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Detecting Weather Fronts from Satellite Observations: What Does Reanalysis Add? — arXiv:2611.08148

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.08148
License (as archived): CC0-1.0
Archived: 2026-11-06T14:50:53.358Z
SHA-256 of the archived text: 9c3660a41e77f414f338622ab0bd24f8bc180b60d0bfa3ca6ccf6af95b5f6a14
Stable URL of this page: https://metraipolis.com/documento/dc_f308331bcaa7

How to cite

Detecting Weather Fronts from Satellite Observations: What Does Reanalysis Add? — arXiv:2611.08148 — arXiv (en), https://arxiv.org/abs/2611.08148 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f308331bcaa7 (Metraipolis archive dc_f308331bcaa7, sha256 9c3660a41e77f414f338622ab0bd24f8bc180b60d0bfa3ca6ccf6af95b5f6a14)

BibTeX

@misc{dcf308331bcaa7,
  title = {Detecting Weather Fronts from Satellite Observations: What Does Reanalysis Add? — arXiv:2611.08148},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.08148},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f308331bcaa7; sha256 9c3660a41e77f414f338622ab0bd24f8bc180b60d0bfa3ca6ccf6af95b5f6a14},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.08148. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Weather fronts are transition regions between air masses with contrasting thermodynamic properties and are associated with mid-latitude precipitation and severe weather. We investigate machine learning (ML) methods for detecting weather fronts and quantify the contribution of reanalysis data to performance. We compare two configurations of the same ML model for pixel-wise frontal classification over the North Atlantic and Europe: (1) using only geostationary infrared satellite observations, and (2) additionally using 28 ERA5 reanalysis fields. The satellite-only model achieves a neighbourhood critical success index at a 150 km tolerance (CSI@150) of $0.402$, which lies within the range of agreement between analyses from different meteorological services. The satellite$+$ERA5 model achieves $0.575$, an improvement of $0.173$. Our analysis shows that low-level winds are the least predictable from the satellite representation ($R^2 = 0.23$, compared with $0.88$ for temperature), while removing winds or moisture causes the largest decreases in detection performance.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Computer Vision and Pattern Recognition (cs.CV); Atmospheric and Oceanic Physics (physics.ao-ph).
Pagina del lavoro: https://arxiv.org/abs/2611.08148 — 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 2026-11-06T14:21:42.643Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).