Výsledky vyhledávání - "Abad, Jose A."

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Are Deep Learning Methods Suitable for Downscaling Global Climate Projections? Review and Intercomparison of Existing Models

Autor: González-Abad, Jose, Gutiérrez, José Manuel

Deep Learning (DL) has shown promise for downscaling global climate change projections under different approaches, including Perfect Prognosis (PP) and Regional Climate Model (RCM) emulation. Unlike emulators, PP downscaling models are trained on obs

Externí odkaz: http://arxiv.org/abs/2411.05850

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A Likelihood-Based Generative Approach for Spatially Consistent Precipitation Downscaling

Autor: González-Abad, Jose

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 distribution, leading to spatially inconsistent projections when sampling

Externí odkaz: http://arxiv.org/abs/2407.04724

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Multi-variable Hard Physical Constraints for Climate Model Downscaling

Autor: González-Abad, Jose, Hernández-García, Álex, Harder, Paula, Rolnick, David, Gutiérrez, José Manuel

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 that limits their accuracy in reproducing local-scale phenomena. Stat

Externí odkaz: http://arxiv.org/abs/2308.01868

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On the use of Deep Generative Models for Perfect Prognosis Climate Downscaling

Autor: González-Abad, Jose, Baño-Medina, Jorge, Cachá, Ignacio Heredia

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 results to reproduce the observed local variability, they are based on

Externí odkaz: http://arxiv.org/abs/2305.00974

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Deep Ensembles to Improve Uncertainty Quantification of Statistical Downscaling Models under Climate Change Conditions

Autor: González-Abad, Jose, Baño-Medina, Jorge

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 variables. Nevertheless, their ability to generalize to climate change co

Externí odkaz: http://arxiv.org/abs/2305.00975

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Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches

Autor: González-Abad, Jose, Baño-Medina, Jorge, Gutiérrez, José Manuel

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 (PP) approach. Given their complexity, it is crucial to properly evalu

Externí odkaz: http://arxiv.org/abs/2302.01771

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A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters

Autor: González-Abad, Jose, García, Álvaro López, Kozlov, Valentin Y.

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 executed on specialized hardware such graphical processing units (GPUs)

Externí odkaz: http://arxiv.org/abs/2208.02498

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