Selecting Observationally Constrained Global Climate Model Ensembles Using Autoencoders and Transfer Learning

This code is based on the publication: Ibebuchi, C. C., Akinyemi, O., & Abu, I. O. (2025). Selecting observationally constrained Global Climate Model ensembles using autoencoders and transfer learning. Journal of Geophysical Research: Machine Learning and Computation, 2(1), e2024JH000528.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-02-23
DOI
https://doi.org/10.5281/zenodo.14914621
Citations
1
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
1.11
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article

Selecting Observationally Constrained Global Climate Model Ensembles Using Autoencoders and Transfer Learning

1 citations
Zenodo (CERN European Organization for Nuclear Research)
Climate variability and models
1.11
article

Selecting Observationally Constrained Global Climate Model Ensembles Using Autoencoders and Transfer Learning

article en
1 citations

Abstract

This code is based on the publication: Ibebuchi, C. C., Akinyemi, O., & Abu, I. O. (2025). Selecting observationally constrained Global Climate Model ensembles using autoencoders and transfer learning. Journal of Geophysical Research: Machine Learning and Computation, 2(1), e2024JH000528.

Zenodo (CERN European Organization for Nuclear Research)
Kent State University (US)
Climate action
Openalex Percentile: Top 17%
Climate variability and models
1.11
DOIOpen Access PDFOpenAlex
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Selecting Observationally Constrained Global Climate Model Ensembles Using Autoencoders and Transfer Learning · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS