Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting

TL;DR

Njord is a novel probabilistic graph neural network model for ocean forecasting that provides uncertainty estimates and outperforms deterministic models on global and regional ocean data.

Contribution

It introduces Njord, combining deep latent variables with graph neural networks for scalable probabilistic ocean forecasting.

Findings

Njord achieves the lowest errors on OceanBench benchmark.

It provides accurate uncertainty estimates in forecasts.

Strong performance on both global and regional ocean datasets.

Abstract

Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a probabilistic data-driven model for ocean forecasting, applicable to both global and regional domains. Njord combines a deep latent variable framework with a graph neural network architecture, enabling sampling each forecast step in a single forward pass. We apply Njord globally at 0.25{\deg} resolution and regionally to the Baltic Sea at 2 km resolution. To scale to these large ocean grids we introduce K-means cluster meshes that adapt to irregular sea surface geometry. Experiments demonstrate strong performance on both domains compared to deterministic machine learning baselines, while also providing uncertainty estimates from the sampled ensemble forecasts. On the global OceanBench benchmark, Njord achieves the lowest errors on average across upper-ocean variables when evaluated against real-world observations, with the largest improvements in surface temperature prediction.