18 citations · 38 across the 8 of their papers we have counts for
8 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…
Are Deep Learning Methods Suitable for Downscaling Global Climate Projections? An Intercomparison for Temperature and Precipitation over Spain
Jose González-Abad, José Manuel Gutiérrez
Deep Learning (DL) has shown promise for downscaling global climate change projections under different approaches, including Perfect Prognosis (PP) and Regional Climate Model (RCM)…
A Likelihood-Based Generative Approach for Spatially Consistent Precipitation Downscaling
Jose González-Abad
Deep learning has emerged as a promising tool for precipitation downscaling. However, current models rely on likelihood-based loss functions to properly model the precipitation dis…
Multi-variable Hard Physical Constraints for Climate Model Downscaling
Jose González-Abad, Álex Hernández-García, Paula Harder +2
Global Climate Models (GCMs) are the primary tool to simulate climate evolution and assess the impacts of climate change. However, they often operate at a coarse spatial resolution…
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…
A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters
Jose González-Abad, Álvaro López García, Valentin Y. Kozlov
Deep learning has been postulated as a solution for numerous problems in different branches of science. Given the resource-intensive nature of these models, they often need to be e…