv Machine Learning →

arXiv:2605. 20494v2 Announce Type: replace Abstract: Reliable assessment of tropical cyclone risk is limited by the short and spatially uneven historical record, especially for rare, high-intensity landfalls that dominate insured loss.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

safety

arXiv Machine Learning

Aug 11

Deep Learning Imputation of Missing Radius of Maximum Winds (Rmax) Values in Tropical Cyclone Best-Track Data

arXiv:2608. 09683v1 Announce Type: new Abstract: Probabilistic coastal hazard assessments require accurate characterization of tropical cyclone (TC) parameters, yet datasets often contain missing records for the radius of maximum winds (Rmax), a key variable in Joint Probability Method analyses.

By Swastik Agrawal, Nishkal Hundia, Ziyue Liu, Michelle Bensi

fine-tuning

arXiv Machine Learning

Jun 29

Trustworthy Predictive Distributions for Tail Events with Semiparametric Diagnostic Transport Maps

arXiv:2603. 11229v2 Announce Type: replace-cross Abstract: Machine learning forecast systems are moving beyond point predictions to full predictive distributions for future outcomes y conditional on complex inputs x.

By Elizabeth Cucuzzella, Rafael Izbicki, Ann B. Lee

arXiv Machine Learning

Aug 12

AIFS-TC: A simple correction competitive with the operational frontier for tropical cyclone intensity forecasting

arXiv:2608. 09959v1 Announce Type: cross Abstract: AI weather models are in the process of revolutionising weather forecasting.

By Anna Allen, Wessel P. Bruinsma, Michael Maier-Gerber, Harrison Cook, Matthew Chantry, Richard E. Turner

llms agents benchmarks

arXiv Machine Learning

Jul 1

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.

By John S. Schreck, William Chapman, Charlie Becker, David John Gagne II

arXiv AI

Jun 4

Physics-Informed Machine Learning for Short-Term Flood Prediction

arXiv:2606. 04143v1 Announce Type: cross Abstract: Accurate flood forecasting is essential for mitigating disaster risks and protecting communities.

By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

safety

arXiv Machine Learning

Aug 4

From AI Weather Prediction to Infrastructure Resilience: A Real-Time Correction-Downscaling Framework for Tropical Cyclone Impact Forecasting

arXiv:2603. 12828v2 Announce Type: replace-cross Abstract: This paper addresses a missing capability in infrastructure resilience: turning fast, global AI weather forecasts into asset-scale, actionable risk intelligence.

By You Wu, Zhenguo Wang, Naiyu Wang

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Related stories

Deep Learning Imputation of Missing Radius of Maximum Winds (Rmax) Values in Tropical Cyclone Best-Track Data

Trustworthy Predictive Distributions for Tail Events with Semiparametric Diagnostic Transport Maps

AIFS-TC: A simple correction competitive with the operational frontier for tropical cyclone intensity forecasting

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

Physics-Informed Machine Learning for Short-Term Flood Prediction

From AI Weather Prediction to Infrastructure Resilience: A Real-Time Correction-Downscaling Framework for Tropical Cyclone Impact Forecasting