Repository for developing code for using deep learning models in RCM emulators

This repository consists of a collection of Python scripts encapsulating the base functions needed to apply the DeepESD model in the context of RCM emulators. Additionally, it includes a Jupyter Notebook illustrating how to apply those functions.

This repository is a continuation of SantanderMetGroup/2024_Bano_Emulators_AIES. The original repository contains the code to reproduce the results from Baño-Medina et al. (2024). This fork aims to continue the code development started there. The code needed to implement the DeepESD model can be found in a Zenodo repository (Baño-Medina et al. 2022a; https://doi.org/10.5281/zenodo.6828303) associated to a previous publication (Baño-Medina et al. 2022b).

References

Baño-Medina, J., R. Manzanas, E. Cimadevilla, J. Fernández, J. González-Abad, A. S. Cofiño, and J. M. Gutiérrez, 2022a: Repository supporting the results presented in the manuscript on Downscaling Multi-Model Climate Projection Ensembles with Deep Learning (DeepESD): Contribution to CORDEX EUR-44. Zenodo.

Baño-Medina, J., R. Manzanas, E. Cimadevilla, J. Fernández, J. González-Abad, A. S. Cofiño, and J. M. Gutiérrez, 2022b: Downscaling multi-model climate projection ensembles with deep learning (deepesd): contribution to cordex eur-44. Geoscientific Model Development, 15 (17), 6747–6758.

Baño-Medina, J., M. Iturbide, J. Fernández, and J. M. Gutiérrez, 2024: Transferability and explainability of deep learning emulators for regional climate model projections: Perspectives for future applications Geoscientific Model Development, Submitted to Artificial Intelligence for the Earth Systems.