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GOES-East full-disk AI nowcasting of cloud evolution in observation space — arXiv:2608.20540

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.20540
License (as archived): CC0-1.0
Archived: 2026-10-18T11:14:37.697Z
SHA-256 of the archived text: 41db9ba0cd17c083b96817da91c7e811fe7f0acbe6e87d6e9f107f4a3d9f7514
Stable URL of this page: https://metraipolis.com/documento/dc_d35cb2056cc2

How to cite

GOES-East full-disk AI nowcasting of cloud evolution in observation space — arXiv:2608.20540 — arXiv (en), https://arxiv.org/abs/2608.20540 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d35cb2056cc2 (Metraipolis archive dc_d35cb2056cc2, sha256 41db9ba0cd17c083b96817da91c7e811fe7f0acbe6e87d6e9f107f4a3d9f7514)

BibTeX

@misc{dcd35cb2056cc2,
  title = {GOES-East full-disk AI nowcasting of cloud evolution in observation space — arXiv:2608.20540},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.20540},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d35cb2056cc2; sha256 41db9ba0cd17c083b96817da91c7e811fe7f0acbe6e87d6e9f107f4a3d9f7514},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.20540. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Clouds affect aviation, solar energy, remote sensing, and storm prediction, yet they remain among the hardest atmospheric features to forecast, particularly at convective scales. Because clouds are shaped by processes spanning a wide range of space and time scales, numerical weather prediction, extrapolation methods, and existing machine learning (ML) approaches are each limited by some combination of accuracy, domain size, and temporal resolution. We present DOP+, an ML approach for clouds that forecasts GOES-East full-disk infrared brightness temperatures by extending direct observation prediction (DOP) with conditioning on meteorological fields. The domain covers tropical, midlatitude, and marine regimes across $\sim 10^8 ~ km^2$, roughly a fifth of Earth's surface. DOP+ forecasts cloud evolution at 10-minute resolution and outperforms persistence, synoptic-scale NWP, and a pure DOP baseline across 0-6 h lead times in fractions skill score and mean absolute error skill score. Convective structure is retained out to 2-3 h. DOP+ thus achieves a state-of-the-art combination of accuracy, temporal resolution, and spatial coverage. Our work lays the foundation for fully global cloud nowcasting at convective timescales.

Tema degli esploratori: terra, clima ed energia (retro-catalogo arXiv: annunci del 2026-08-24 in physics.ao-ph).
Categorie arXiv: Atmospheric and Oceanic Physics (physics.ao-ph).
Pagina del lavoro: https://arxiv.org/abs/2608.20540 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T11:08:59.858Z dalla pagina https://arxiv.org/catchup/physics.ao-ph/2026-08-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).