arXiv Machine Learning By Emma Kasteleyn, Timo Maier, Axel Lauer, Veronika Eyring, Pierre Gentine, Ana Lucic

PhysMetrics.Weather: An Evaluation Framework for Physical Consistency in ML Weather Models

Read the original on arXiv Machine Learning →

arXiv:2606. 10642v1 Announce Type: new Abstract: Machine learning weather prediction (MLWP) models have achieved impressive forecasting performance at a small fraction of the computational costs required for traditional physics-based methods.

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
Jun 18

Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting

arXiv:2406. 14399v4 Announce Type: replace Abstract: The development of Time-Series Forecasting (TSF) models is often constrained by the lack of comprehensive datasets, especially in Global Station Weather Forecasting (GSWF), where existing datasets are small, temporally short, and spatially sparse.

By Tao Han, Zhibin Wen, Zhenghao Chen, Dazhao Du, Song Guo, Lei Bai
arXiv Machine Learning
Sep 17

Every Fixed Metric Has a Blind Spot: A Learned Atmospheric Critic for Scoring Forecast Realism

The paper introduces a learned atmospheric critic that discriminates between real weather data and model outputs to produce a realism score. Unlike fixed metrics, the discriminator adapts to the specific failure modes of a given model, effectively detecting various synthetic corruptions in ERA5 data. Experiments show the learned critic outperforms existing metrics and reveals that realism decreases with longer forecast lead times, favoring numerical over machine‑learning models.

By Younes Elberkennou, Dmitri Demler, Thierry Meier, Luca Rispoli, Fanny Lehmann, Joel Oskarsson
arXiv Machine Learning
Jun 9

Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts

arXiv:2605. 30184v2 Announce Type: replace Abstract: While AI weather models excel at short-to-medium range forecasts (up to 15 days), they frequently suffer from ill-defined "instabilities" when rolled out over longer horizons.

By Fanny Lehmann, Firat Ozdemir, Yun Cheng, Torsten Hoefler, Sebastian Schemm, Benedikt Soja, Siddhartha Mishra