Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T10:57:32.713146Z
Paper Citation Record · LEDGER
Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations
As of 8 August 2026, this Paper Citation Record lists 2 of 2 outbound references and 1 inbound Pith citation observation for arXiv:2506.03842.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-25T19:34:17.100135Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
2 of 2 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
Observation c1ce6bbd-c31c-4c12-b827-654b8735131c · outbound
This paper cites Denoising Diffusion Probabilistic Models.
Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations Denoising Diffusion Probabilistic Models
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:57:32.708727Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 730e0aac-a9f2-4cb7-abe0-e583e89971d3 · outbound
This paper cites J., 2007: Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models.
Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations J., 2007: Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models
Reference 2015
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:57:32.713146Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 70c1db4a-41d1-4f5a-b35f-2df99b418494 · inbound
Event-Aware Loss Design for Forecasting of Convective Precipitation and Lightning cites this paper.
Event-Aware Loss Design for Forecasting of Convective Precipitation and Lightning Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-06-25T19:38:18.584494Z
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.