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ORCIDJorge Luis Baño-Medina
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2020 – today
- 2024
[i5]Jorge Baño-Medina, Agniv Sengupta, Allison Michaelis, Luca Delle Monache, Julie Kalansky, Duncan Watson-Parris:
Harnessing AI data-driven global weather models for climate attribution: An analysis of the 2017 Oroville Dam extreme atmospheric river. CoRR abs/2409.11605 (2024)- 2023
- [i4]Jose González-Abad, Jorge Baño-Medina, José Manuel Gutiérrez Llorente:
Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches. CoRR abs/2302.01771 (2023) - [i3]Jose González-Abad, Jorge Baño-Medina, Ignacio Heredia Cacha:
On the use of Deep Generative Models for Perfect Prognosis Climate Downscaling. CoRR abs/2305.00974 (2023) - [i2]Jose González-Abad, Jorge Baño-Medina:
Deep Ensembles to Improve Uncertainty Quantification of Statistical Downscaling Models under Climate Change Conditions. CoRR abs/2305.00975 (2023) - [i1]Jorge Luis Baño-Medina, Maialen Iturbide, Jesús Fernández, José Manuel Gutiérrez:
Transferability and explainability of deep learning emulators for regional climate model projections: Perspectives for future applications. CoRR abs/2311.03378 (2023) - 2020
- [c1]Jorge Baño-Medina
:
Understanding Deep Learning Decisions in Statistical Downscaling Models. CI 2020: 79-85
2010 – 2019
- 2019
- [j1]Maialen Iturbide, Joaquín Bedia, Sixto Herrera, Jorge Luis Baño-Medina, Jesús Fernández, María Dolores Frías, Rodrigo Manzanas, Daniel San Martín Segura, Ezequiel Cimadevilla Álvarez, Antonio S. Cofiño, José Manuel Gutiérrez Llorente:
The R-based climate4R open framework for reproducible climate data access and post-processing. Environ. Model. Softw. 111: 42-54 (2019)
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