Pith Number

pith:YAV6A3HE

pith:2026:YAV6A3HEADRDP6WLZ2MPGYVBVT

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A plug-and-play generative framework for multi-satellite precipitation estimation

Haofei Sun, Hao Li, Jun Li, Wei Han, Wei Huang, Xiaoze Xu, Xingtao Song, Xiuyu Sun, Yunfan Yang, Zhiqiu Gao

PRISMA learns an unconditional precipitation prior from merged satellite fields and constrains it with independently trained sensor branches to fuse infrared and microwave data without full retraining.

arxiv:2605.14426 v1 · 2026-05-14 · physics.ao-ph · cs.AI

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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

Applied to FY-4B AGRI infrared and GPM GMI microwave observations, PRISMA improves Critical Success Index by up to 40.3% and reduces root-mean-square error by 22.6% relative to infrared-only estimation within microwave swaths, while also improving probabilistic skill.

C2weakest assumption

That an unconditional precipitation prior learned from IMERG Final fields can be effectively constrained by independently trained sensor-specific conditional branches without loss of accuracy or the need for joint retraining when adding new observation sources.

C3one line summary

PRISMA introduces a plug-and-play latent generative model that improves multi-sensor precipitation estimates by learning an unconditional prior from IMERG data and constraining it with independent sensor-specific branches.

References

47 extracted · 47 resolved · 4 Pith anchors

[1] Technical Report WMO- No 1970

[2] Contribution of Work- ing Group I to the Sixth Assessment Report 2021

[3] Dai, T.-Y., Ushijima-Mwesigwa, H.: PrecipDiff: Leveraging image diffusion models to enhance satellite-based precipitation observations (2025) 2025

[4] Brempong, E.A., Hassen, M.A., MohamedKhair, M., Dube, V., Hincapie Potes, S., Graham, O., Brik, A., McGovern, A., Huffman, G.J., Hickey, J.: Oya: Deep learning for accurate global precipitation estima 2025

[5] Technical Report WMO-No 2021

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:07.189431Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c02be06ce400e237facbce98f362a1acee0dbc4574fbbffb556df4a0bf2fb4d1

Aliases

arxiv: 2605.14426 · arxiv_version: 2605.14426v1 · doi: 10.48550/arxiv.2605.14426 · pith_short_12: YAV6A3HEADRD · pith_short_16: YAV6A3HEADRDP6WL · pith_short_8: YAV6A3HE

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/YAV6A3HEADRDP6WLZ2MPGYVBVT \
  | 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: c02be06ce400e237facbce98f362a1acee0dbc4574fbbffb556df4a0bf2fb4d1

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "30b3ac86538b904222c0f5b9bf75857fdde18efe0baa792730202a23a4c453ac",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "physics.ao-ph",
    "submitted_at": "2026-05-14T06:18:53Z",
    "title_canon_sha256": "45c1e86638ee9458c2779628f2ac08389dcb221eaebb0793c1cbd3b0200651c5"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14426",
    "kind": "arxiv",
    "version": 1
  }
}