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arXiv Machine Learning

Generated storm lifecycles fill the rare-event tail of short rainfall records

The paper introduces StormGen, a generative machine‑learning model that expands the initial hours of an observed storm into full, physically plausible hourly rainfall lifecycles within minutes. Tested on a decade of southeast Texas rainfall, StormGen accurately reproduced the most severe storms, generating close counterparts for 4–5% of the top 2% of events, outperforming stochastic storm transposition. Remarkably, the model reconstructed Hurricane Harvey’s lifecycle and extreme catchment totals from a single hour of data, even though the event was not in the training set.

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arXiv Machine Learning
Sep 4

SurgeGen: A Hybrid Generative Diffusion Framework for Storm Surge Scenario Synthesis

SurgeGen is a two‑stage generative diffusion framework that synthesizes storm surge scenarios conditioned on continuous storm parameters. The first stage produces a coarse baseline surge estimate, which then conditions a diffusion model that refines the output to capture realistic spatial patterns and variability. The method can generate diverse, realistic surge scenarios both within and beyond the training distribution.

By Shunan Zheng, John J. Hasenbein
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arXiv Machine Learning
Oct 16

KNOU-Flood: Advancing global streamflow forecasting with lead-aware probabilistic machine learning

KNOU‑Flood extends the open AIFL recipe for global streamflow forecasting by retaining a single‑layer LSTM and two‑stage transfer learning while adding a longer 424‑day input window, lead‑aware forecast inputs, and GraphCast as an additional forecast source. The model also introduces a latent‑noise ensemble trained with the fair CRPS, achieving a median KGE′ of 0.65 at lead time 1 and 0.48 at lead time 10 across 2,099 test basins, outperforming AIFL and a 1,024‑unit baseline. The probabilistic head, GraphCast channels, longer window, and mixed fine‑tuning collectively reduce lead‑time decay, improve calibration, and enhance detection of multi‑year floods.

By Minjong Cheon
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arXiv AI
Oct 27

EvoBench: An Evolving, Diagnostic Benchmark Framework for Environmental Modeling

EvoBench is a diagnostic benchmark framework designed to reveal where and when machine‑learning models for environmental variables fail, rather than relying on a single average accuracy score. It tracks model performance over time and across spatial extrapolation, highlighting issues such as non‑stationarity, poor performance at unmonitored sites, and under‑prediction of rare events like floods. In a case study on daily streamflow prediction across 9,000 U.S. basins, EvoBench shows that no model dominates all scenarios and that targeted fixes can mitigate identified weaknesses.

By Yiming Sun, Yuan Zhang, Qi Cheng, Yingda Fan, Runlong Yu, Yiqun Xie, Xiaowei Jia
benchmarks
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arXiv Machine Learning
Oct 16

Can AI Forecast the Extremes? Evaluating Probabilistic Weather Models on Tropical Cyclones

The study evaluates probabilistic AI weather models—GenCast, FourCastNet 3, and FGN—against the ECMWF IFS-ENS ensemble for tropical cyclone forecasting. Using new probabilistic metrics, the authors find that AI models, particularly FGN, outperform IFS-ENS in cyclone tracking, achieving better location scores for 90% of storms in 2025. The analysis also identifies intensity and spread limitations, model‑specific failure modes, and the influence of pretraining data, highlighting opportunities for targeted improvement.

By Nathan Chalumeau, Xavier Renard, Remi Meynadier, Hugo Rakotoarimanga, Marcin Detyniecki, Emmanuel de Bezenac
benchmarks
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arXiv Machine Learning
Jun 4

Uncovering Insights of Compound Flooding with Data-Driven AI

arXiv:2506. 04281v2 Announce Type: replace Abstract: Compound flooding, driven by nonlinear interactions between multiple hydrometeorological factors, poses a significant challenge to hazard prevention.

By Xu Zheng, Chaohao Lin, Sipeng Chen, Zhuomin Chen, Jimeng Shi, Jayantha Obeysekera, Jingchao Ni, Wei Cheng, Jason Liu, Dongsheng Luo
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arXiv Machine Learning
Sep 2

GenONet: A Generative operator Network for High-Resolution Precipitation Nowcasting

GenONet introduces a Spatio-Temporal U-DeepONet architecture that serves as a generator in a GAN framework for high‑resolution precipitation nowcasting up to three hours ahead. By learning continuous‑time precipitation dynamics with a Deep Operator Network and enforcing physics through a moisture‑conservation loss, the model produces sharp, physically consistent forecasts that outperform baselines, especially for high‑intensity events and longer lead times. Ablation studies confirm the added value of the physics‑informed regularizer and the synergy of operator learning with adversarial training.

By Mohammad Kian Golkar, Luciano Alves de Oliveira, Mohammad Khanjani
diffusionbenchmarkssafety
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