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arXiv Machine Learning

Stochastic weather generators for high-frequency wind vector time series

arXiv:2606. 09941v1 Announce Type: cross Abstract: Surface winds can vary substantially from one minute to the next, so there is scope for studying its variation on this fine time scale.

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arXiv AI
Jun 26

Sampling sea state using a diffusion model

arXiv:2606. 26389v1 Announce Type: cross Abstract: Sea state prediction is essential for operational maritime applications and coupled earth system modeling, yet current spectral wave models remain computationally prohibitive for many use cases, including online coupling to climate simulations and making probabilistic (ensemble-based) predictions.

By Jiarong Wu, Bertrand Chapron, Laure Zanna
diffusion
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arXiv Machine Learning
Aug 27

Frequency-aware forecasting for short-term typhoon gust prediction

The paper introduces WDANet, a frequency‑aware forecasting framework that uses stationary wavelet decomposition, FiLM, and a dual‑branch encoder‑decoder to separately model trend and fluctuation components in typhoon gust prediction. Applied to offshore Western Pacific wind data, WDANet outperforms ECMWF‑HRES for short lead times, achieving higher accuracy within the first 6 hours and better RMSE/MAE during extreme wind events. The study suggests WDANet could improve offshore wind power operations, disaster warnings, and risk mitigation.

By Xuefei Wang, Tingyi Liu, Heng Zhang, Shengjun Zhang
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arXiv AI
Aug 18

High-Resolution Probabilistic Data-Driven Weather Modeling with a Stretched-Grid

arXiv:2511. 23043v2 Announce Type: replace-cross Abstract: We present a probabilistic data-driven weather model providing ensembles of high spatial resolution realizations of 87 variables at arbitrary ensemble size and forecast length.

By Even Marius Nordhagen, H{\aa}vard Homleid Haugen, Magnus Sikora Ingstad, Aram Farhad Shafiq Salihi, Thomas Nils Nipen, Ivar Ambj{\o}rn Seierstad, Inger-Lise Frogner, Mariana Clare, Simon Lang, Matthew Chantry, Peter Dueben, J{\o}rn Kristiansen
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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
llmsbenchmarkssafety
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arXiv Machine Learning
Aug 13

Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling

arXiv:2608. 12271v1 Announce Type: new Abstract: Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near-surface conditions also depend on unresolved terrain and land-surface properties.

By Pedro Sousa (Department of Computer Science, University of Cambridge), Will Tebbutt (Department of Engineering, University of Cambridge), Sadiq Jaffer (Department of Computer Science, University of Cambridge), Robin Young (Department of Computer Science, University of Cambridge), Anil Madhavapeddy (Department of Computer Science, University of Cambridge), Richard E. Turner (Department of Engineering, University of Cambridge)
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