Physics-informed deep learning framework to model intense precipitation events at super resolution.

Teufel B, Carmo F, Sushama L, Sun L, Khaliq MN, Bélair S, Shamseldin A, Kumar DN, Vaze J

DOI: 10.1186/s40562-023-00272-z
Published: 2023
Container: Geoscience letters
Publisher: Not recorded
Open access: yes

Credibility signals

limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
cautionDOI resolves: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility. Source: DOAJ; license: CC0
not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredROR affiliation: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

Cite this work

BibTeX

@article{allodium:10.1186/s40562-023-00272-z,
  title = {Physics-informed deep learning framework to model intense precipitation events at super resolution.},
  author = {Teufel B and Carmo F and Sushama L and Sun L and Khaliq MN and Bélair S and Shamseldin A and Kumar DN and Vaze J},
  year = {2023},
  journal = {Geoscience letters},
  doi = {10.1186/s40562-023-00272-z},
  url = {https://doi.org/10.1186/s40562-023-00272-z}
}

RIS

TY  - JOUR
TI  - Physics-informed deep learning framework to model intense precipitation events at super resolution.
AU  - Teufel B
AU  - Carmo F
AU  - Sushama L
AU  - Sun L
AU  - Khaliq MN
AU  - Bélair S
AU  - Shamseldin A
AU  - Kumar DN
AU  - Vaze J
PY  - 2023
JO  - Geoscience letters
DO  - 10.1186/s40562-023-00272-z
UR  - https://doi.org/10.1186/s40562-023-00272-z
ER  -

APA

B, T., F, C., L, S., L, S., MN, K., S, B., A, S., DN, K., & J, V. (2023). Physics-informed deep learning framework to model intense precipitation events at super resolution.. Geoscience letters. https://doi.org/10.1186/s40562-023-00272-z

Source records

pubmed · retrieved 2026-09-26T18:25:02.594Z