Saved in:
| Main Authors: | Navarro, Maria Conchita Agana, Li, Geng, Wolf, Theo, Pérez-Ortiz, María |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.12147 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Assessing the Robustness of Climate Foundation Models under No-Analog Distribution Shifts
by: Navarro, Maria Conchita Agana, et al.
Published: (2026)
by: Navarro, Maria Conchita Agana, et al.
Published: (2026)
A machine learning model for skillful climate system prediction
by: Zhou, Chenguang, et al.
Published: (2025)
by: Zhou, Chenguang, et al.
Published: (2025)
Augmented CARDS: A machine learning approach to identifying triggers of climate change misinformation on Twitter
by: Rojas, Cristian, et al.
Published: (2024)
by: Rojas, Cristian, et al.
Published: (2024)
Regional climate risk assessment from climate models using probabilistic machine learning
by: Wan, Zhong Yi, et al.
Published: (2024)
by: Wan, Zhong Yi, et al.
Published: (2024)
Tackling extreme urban heat: a machine learning approach to assess the impacts of climate change and the efficacy of climate adaptation strategies in urban microclimates
by: Buster, Grant, et al.
Published: (2024)
by: Buster, Grant, et al.
Published: (2024)
Toward generative machine learning for boosting ensembles of climate simulations
by: Gooya, Parsa, et al.
Published: (2026)
by: Gooya, Parsa, et al.
Published: (2026)
XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change
by: Wei, Jiawen, et al.
Published: (2025)
by: Wei, Jiawen, et al.
Published: (2025)
Assessing wildfire susceptibility in Iran: Leveraging machine learning for geospatial analysis of climatic and anthropogenic factors
by: Masoudian, Ehsan, et al.
Published: (2025)
by: Masoudian, Ehsan, et al.
Published: (2025)
Evaluating the transferability potential of deep learning models for climate downscaling
by: Prasad, Ayush, et al.
Published: (2024)
by: Prasad, Ayush, et al.
Published: (2024)
Accelerating exoplanet climate modelling: A machine learning approach to complement 3D GCM grid simulations
by: Plaschzug, Alexander, et al.
Published: (2025)
by: Plaschzug, Alexander, et al.
Published: (2025)
Embedding machine-learnt sub-grid variability improves climate model biases
by: Giles, Daniel, et al.
Published: (2024)
by: Giles, Daniel, et al.
Published: (2024)
Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis data
by: De Clercq, Djavan, et al.
Published: (2024)
by: De Clercq, Djavan, et al.
Published: (2024)
Regional climate projections using a deep-learning-based model-ranking and downscaling framework: Application to European climate zones
by: Loganathan, Parthiban, et al.
Published: (2025)
by: Loganathan, Parthiban, et al.
Published: (2025)
Transferring climate change physical knowledge
by: Immorlano, Francesco, et al.
Published: (2023)
by: Immorlano, Francesco, et al.
Published: (2023)
Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers
by: Panja, Madhurima, et al.
Published: (2025)
by: Panja, Madhurima, et al.
Published: (2025)
Machine learning models for predicting catastrophe bond coupons using climate data
by: Kończal, Julia, et al.
Published: (2025)
by: Kończal, Julia, et al.
Published: (2025)
ClimateQ&A: Bridging the gap between climate scientists and the general public
by: De La Calzada, Natalia, et al.
Published: (2024)
by: De La Calzada, Natalia, et al.
Published: (2024)
MIRANDA: MId-feature RANk-adversarial Domain Adaptation toward climate change-robust ecological forecasting with deep learning
by: Jiang, Yuchang, et al.
Published: (2026)
by: Jiang, Yuchang, et al.
Published: (2026)
Density correction for multivariate spatial fields of global climate model output using deep learning
by: Majumder, Reetam, et al.
Published: (2024)
by: Majumder, Reetam, et al.
Published: (2024)
Correlation inference attacks against machine learning models
by: Creţu, Ana-Maria, et al.
Published: (2021)
by: Creţu, Ana-Maria, et al.
Published: (2021)
The land use-climate change-biodiversity nexus in European islands stakeholders
by: Moustakas, Aristides, et al.
Published: (2025)
by: Moustakas, Aristides, et al.
