arXiv Machine Learning By Francesco Pinto, Luca Lanzilao, Paco Lopez Dekker, Angela Meyer

Skillful forecasting of offshore winds from satellite scatterometer constellations

Read the original on arXiv Machine Learning →

arXiv:2607. 27152v1 Announce Type: new Abstract: Accurate intraday forecasts of offshore wind are becoming increasingly important for power system operation and the integration of growing shares of offshore wind energy.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI
1d ago

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
arXiv Machine Learning
6d ago

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)