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

no code implementations • 27 Apr 2023 • 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 variables.

Uncertainty Quantification

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

1 code implementation • 27 Apr 2023 • 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.

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

1 code implementation • 3 Feb 2023 • 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 (PP) approach.

Explainable Artificial Intelligence (XAI)