Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Precipitation nowcasting with generative diffusion models
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2308.06733.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T22:05:33.014331Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
13
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 21403c64-5f83-413b-98e3-36d07ffde7a7 · inbound
Skillful High-Resolution Ensemble Precipitation Forecasting with an Integrated Deep Learning Framework cites this paper.
Skillful High-Resolution Ensemble Precipitation Forecasting with an Integrated Deep Learning Framework Precipitation nowcasting with generative diffusion models
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:05:33.014331Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0ed53b33-8254-4f38-ad16-5bb0077a84de · inbound
Deep Learning and Foundation Models for Weather Prediction: A Survey cites this paper.
Deep Learning and Foundation Models for Weather Prediction: A Survey Precipitation nowcasting with generative diffusion models
Reference 2017
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T20:52:51.361533Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 32a80ec3-6d64-4403-864e-f70a8ce547a2 · inbound
Using Generative Models to Produce Realistic Populations of UK Windstorms cites this paper.
Using Generative Models to Produce Realistic Populations of UK Windstorms Precipitation nowcasting with generative diffusion models
Reference 2014
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T13:47:50.102269Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a5b48892-88b4-4335-9db7-4e7adc1101c2 · 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 Precipitation nowcasting with generative diffusion models
Reference 141
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T08:03:08.898482Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 9620bda2-f0aa-4e93-96f0-2931f1d9c4f9 · inbound
MotifGen: Spatiotemporal interpolation of misaligned satellite images via multi-source generative modeling, in an application to tropical cyclones cites this paper.
MotifGen: Spatiotemporal interpolation of misaligned satellite images via multi-source generative modeling, in an application to tropical cyclones Precipitation nowcasting with generative diffusion models
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-06-26T00:18:42.065360Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.