Pith Number

pith:2IFIJ3ZB

pith:2026:2IFIJ3ZBXK4PSY6GNSZVTHWUFK

not attested not anchored not stored refs pending

Enabling High-Accuracy Data Assimilation with Limited Ensembles via Machine Learning-Based Covariance Correction

Guangyao Wang, Li Zhao, Seungnam Kim, Zeng Liu, Zhaokuan Lu, Zhilin Li, Zhou Yao

A multilayer perceptron predicts the covariance gap between small and large ensembles and scales the small-ensemble matrix to raise EnKF analysis accuracy.

arxiv:2605.11639 v2 · 2026-05-12 · physics.ao-ph · math.ST · stat.TH

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\usepackage{pith}
\pithnumber{2IFIJ3ZBXK4PSY6GNSZVTHWUFK}

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Record completeness

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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

the proposed algorithm can significantly outperform the standard EnKF with the same limited ensemble size, by achieving notably higher analysis accuracy while remaining computationally efficient

C2weakest assumption

the latter being assumed to be an accurate approximation of the underlying truth (large-ensemble covariance treated as ground truth for training the MLP)

C3one line summary

An MLP predicts the covariance difference between limited and large ensembles and corrects the EnKF forecast covariance via element-wise scaling, yielding higher accuracy than standard EnKF on Lorenz-63 and Lorenz-96.

Receipt and verification

First computed	2026-05-25T02:01:23.535460Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d20a84ef21bab8f963c66cb3599ed42a9546ff9c123eae76e0dcfdf503eea00f

Aliases

arxiv: 2605.11639 · arxiv_version: 2605.11639v2 · doi: 10.48550/arxiv.2605.11639 · pith_short_12: 2IFIJ3ZBXK4P · pith_short_16: 2IFIJ3ZBXK4PSY6G · pith_short_8: 2IFIJ3ZB

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/2IFIJ3ZBXK4PSY6GNSZVTHWUFK \
  | 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: d20a84ef21bab8f963c66cb3599ed42a9546ff9c123eae76e0dcfdf503eea00f

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "19d6ce163ecb2104221b4339e3cb48c40fe0342fa206a5541a4bde70b0f5683b",
    "cross_cats_sorted": [
      "math.ST",
      "stat.TH"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "physics.ao-ph",
    "submitted_at": "2026-05-12T07:00:50Z",
    "title_canon_sha256": "8c9f830321f8f976e8ddd0724df9b6d15682faaa833cd42499fa1861dc1385ae"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.11639",
    "kind": "arxiv",
    "version": 2
  }
}