Published: (2025)
On the importance of learning non-local dynamics for stable data-driven climate modeling: A 1D gravity wave-QBO testbed
by: Pahlavan, Hamid A., et al.
Published: (2024)
by: Pahlavan, Hamid A., et al.
Published: (2024)
Robustness of AI-based weather forecasts in a changing climate
by: Rackow, Thomas, et al.
Published: (2024)
by: Rackow, Thomas, et al.
Published: (2024)
Dynamical-generative downscaling of climate model ensembles
by: Lopez-Gomez, Ignacio, et al.
Published: (2024)
by: Lopez-Gomez, Ignacio, et al.
Published: (2024)
A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics
by: Sorensen, Benedikt Barthel, et al.
Published: (2024)
by: Sorensen, Benedikt Barthel, et al.
Published: (2024)
Better than classical? The subtle art of benchmarking quantum machine learning models
by: Bowles, Joseph, et al.
Published: (2024)
by: Bowles, Joseph, et al.
Published: (2024)
Prospects of federated machine learning in fluid dynamics
by: San, Omer, et al.
Published: (2022)
by: San, Omer, et al.
Published: (2022)
Learning to generate physical ocean states: Towards hybrid climate modeling
by: Meunier, Etienne, et al.
Published: (2025)
by: Meunier, Etienne, et al.
Published: (2025)
Advancing climate model interpretability: Feature attribution for Arctic melt anomalies
by: Ale, Tolulope, et al.
Published: (2025)
by: Ale, Tolulope, et al.
Published: (2025)
When to retrain a machine learning model
by: Florence, Regol, et al.
Published: (2025)
by: Florence, Regol, et al.
Published: (2025)
Understanding with toy surrogate models in machine learning
by: Páez, Andrés
Published: (2024)
by: Páez, Andrés
Published: (2024)
Generative assimilation and prediction for weather and climate
by: Yang, Shangshang, et al.
Published: (2025)
by: Yang, Shangshang, et al.
Published: (2025)
Enhancing molecular dynamics with equivariant machine-learned densities
by: Bogojeski, Mihail, et al.
Published: (2026)
by: Bogojeski, Mihail, et al.
Published: (2026)
On the importance of structural identifiability for machine learning with partially observed dynamical systems
by: Norden, Janis, et al.
Published: (2025)
by: Norden, Janis, et al.
Published: (2025)
Causal hybrid modeling with double machine learning
by: Cohrs, Kai-Hendrik, et al.
Published: (2024)
by: Cohrs, Kai-Hendrik, et al.
Published: (2024)
Suppressing unknown disturbances to dynamical systems using machine learning
by: Restrepo, Juan G., et al.
Published: (2023)
by: Restrepo, Juan G., et al.
Published: (2023)
Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts
by: Benítez-Andrades, José Alberto, et al.
Published: (2024)
by: Benítez-Andrades, José Alberto, et al.
Published: (2024)
A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
by: Kim, Dongjin, et al.
Published: (2025)
by: Kim, Dongjin, et al.
Published: (2025)
Quantifying uncertainty in climate projections with conformal ensembles
by: Harris, Trevor, et al.
Published: (2024)
by: Harris, Trevor, et al.
Published: (2024)
A comparative analysis of machine learning models in SHAP analysis
by: Lin, Justin, et al.
Published: (2026)
by: Lin, Justin, et al.
Published: (2026)
Similar Items
-
Assessing the Robustness of Climate Foundation Models under No-Analog Distribution Shifts
by: Navarro, Maria Conchita Agana, et al.
Published: (2026) -
A machine learning model for skillful climate system prediction
by: Zhou, Chenguang, et al.
Published: (2025) -
Augmented CARDS: A machine learning approach to identifying triggers of climate change misinformation on Twitter
by: Rojas, Cristian, et al.
Published: (2024) -
Regional climate risk assessment from climate models using probabilistic machine learning
by: Wan, Zhong Yi, et al.
Published: (2024) -
Tackling extreme urban heat: a machine learning approach to assess the impacts of climate change and the efficacy of climate adaptation strategies in urban microclimates
by: Buster, Grant, et al.
Published: (2024)