arXiv Machine Learning June 29, 2026 By Elizabeth Cucuzzella, Rafael Izbicki, Ann B. Lee

Trustworthy Predictive Distributions for Tail Events with Semiparametric Diagnostic Transport Maps

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

1d ago

Proper Scoring Rule-based Diffusion for Probabilistic Weather Forecasting

arXiv:2609.38632v1 Announce Type: new Abstract: Recent probabilistic weather forecasters train stochastic predictors with the continuous ranked probability score (CRPS) to generate each ensemble memb...

By Joonhyeong Park, Giung Nam, Hyungi Lee, Kyunghyun Cho, Byoungwoo Park, Juho Lee

diffusion fine-tuning

arXiv Machine Learning

Sep 23

FAST-ML: A Hybrid Physics-Machine Learning Framework for Tropical Cyclone Intensity Forecasting

arXiv:2609.25505v1 Announce Type: cross Abstract: Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics nu...

By Shijie Xiao, Jonathan Lin, Thomas Ehrmann, Ali Sarhadi

safety

arXiv Machine Learning

Jun 10

Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting

arXiv:2509. 25017v2 Announce Type: replace Abstract: Wildfires are among the most severe natural hazards, posing a significant threat to both humans and natural ecosystems.

By Spyros Kondylatos, Nikolas Papadopoulos, Gustau Camps-Valls, Ioannis Papoutsis

arXiv Machine Learning

Jul 7

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

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.

By Jennifer Nakamura, Upmanu Lall

safety

arXiv Statistics ML

Sep 22

Forecasting threshold exceedance of atmospheric variables at a specific location

arXiv:2605.31079v2 Announce Type: replace-cross Abstract: Accurate short-term forecasting of extreme weather events is important for early warning and risk mitigation. We compare two approaches for p...

By Roberta Baggio, Jean-Fran\c{c}ois Muzy

arXiv Machine Learning

Sep 25

Improving global precipitation forecasts with an AI weather model trained on satellite observations

The paper presents Laxmi, a retrained version of the AIFS weather model that uses satellite-based precipitation observations instead of ERA5 reanalysis data. Laxmi achieves a 19% improvement in global probabilistic accuracy, reduces drizzle overprediction by 33%, and boosts the 95th percentile Brier skill score by 57%. In a case study of 10 Indian tropical storms, Laxmi accurately forecasted 150 mm event-total precipitation in 7 events, outperforming both the original AIFS and the leading physical model IFS.

By Julian F. Schmitt, Bertrand Delorme, Robert C. King, Yashica Patodia, Tapio Schneider, Aditi Sheshadri, Ravi Jain

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