Paper Citation Record · LEDGER

Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2304.12891.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2304.12891 v1

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measured 0 of 0 reference resolution

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measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:01:06.415491Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-01T09:45:39.879379Z

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5fa8b357-2d5b-4a10-a53b-b272ebee3978 · inbound

LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting cites this paper.

LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T05:01:06.415491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93d82fd2-6225-49c8-9366-f24e852e741b · inbound

Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence cites this paper.

Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T21:12:36.444939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d10db356-aeea-4d05-845d-e6dd5c251b8a · inbound

PODiff: Latent Diffusion in Proper Orthogonal Decomposition Space for Scientific Super-Resolution cites this paper.

PODiff: Latent Diffusion in Proper Orthogonal Decomposition Space for Scientific Super-Resolution Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:21:20.467375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fcdfa1d7-e3d1-4630-b53c-049371d020b4 · inbound

PixelFlowCast: Latent-Free Precipitation Nowcasting via Pixel Mean Flows cites this paper.

PixelFlowCast: Latent-Free Precipitation Nowcasting via Pixel Mean Flows Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

Reference 37

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T07:26:30.827527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0430118d-a3dc-431a-9b99-33b02d62b410 · inbound

Generative climate downscaling enables high-resolution compound risk assessment by preserving multivariate dependencies cites this paper.

Generative climate downscaling enables high-resolution compound risk assessment by preserving multivariate dependencies Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:27:07.078373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 161722e2-7503-4003-8edf-76fe22f69d6b · inbound

VMU-Diff: A Coarse-to-fine Multi-source Data Fusion Framework for Precipitation Nowcasting cites this paper.

VMU-Diff: A Coarse-to-fine Multi-source Data Fusion Framework for Precipitation Nowcasting Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:09:44.645902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ed85b392-8bef-4721-a508-c41091c6bf30 · inbound

SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland cites this paper.

SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-19T17:47:41.692646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7dd84a77-8575-43a4-b404-0cdda5e19a89 · inbound

Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problems cites this paper.

Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problems Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

Reference 103

Resolution: verified exact; arxiv_id, observed 2026-05-20T08:03:08.889920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 100d959e-b9ec-4980-9553-d48c51084e3e · inbound

Beyond MSE: Improving Precipitation Nowcasting with Multi-Quantile Regression cites this paper.

Beyond MSE: Improving Precipitation Nowcasting with Multi-Quantile Regression Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:53:16.291053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a31c2aa0-1367-42a9-b15e-157635c81a53 · inbound

Probabilistic Precipitation Nowcasting with Rectified Flow Transformers cites this paper.

Probabilistic Precipitation Nowcasting with Rectified Flow Transformers Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-06-29T00:12:50.381257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3c2730ce-5eb6-417c-b87c-4608cd97dada · inbound

Patch-PODiff-ViT: Structured Latent Diffusion with Patchwise POD for Super-Resolution and Uncertainty Quantification cites this paper.

Patch-PODiff-ViT: Structured Latent Diffusion with Patchwise POD for Super-Resolution and Uncertainty Quantification Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T09:45:39.881036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.