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.14914620
Primary Topic
Prenatal Screening and Diagnostics
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Zenodo (CERN European Organization for Nuclear Research)
Prenatal Screening and Diagnostics
article

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

article en

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 42%
Prenatal Screening and Diagnostics
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