Paper Citation Record · LEDGER

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields

As of 22 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2505.12732.

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

pith.paper-citation-record.v1
2505.12732 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:32:41.789551Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T03:57:58.024137Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T06:46:52.478524Z

Reference resolution

20 of 20 outbound references displayed

verified exact0
verified fuzzy16
unresolved4
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7d44d874-482e-4ad0-ae95-4ba1720e69aa · outbound

This paper cites IEEE Transactions on sustainable energy7(4), 1525–1537 (2016).

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields IEEE Transactions on sustainable energy7(4), 1525–1537 (2016)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:42.187025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 02b28b13-67f8-44f6-9a1d-c84a4ccc4c30 · outbound

This paper cites Wind Energy Science7(6), 2231–2254 (2022).

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields Wind Energy Science7(6), 2231–2254 (2022)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:42.170871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1e41808b-e53f-4774-a0c8-aaa62dcb425f · outbound

This paper cites Science382(6677), 1416–1421 (2023).

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields Science382(6677), 1416–1421 (2023)

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:41.704056Z

Source-reported events for the cited work

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Observation 4c97cb2a-ee17-4a1c-99b0-d2ed18f1e71d · outbound

This paper cites FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators.

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:41.708889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a04dfe8d-5d83-4018-a11c-2485efb6f31a · outbound

This paper cites Nature619(7970), 533–538 (2023).

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields Nature619(7970), 533–538 (2023)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:42.142569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e90fd774-e790-49cd-b3ab-87d89cc605f2 · outbound

This paper cites npj climate and atmospheric science6(1), 190 (2023).

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields npj climate and atmospheric science6(1), 190 (2023)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:42.122455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6716c843-02a1-470e-81c6-494eb02f63b9 · outbound

This paper cites Nature Communications15(1), 6425 (2024).

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields Nature Communications15(1), 6425 (2024)

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:42.103100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f420ef18-d374-499c-b6be-4d044a26329a · outbound

This paper cites Nature communications13(1), 1–10 (2022).

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields Nature communications13(1), 1–10 (2022)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:42.085083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation eea3f373-1a50-47c2-9bca-6e23da867844 · outbound

This paper cites GenCast: Diffusion-based ensemble forecasting for medium-range weather.

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields GenCast: Diffusion-based ensemble forecasting for medium-range weather

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:41.734570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 965e9b24-2c79-45b3-89c0-a745c4065de7 · outbound

This paper cites Quarterly journal of the royal meteorological society146(730), 1999–2049 (2020).

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields Quarterly journal of the royal meteorological society146(730), 1999–2049 (2020)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:42.068204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 94ae68d9-6e8d-48d2-9977-ef17ce3a9042 · outbound

This paper cites Renewable Energy 101, 1–9 (2017).

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields Renewable Energy 101, 1–9 (2017)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:42.049472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dfea4183-cb49-4c0f-865a-c4d68266209f · outbound

This paper cites Geography Compass7(4), 249–265 (2013).

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields Geography Compass7(4), 249–265 (2013)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:42.024742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 09698823-8819-4058-9e87-548f9d3bffa9 · outbound

This paper cites Journal of Marine Science and Engineering9(3), 318 (2021).

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields Journal of Marine Science and Engineering9(3), 318 (2021)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:42.005379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f2922fcc-b718-48aa-829e-769f2a12370e · outbound

This paper cites Climate dynamics39, 2497–2522 (2012).

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields Climate dynamics39, 2497–2522 (2012)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:41.985137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0ace7e11-5bef-49dc-85ec-c77116cc2917 · outbound

This paper cites Journal of Applied Meteorology and Climatology57(3), 733–753 (2018).

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields Journal of Applied Meteorology and Climatology57(3), 733–753 (2018)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:41.966303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1b3b703a-a29c-4699-90a2-97981497eecf · outbound

This paper cites In: 2016 International Conference on High Performance Computing & Simulation (HPCS), pp.

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields In: 2016 International Conference on High Performance Computing & Simulation (HPCS), pp

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:41.949495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8cd5216c-5c18-4bb0-a1b1-88e4c8841274 · outbound

This paper cites Boundary-layer meteorology174(1), 1–59 (2020).

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields Boundary-layer meteorology174(1), 1–59 (2020)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:41.933460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 79bc5a5e-fb23-44a5-b299-e365f93a8a26 · outbound

This paper cites Geological Survey: Shuttle Radar Topography Mis- sion (SRTM) 1 Arc-Second Global.

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields Geological Survey: Shuttle Radar Topography Mis- sion (SRTM) 1 Arc-Second Global

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:41.914373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5c83ea8b-9548-4a4a-8aff-affd2d60cdfc · outbound

This paper cites Zenodo (2021).

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields Zenodo (2021)

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:41.784219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36915295-4b31-44fd-b13f-09a4fbbfdbf7 · outbound

This paper cites Boundary-Layer Meteorology109, 227–254 (2003) 14.

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields Boundary-Layer Meteorology109, 227–254 (2003) 14

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:41.894768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 8de573a5-7fd4-4b71-9bb0-4a10957f024e · inbound

WindINR: Latent-State INR for Fast Local Wind Query and Correction in Complex Terrain cites this paper.

WindINR: Latent-State INR for Fast Local Wind Query and Correction in Complex Terrain Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:46:52.480867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.