Pith Number

pith:4M6JU3YR

pith:2025:4M6JU3YR7YAQCTGSYYJ5C6G2GQ

not attested not anchored not stored refs resolved

Predicting Forecast Error for the HRRR Using LSTM Neural Networks: A Comparative Study Using New York and Oklahoma State Mesonets

Chris D. Thorncroft, David Aaron Evans, Jay C. Rothenberger, Kara J. Sulia, Lauriana C. Gaudet, Nick P. Bassill

LSTM networks trained on mesonet data predict HRRR precipitation forecast errors with 48 percent average improvement.

arxiv:2512.14898 v2 · 2025-12-16 · physics.ao-ph

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\usepackage{pith}
\pithnumber{4M6JU3YR7YAQCTGSYYJ5C6G2GQ}

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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

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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

LSTMs predict precipitation error most accurately, providing, on average, a 48% improvement relative to the HRRR forecast, followed by wind error, providing, on average, a 15% improvement, and then temperature error, providing, on average, a 25% improvement.

C2weakest assumption

The LSTM trained on historical mesonet-HRRR pairs will continue to predict future forecast errors accurately without major changes in model behavior or observation quality.

C3one line summary

LSTM networks predict HRRR forecast errors with average improvements of 48% for precipitation, 25% for temperature, and 15% for wind using mesonet ground truth.

References

72 extracted · 72 resolved · 0 Pith anchors

[1] Case history inversion and interpretation of a 3d seismic data set from the ouachita mountains, oklahoma 2009 · doi:10.1190/1.3073005

[2] New york state climate change projections methodology report 2023

[3] A. G. Barnston and C. F. Ropelewski. Prediction of enso episodes using canonical correlation analysis. J. Climate, 5: 0 1316--1345, 1992. doi:10.1175/1520-0442(1992)005<1316:POEEUC>2.0.CO;2 1992 · doi:10.1175/1520-0442(1992)005

[4] Bishop and Hugh Bishop 2023 · doi:10.1007/978-3-031-45468-4

[5] B. K. Blaylock, J. D. Horel, and S. T. Liston. Cloud archiving and data mining of high-resolution rapid refresh forecast model output. Computers & Geosciences, 109: 0 43--50, 2017. doi:10.1016/j.cageo 2017 · doi:10.1016/j.cageo.2017.08.005

Receipt and verification

First computed	2026-05-17T23:39:00.496224Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e33c9a6f11fe01014cd2c613d178da341294bcbc2eadfd7b993b7d20329949cf

Aliases

arxiv: 2512.14898 · arxiv_version: 2512.14898v2 · doi: 10.48550/arxiv.2512.14898 · pith_short_12: 4M6JU3YR7YAQ · pith_short_16: 4M6JU3YR7YAQCTGS · pith_short_8: 4M6JU3YR

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/4M6JU3YR7YAQCTGSYYJ5C6G2GQ \
  | 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: e33c9a6f11fe01014cd2c613d178da341294bcbc2eadfd7b993b7d20329949cf

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "9299902930f4384a308119bbb98f7acaff899acd995444595bada731c8a411dd",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "physics.ao-ph",
    "submitted_at": "2025-12-16T20:22:41Z",
    "title_canon_sha256": "b0dc5256e94af5d23fbe081221333ab338deef9de2b3a1261f280ece11bd5ddf"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2512.14898",
    "kind": "arxiv",
    "version": 2
  }
}