Pith Number

pith:FSM6DRXM

pith:2026:FSM6DRXMSAXOYPL432CJNTUZCG

not attested not anchored not stored refs resolved

Guided Diffusion Sampling for Precipitation Forecast Interventions

Ayumu Ueyama, Hiroshi Kera, Kazuhiko Kawamoto

Gradient-guided diffusion sampling steers weather model trajectories to reduce extreme precipitation forecasts while preserving physical consistency.

arxiv:2605.14317 v1 · 2026-05-14 · cs.LG · physics.ao-ph

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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

Experiments on extreme precipitation events from WeatherBench2 demonstrate that our method achieves effective precipitation reduction while yielding more physically plausible interventions than adversarial perturbations.

C2weakest assumption

That steering the diffusion sampling trajectory via gradients maintains consistency with the learned atmospheric distribution and produces interventions that remain physically plausible under the three evaluation perspectives.

C3one line summary

Gradient-guided diffusion sampling reduces extreme precipitation forecasts in data-driven weather models while producing more physically plausible changes than adversarial perturbations.

References

42 extracted · 42 resolved · 2 Pith anchors

[1] Historical review of research activities toward typhoons/hurricanes modification in japan and the united states.Journal of the Meteorological Society of Japan 2025

[2] Moshe Alamaro, Juergen Michele, and Vladimir Pudov. A preliminary assessment of inducing anthro- pogenic tropical cyclones using compressible free jets and the potential for hurricane mitigation.The J 2006

[3] Forecasting fails: Unveiling evasion attacks in weather prediction models.arXiv preprint arXiv:2512.08832, 2025 2025

[4] R. N. Bannister. A review of operational methods of variational and ensemble-variational data assimilation. Quarterly Journal of the Royal Meteorological Society, 143(703):607–633, 2017 2017

[5] The quiet revolution of numerical weather prediction 2015

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:09.885188Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2c99e1c6ec902eec3d7cde8496ce9911b5252cf2a23e186323786f6542468d07

Aliases

arxiv: 2605.14317 · arxiv_version: 2605.14317v1 · doi: 10.48550/arxiv.2605.14317 · pith_short_12: FSM6DRXMSAXO · pith_short_16: FSM6DRXMSAXOYPL4 · pith_short_8: FSM6DRXM

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/FSM6DRXMSAXOYPL432CJNTUZCG \
  | 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: 2c99e1c6ec902eec3d7cde8496ce9911b5252cf2a23e186323786f6542468d07

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "035c646a70cc5c55539f134c244fc95e1f1f838be561024f7321c038fae5da57",
    "cross_cats_sorted": [
      "physics.ao-ph"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T03:27:54Z",
    "title_canon_sha256": "336d4d37ae7b8481d5675cd68be33a33e0c4f39e53b30e4e6e4f6190eec22c5f"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14317",
    "kind": "arxiv",
    "version": 1
  }
}