Pith Number

pith:JCS2PZMK

pith:2026:JCS2PZMKKNKGL5BGOA5P2XEZYL

not attested not anchored not stored refs resolved

Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting

Daniel Holmberg, Erik Larsson, Fredrik Lindsten, Joel Oskarsson, Teemu Roos

A probabilistic graph neural network for ocean forecasting achieves the lowest errors on a global benchmark while providing uncertainty estimates.

arxiv:2605.15470 v1 · 2026-05-14 · cs.LG · physics.ao-ph

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\pithnumber{JCS2PZMKKNKGL5BGOA5P2XEZYL}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

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.

C2weakest assumption

That K-means cluster meshes adapt sufficiently well to irregular sea-surface geometry to allow accurate and efficient scaling of the graph neural network to global 0.25-degree and regional 2 km grids.

C3one line summary

Njord is a probabilistic GNN model using latent variables and adaptive K-means meshes that produces ensemble forecasts and outperforms deterministic ML baselines on global OceanBench and Baltic Sea domains.

References

46 extracted · 46 resolved · 2 Pith anchors

[1] From observation to information and users: The Copernicus Marine Service perspective.Frontiers in Marine Science, 6:234, 2019 2019

[2] Evolution of the Copernicus Marine Service global ocean analysis and forecasting high-resolution system: Potential benefit for a wide range of users 2023

[3] Nemo-Nordic 2.0: Operational marine forecast model for the Baltic Sea.Geoscientific Model Development, 14(9):5731–5749, 2021 2021

[4] GLONET: Mercator’s end-to-end neural global ocean forecasting system.Journal of Geophysical Research: Machine Learning and Computation, 2(3), 2025 2025

[5] Accurate Mediter- ranean Sea forecasting via graph-based deep learning.Scientific Reports, 15(45051), 2025 2025

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:01:00.210363Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

48a5a7e58a535465f426703afd5c99c2f3671ec8441179922852d07060f154d8

Aliases

arxiv: 2605.15470 · arxiv_version: 2605.15470v1 · doi: 10.48550/arxiv.2605.15470 · pith_short_12: JCS2PZMKKNKG · pith_short_16: JCS2PZMKKNKGL5BG · pith_short_8: JCS2PZMK

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/JCS2PZMKKNKGL5BGOA5P2XEZYL \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 48a5a7e58a535465f426703afd5c99c2f3671ec8441179922852d07060f154d8

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "47eb8649e97a100143f620904b773c8886acb8268398657e7bf757c7df19d457",
    "cross_cats_sorted": [
      "physics.ao-ph"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T23:17:21Z",
    "title_canon_sha256": "5d3bbf7b839a04a4e3d864909cc996904a3eb3e0183898e750f2ba9618d661cd"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15470",
    "kind": "arxiv",
    "version": 1
  }
}