Cover Image

Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Huan, Finkel, Justin, Abbot, Dorian S., Gerber, Edwin P., Weare, Jonathan
Format:	Preprint
Published:	2024
Subjects:	Atmospheric and Oceanic Physics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Using a rare event sampling technique to quantify extreme El Niño event statistics
by: Packman, Sarah, et al.
Published: (2025)

Can AI weather models predict out-of-distribution gray swan tropical cyclones?
by: Sun, Y. Qiang, et al.
Published: (2024)

AI-boosted rare event sampling to characterize extreme weather
by: Lancelin, Amaury, et al.
Published: (2025)

Predator and Prey: A Minimum Recipe for the Transition from Steady to Oscillating Precipitation in Hothouse Climates
by: Yang, Da, et al.
Published: (2024)

Overcoming set imbalance in data driven parameterization: A case study of gravity wave momentum transport
by: Yang, L. Minah, et al.
Published: (2024)

The QBO, the annual cycle, and their interactions: Isolating periodic modes with Koopman analysis
by: Valva, Claire, et al.
Published: (2024)

Climate Bistability at the Inner Edge of the Habitable Zone due to Runaway Greenhouse and Cloud Feedbacks
by: Fan, Bowen, et al.
Published: (2024)

Boosting Ensembles for Statistics of Tails at Conditionally Optimal Advance Split Times
by: Finkel, Justin, et al.
Published: (2025)

Rare event sampling for moving targets: extremes of temperature and daily precipitation in a general circulation model
by: Finkel, Justin, et al.
Published: (2025)

Interpretable Neural Networks to Predict Momentum Fluxes of Orographic Gravity Waves
by: Haslauer, Elias, et al.
Published: (2026)

Earth's Infrared Background
by: Shamir, Ofer, et al.
Published: (2025)

Hybrid weather prediction using spectral nudging toward machine-learning forecasts
by: Polichtchouk, I., et al.
Published: (2026)

Bringing statistics to storylines: rare event sampling for sudden, transient extreme events
by: Finkel, Justin, et al.
Published: (2024)

AI-informed model-analogs for understanding subseasonal-to-seasonal jet stream and North American temperature predictability
by: Landsberg, Jacob B., et al.
Published: (2025)

Do AI models predict storm impacts as accurately as physics-based models? A case study of the February 2020 storm series over the North Atlantic
by: Afargan-Gerstman, Hilla, et al.
Published: (2025)

Improving prediction of heavy rainfall in the Mediterranean with Neural Networks using both observation and Numerical Weather Prediction data
by: Pujol, Killian, et al.
Published: (2025)

Using machine learning to downscale coarse-resolution environmental variables for understanding the spatial frequency of convective storms
by: Yu, Hungjui, et al.
Published: (2025)

Emerging AI-based weather prediction models as downscaling tools
by: Koldunov, Nikolay, et al.
Published: (2024)

AI-based data assimilation: Learning the functional of analysis estimation
by: Keller, Jan D., et al.
Published: (2024)

Hybrid physics-AI outperforms numerical weather prediction for extreme precipitation nowcasting
by: Das, Puja, et al.
Published: (2024)

Physics captured by data-based methods in El Niño prediction
by: Lancia, G., et al.
Published: (2022)

Improving AI weather prediction models using global mass and energy conservation schemes
by: Sha, Yingkai, et al.
Published: (2025)

Hybrid ensemble forecasting combining physics-based and machine-learning predictions through spectral nudging
by: Polichtchouk, Inna, et al.
Published: (2026)

Buoy observation of high frequency ocean wave energy: accuracy, consistency, and concerns for predictive applications
by: Rogers, W. Erick, et al.
Published: (2025)

Intraseasonal Oscillation of Land Surface Moisture and its role in the maintenance of land ITCZ during the active phases of the Indian Summer Monsoon
by: Gautam, Pratibha, et al.
Published: (2023)

Flo: A data-driven limited-area storm surge model
by: Kristensen, Nils Melsom, et al.
Published: (2026)

Cast3: Translating numerical weather prediction principles into data-driven forecasting
by: Nai, Congyi, et al.
Published: (2026)

Omega-blocks with spatially compounding extremes over Europe are highly sensitive to remote atmospheric drivers
by: Mittermeier, Magdalena, et al.
Published: (2026)

Predictability of north Pacific blocking events : Analogue based analysis of historical MIROC6 simulations
by: Xavier, Anupama K, et al.
Published: (2025)

Asymmetric response of the North Atlantic gyres to the North Atlantic Oscillation
by: Bhagtani, Dhruv, et al.
Published: (2024)

Transfer Learning for Emulating Ocean Climate Variability across $CO_2$ forcing
by: Dheeshjith, Surya, et al.
Published: (2024)

The rise of data-driven weather forecasting
by: Ben-Bouallegue, Zied, et al.
Published: (2023)

Skilful global seasonal predictions from a machine learning weather model trained on reanalysis data
by: Kent, Chris, et al.
Published: (2025)

AIFS -- ECMWF's data-driven forecasting system
by: Lang, Simon, et al.
Published: (2024)

The Tara Polaris scientific vision: Advancing our understanding of the central Arctic Ocean to better address life in the Earth System
by: Babin, Marcel, et al.
Published: (2026)

Using data assimilation tools to dissect GraphDOP
by: Laloyaux, Patrick, et al.
Published: (2025)

Using Unstable Periodic Orbits to Understand Blocking Behaviour in a Low Order Land-Atmosphere Model
by: Hamilton, Oisín, et al.
Published: (2025)

Technical overview and architecture of the FastNet Machine Learning weather prediction model, version 1.0
by: Daub, Eric G., et al.
Published: (2025)

Can Transfer Learning be Used to Identify Tropical State-Dependent Bias Relevant to Midlatitude Subseasonal Predictability?
by: Mayer, Kirsten J., et al.
Published: (2024)

Learning effective Sargassum transport dynamics from limited drifter observations
by: Beron-VEra, F. J., et al.
Published: (2026)