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pith:CXG4MYYI

pith:2026:CXG4MYYIBTDL2RWBNYKGULHNTD
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MambaRain: Multi-Scale Mamba-Attention Framework for 0-3 Hour Precipitation Nowcasting

Boyu Liu, Chunlei Shi, Cui Wu, Dan Niu, Hao Li, Hongbin Wang, Ni Fan, Xiang Xu, Xue Han, Yanlan Yang, Yongchao Feng, Yufeng Zhu, Zengliang Zang

MambaRain combines Mamba blocks with self-attention to extend accurate precipitation nowcasting to three hours.

arxiv:2605.14606 v1 · 2026-05-14 · cs.CV

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4 Citations open
5 Replications open

Claims

C1strongest claim

MambaRain substantially outperforms existing deterministic methodologies in 0-3 hour nowcasting tasks, with particularly pronounced performance gains in the challenging 2-3 hour prediction range.

C2weakest assumption

That the complementary combination of Mamba blocks for temporal dynamics and self-attention for spatial correlations will capture the chaotic, multi-scale nature of precipitation fields without introducing new artifacts or requiring extensive post-hoc tuning that undermines the claimed gains.

C3one line summary

MambaRain integrates Mamba's efficient long-sequence modeling with attention mechanisms and a spectral loss to extend accurate deterministic precipitation nowcasting from 0-2 hours to 0-3 hours.

References

38 extracted · 38 resolved · 1 Pith anchors

[1] Wavec2r: Wavelet-driven coarse-to-refined hierarchical learning for radar retrieval, 2026
[2] Alphapre: Amplitude-phase disentanglement model for precipitation nowcasting, 2025
[3] Lmcast: A pretrained language model guided long-term memory transformer for precipitation nowcasting, 2025
[4] Pimmnet: In- troducing multi-modal precipitation nowcasting via a physics-informed perspective, 2025
[5] End-to-end data-driven weather prediction, 2025

Formal links

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Receipt and verification
First computed2026-05-17T23:39:04.212102Z
Builderpith-number-builder-2026-05-17-v1
SignaturePith Ed25519 (pith-v1-2026-05) · public key
Schemapith-number/v1.0

Canonical hash

15cdc663080cc6bd46c16e146a2ced98f2926209a76457f84fda362850a5fd18

Aliases

arxiv: 2605.14606 · arxiv_version: 2605.14606v1 · doi: 10.48550/arxiv.2605.14606 · pith_short_12: CXG4MYYIBTDL · pith_short_16: CXG4MYYIBTDL2RWB · pith_short_8: CXG4MYYI
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/CXG4MYYIBTDL2RWBNYKGULHNTD \
  | 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())"
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Canonical record JSON
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T09:23:12Z",
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