← Back to all news
arXiv Machine Learning

Ranked Right, Sized Wrong: Ensemble Spread Fails Silently at Streamflow Extremes

The study examines how ensemble spread from Long Short-Term Memory (LSTM) models, used for streamflow forecasting, behaves under increasingly extreme flow conditions due to climate change. It finds that prediction error grows relative to the corrected spread—up to four times larger at the highest flows—and that under-dispersion is most pronounced for extreme flows beyond the training record, reaching an 18‑fold discrepancy. While the spread still ranks errors well (near‑oracle ranking), it systematically understates risk, especially for the most critical extreme events.

Related stories

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
More like this →
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
fine-tuning
More like this →
arXiv Machine Learning
Jun 26

CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting

arXiv:2510. 20769v2 Announce Type: replace-cross Abstract: Accurate medium-range precipitation forecasting is essential for hydrometeorological risk management but remains challenging for both numerical weather prediction (NWP) systems and data-driven models.

By Tianyi Xiong, Haonan Chen, Kelly Mahoney, Jingyin Tang, Tim Smith, Janice Bytheway
llmsragsafety
More like this →
arXiv AI
Jun 3

Scalable Uncertainty Quantification for Extreme Weather Forecasting via Empirical Neural Tangent Kernels

arXiv:2606. 02886v1 Announce Type: cross Abstract: Deep learning weather models now match numerical weather prediction accuracy while running orders of magnitude faster, but produce deterministic forecasts without uncertainty estimates, a critical gap for high-stakes decisions during extreme weather events.

By Jose Marie Antonio Mi\~noza, Rex Gregor Laylo, Sebastian C. Iba\~nez
More like this →