# Data-driven ensemble prediction of the global ocean

arXiv: https://arxiv.org/abs/2603.19591 · HTML: https://mlsanity.com/paper/2603.19591/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 20 Mar 2026.

## Builds on (most cited first, 8 of 8)

- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [FuXi: A cascade machine learning forecasting system for 15-day global weather forecast](https://mlsanity.com/paper/2306.12873/) (2023)
- [FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead](https://mlsanity.com/paper/2304.02948/) (2023)
- [FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting](https://mlsanity.com/paper/2405.05925/) (2024)
- [LangYa: Revolutionizing Cross-Spatiotemporal Ocean Forecasting](https://mlsanity.com/paper/2412.18097/) (2024)
- [FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution](https://mlsanity.com/paper/2506.03210/) (2025)
- [FuXi-RTM: A Physics-Guided Prediction Framework with Radiative Transfer Modeling](https://mlsanity.com/paper/2503.19940/) (2025)
- [AviaSafe: A Physics-Informed Data-Driven Model for Aviation Safety-Critical Cloud Forecasts](https://mlsanity.com/paper/2602.22298/) (2026)

## Built on by (most cited first, 2 of 2)

- [Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting](https://mlsanity.com/paper/2605.15470/) (2026)
- [OceanFlow: Coherent Flow Matching for Probabilistic Ocean Modeling](https://mlsanity.com/paper/2610.15518/) (2026)

## Abstract (arXiv)

Data-driven models have advanced deterministic ocean forecasting, but extending machine learning to probabilistic global ocean prediction remains an open challenge. Here we introduce FuXi-ONS, the first machine-learning ensemble forecasting system for the global ocean, providing 5-day forecasts on a global 1deg grid up to 365 days for sea-surface temperature, sea-surface height, subsurface temperature, salinity and ocean currents. Rather than relying on repeated integration of computationally expensive numerical models, FuXi-ONS learns physically structured perturbations and incorporates an atmospheric encoding module to stabilize long-range forecasts. Evaluated against GLORYS12 reanalysis, FuXi-ONS improves both ensemble-mean skill and probabilistic forecast quality relative to deterministic and noise-perturbed baselines, and shows competitive performance against established seasonal forecast references for SST and Ni~no3.4 variability, while running orders of magnitude faster than conventional ensemble systems. These results provide a strong example of machine learning advancing a core problem in ocean science, and establish a practical path toward efficient probabilistic ocean forecasting and climate risk assessment.