Paper Citation Record · LEDGER

Numerical models outperform AI weather forecasts of record-breaking extremes

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2508.15724.

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

pith.paper-citation-record.v1
2508.15724 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:48:44.652322Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T16:31:09.430715Z

Reference resolution

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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 e9277576-86c9-4856-8410-e873a3c789ad · inbound

Understanding and Utilizing Dynamic Coupling in Free-Floating Space Manipulators for On-Orbit Servicing cites this paper.

Understanding and Utilizing Dynamic Coupling in Free-Floating Space Manipulators for On-Orbit Servicing Numerical models outperform AI weather forecasts of record-breaking extremes

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T17:48:44.652322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ace0cb1-8e0c-4d56-b4ff-dda1a35a2bbd · inbound

Evaluating Extreme Precipitation Forecasts: A Threshold-Weighted, Spatial Verification Approach for Comparing an AI Weather Prediction Model Against a High-Resolution NWP Model cites this paper.

Evaluating Extreme Precipitation Forecasts: A Threshold-Weighted, Spatial Verification Approach for Comparing an AI Weather Prediction Model Against a High-Resolution NWP Model Numerical models outperform AI weather forecasts of record-breaking extremes

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-04T07:43:53.014744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3c1f147-af4c-4802-a226-f7a1aecc8359 · inbound

AI-boosted rare event sampling to characterize extreme weather cites this paper.

AI-boosted rare event sampling to characterize extreme weather Numerical models outperform AI weather forecasts of record-breaking extremes

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T07:08:22.153838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b947049-a53f-4d62-ba5d-4e985d2b8a8d · inbound

Assessing Extrapolation of Peaks Over Thresholds with Martingale Testing cites this paper.

Assessing Extrapolation of Peaks Over Thresholds with Martingale Testing Numerical models outperform AI weather forecasts of record-breaking extremes

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T19:03:49.182318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c4fe403-580e-47ed-a299-7f67b272d658 · inbound

Extrapolation in Statistical Learning with Extreme Value Theory cites this paper.

Extrapolation in Statistical Learning with Extreme Value Theory Numerical models outperform AI weather forecasts of record-breaking extremes

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:31:09.436349Z

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 fae284e1-e78d-4f00-837f-b88642f5d27c · inbound

Integrating GNSS-Derived Zenith Wet Delay into a Weather Foundation Model Improves Precipitation Forecasting cites this paper.

Integrating GNSS-Derived Zenith Wet Delay into a Weather Foundation Model Improves Precipitation Forecasting Numerical models outperform AI weather forecasts of record-breaking extremes

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-11T04:13:01.235493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.