3 papers
physics.ao-ph2026
Evaluation of medium range machine learning models for sub-seasonal prediction
Catherine de Burgh-Day, Chen Li, Debra Hudson +4
The performance of two machine learning (ML) atmosphere models - GraphCast and FourCastNetV2 - is evaluated in the context of sub-seasonal prediction, including their ability to re…
physics.ao-ph2026
Comparing Ocean Forecasts Driven with Machine Learning-based and Physics-based Atmospheric Forcings
Xiaobing Zhou, Frank Colberg, Debra Hudson +4
Operational ocean forecasting systems conventionally employ dynamical ocean models driven by atmospheric forcing derived from numerical weather prediction (NWP) models. Recent adva…
physics.ao-ph2025
Statistical post-processing yields accurate probabilistic forecasts from Artificial Intelligence weather models
Belinda Trotta, Robert Johnson, Catherine de Burgh-Day +7
Artificial Intelligence (AI) weather models are now reaching operational-grade performance for some variables, but like traditional Numerical Weather Prediction (NWP) models, they…