140 citations · 152 across the 6 of their papers we have counts for
6 papers
CORDEX-ML-Bench: A Benchmark for Data-Driven Regional Climate Downscaling -Experiment Design and Overview
Neelesh Rampal, José González-Abad, Henry Addison +34
Machine learning (ML) has emerged as a cost-effective approach to complement dynamical downscaling for producing high-resolution regional climate projections. However, the absence…
Transferability and explainability of deep learning emulators for regional climate model projections: Perspectives for future applications
Jorge Bano-Medina, Maialen Iturbide, Jesus Fernandez +1
Regional climate models (RCMs) are essential tools for simulating and studying regional climate variability and change. However, their high computational cost limits the production…
Deep Ensembles to Improve Uncertainty Quantification of Statistical Downscaling Models under Climate Change Conditions
Jose González-Abad, Jorge Baño-Medina
Recently, deep learning has emerged as a promising tool for statistical downscaling, the set of methods for generating high-resolution climate fields from coarse low-resolution var…
On the use of Deep Generative Models for Perfect Prognosis Climate Downscaling
Jose González-Abad, Jorge Baño-Medina, Ignacio Heredia Cachá
Deep Learning has recently emerged as a perfect prognosis downscaling technique to compute high-resolution fields from large-scale coarse atmospheric data. Despite their promising…
Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches
Jose González-Abad, Jorge Baño-Medina, José Manuel Gutiérrez
Deep learning (DL) has emerged as a promising tool to downscale climate projections at regional-to-local scales from large-scale atmospheric fields following the perfect-prognosis…
Implementation of FAIR principles in the IPCC: The WGI AR6 Atlas repository
Maialen Iturbide, Jesús Fernández, José M. Gutiérrez +22
The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) has adopted the FAIR Guiding Principles. The Atlas chapter of Working Group I (WGI) is pre…