Skip to content
DraconiticResearch
Search Sources API
203,948 studies31 sourcesUpdated 40 min ago
PreprintPublisher termsEarthArXiv

Preserving Probabilistic Meaning in Weather-to-Text Generation

Related

  1. Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction
    Anna Asch, Raphael Rossellini, Pedram Hassanzadeh +1
    arXiv (Cornell University)PreprintLicence not statedFull text6 citedAtmospheric science & meteorologyOpenAlex
  2. SDECast: Probabilistic Weather Forecasting in Continuous Time with Neural SDEs
    Maria Marchenko, Martin Andrae, Fredrik Lindsten +1
    arXivPreprintarXiv licenceFull textAtmospheric science & meteorologyStatisticsarXiv
  3. Probabilistic AI models outperform numerical weather prediction models on record breaking heat extremes
    Jakob Benjamin Wessel, Fiona Spuler, Christopher A. T. Ferro +2
    arXivPreprintCC BY 4.0Full textGeophysics & seismologyStatisticsarXiv
  4. Learning joint probabilistic weather forecasts from station observations alone
    Chaeyeon Yi, Yun Am Seo
    arXivPreprintCC BY 4.0Full textAtmospheric science & meteorologyStatisticsarXiv
  5. Butterfly Effect and the Kinetic Energy Cascade in Probabilistic Machine Learning Weather Prediction Models
    Jiakai Chen, Joel Oskarsson, Simon Driscoll +1
    arXivPreprintCC BY 4.0Full textAtmospheric science & meteorologyarXiv
BibTeX
@misc{ding2026preserving,
  title = {{Preserving Probabilistic Meaning in Weather-to-Text Generation}},
  author = {Wanfeng Ding and Pengxin Lin and Hanif Bhuiyan},
  howpublished = {EarthArXiv},
  year = {2026},
  month = oct,
  doi = {10.31223/x5tr4z},
  url = {https://eartharxiv.org/repository/view/15308/},
}
RIS
TY  - UNPB
TI  - Preserving Probabilistic Meaning in Weather-to-Text Generation
AU  - Wanfeng Ding
AU  - Pengxin Lin
AU  - Hanif Bhuiyan
PY  - 2026
DA  - 2026/10/01
PB  - EarthArXiv
DO  - 10.31223/x5tr4z
UR  - https://eartharxiv.org/repository/view/15308/
ER  -