Pith Number

pith:AM3Q4NFC

pith:2026:AM3Q4NFCJTHNUHCDOSXZ5LXHJB

not attested not anchored not stored refs pending

Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction

Alex Lu, Genevieve Flaspohler, Haiyu Dong, Hannah Guan, Jakob Schloer, Jeremy Berman, Jonathan A. Weyn, Joshua Talib, Judah Cohen, Lester Mackey, Paulo Orenstein, Soukayna Mouatadid, Zekun Ni

Probabilistic bias correction doubles the subseasonal skill of AI weather forecasts and improves dynamical models for over 90 percent of key targets, winning an international competition.

arxiv:2604.16238 v2 · 2026-04-17 · cs.LG · physics.ao-ph · stat.ML

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\usepackage{pith}
\pithnumber{AM3Q4NFCJTHNUHCDOSXZ5LXHJB}

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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

PBC doubles the subseasonal skill of the AI Forecasting System and improves the skill of the operationally-debiased dynamical model for 91% of pressure, 92% of temperature, and 98% of precipitation targets. In ECMWF's 2025 real-time forecasting competition, its global forecasts placed first for all weather variables and lead times.

C2weakest assumption

That corrections learned from historical probabilistic forecasts will generalize to future unseen forecasts without overfitting or creating new biases at subseasonal lead times.

C3one line summary

Probabilistic bias correction doubles AI subseasonal forecast skill and wins a 2025 international competition by correcting biases in ECMWF models for pressure, temperature, and precipitation.

Cited by

1 paper in Pith

Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction

Receipt and verification

First computed	2026-07-13T01:20:08.379558Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

03370e34a24cceda1c4374af9eaee7484c051016661caa9a05f7cfb0f7c86824

Aliases

arxiv: 2604.16238 · arxiv_version: 2604.16238v2 · doi: 10.48550/arxiv.2604.16238 · pith_short_12: AM3Q4NFCJTHN · pith_short_16: AM3Q4NFCJTHNUHCD · pith_short_8: AM3Q4NFC

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/AM3Q4NFCJTHNUHCDOSXZ5LXHJB \
  | 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: 03370e34a24cceda1c4374af9eaee7484c051016661caa9a05f7cfb0f7c86824

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "bfd64ae730373de9ce0ff248ef05f37ebba4b35cfa64bd22f6083f87bab306ea",
    "cross_cats_sorted": [
      "physics.ao-ph",
      "stat.ML"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-04-17T16:58:06Z",
    "title_canon_sha256": "bc2e2f8ed910dc823254bde7e59596f7dab2a7728f1c9b569576cb91074d6beb"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.16238",
    "kind": "arxiv",
    "version": 2
  }
}