Multimodal atmospheric super-resolution with deep generative models

Dibyajyoti Chakraborty, Haiwen Guan, Jason Stock, Troy Arcomano, Guido Cervone, Romit Maulik

Open source

DOI: 10.1088/3049-4753/ae286e
Published: 2025-12-18
Container: Machine Learning: Earth
Publisher: IOP Publishing
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1088/3049-4753/ae286e,
  title = {Multimodal atmospheric super-resolution with deep generative models},
  author = {Dibyajyoti Chakraborty and Haiwen Guan and Jason Stock and Troy Arcomano and Guido Cervone and Romit Maulik},
  year = {2025},
  journal = {Machine Learning: Earth},
  doi = {10.1088/3049-4753/ae286e},
  url = {https://doi.org/10.1088/3049-4753/ae286e}
}

RIS

TY  - JOUR
TI  - Multimodal atmospheric super-resolution with deep generative models
AU  - Dibyajyoti Chakraborty
AU  - Haiwen Guan
AU  - Jason Stock
AU  - Troy Arcomano
AU  - Guido Cervone
AU  - Romit Maulik
PY  - 2025
JO  - Machine Learning: Earth
DO  - 10.1088/3049-4753/ae286e
UR  - https://doi.org/10.1088/3049-4753/ae286e
ER  -

APA

Chakraborty, D., Guan, H., Stock, J., Arcomano, T., Cervone, G., & Maulik, R. (2025). Multimodal atmospheric super-resolution with deep generative models. Machine Learning: Earth. https://doi.org/10.1088/3049-4753/ae286e

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crossref · retrieved 2026-11-10T10:01:03.880Z